Disruptive IT Impacts Companies, Competition, and Careers
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Case 1-1 Opening Case iii
Information Technology for Management Digital Strategies for Insight, Action, and Sustainable Performance
10th Edition
EFRAIM TURBAN
LINDA VOLONINO, Canisius College
GREGORY R. WOOD, Canisius College
Contributing authors:
JANICE C. SIPIOR, Villanova University GUY H. GESSNER, Canisius College
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This book was set by Aptara, Inc. Cover and text printed and bound by Courier Kendallville.
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ISBN 978-1-118-89778-2
BRV ISBN 978-1-118-99429-0
Printed in the United States of America
10 9 8 7 6 5 4 3 2 1
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BRIEF CONTENTS
1 Doing Business in Digital Times 1
2 Data Governance and IT Architecture Support Long-Term Performance 33
3 Data Management, Big Data Analytics, and Records Management 70
4 Networks for Efficient Operations and Sustainability 110
5 Cybersecurity and Risk Management 141
6 Attracting Buyers with Search, Semantic, and Recommendation Technology 181
7 Social Networking, Engagement, and Social Metrics 221
8 Retail, E-commerce, and Mobile Commerce Technology 264
9 Effective and Efficient Business Functions 297
10 Strategic Technology and Enterprise Systems 331
11 Data Visualization and Geographic Information Systems 367
12 IT Strategy and Balanced Scorecard 389
13 Project Management and SDLC 412
14 Ethical Risks and Responsibilities of IT Innovations 438
Glossary G-1
Organizational Index O-1
Name Index N-1
Subject Index S-1
Part 1
Part 2
Part 3
Part 4
Digital Technology Trends Transforming How Business Is Done
Winning, Engaging, and Retaining Consumers with Technology
Optimizing Performance with Enterprise Systems and Analytics
Managing Business Relationships, Projects, and Codes of Ethics
v
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CONTENTS
Part 1 Digital Technology Trends Transforming How Business Is Done
1 Doing Business in Digital Times 1 Case 1.1, Opening Case: McCain Foods’s Success Factors: Dashboards, Innovation, and Ethics 2 1.1 Every Business Is a Digital Business 6
1.2 Business Process Management and Improvement 15
1.3 The Power of Competitive Advantage 19
1.4 Enterprise Technology Trends 25
1.5 How Your IT Expertise Adds Value to Your Performance and Career 27 Case 1.2, Business Case: Restaurant Creates Opportunities to Engage Customers 31 Case 1.3, Video Case: What Is the Value of Knowing More and Doing More? 32
2 Data Governance and IT Architecture Support Long-Term Performance 33 Case 2.1, Opening Case: Detoxing Dirty Data with Data Governance at Intel Security 34 2.1 Information Management 37
2.2 Enterprise Architecture and Data Governance 42
2.3 Information Systems: The Basics 47
2.4 Data Centers, Cloud Computing, and Virtualization 53
2.5 Cloud Services Add Agility 62 Case 2.2, Business Case: Data Chaos Creates Risk 67 Case 2.3, Video Case: Cloud Computing: Three Case Studies 69
3 Data Management, Big Data Analytics, and Records Management 70 Case 3.1, Opening Case: Coca-Cola Manages at the Point That Makes a Difference 71 3.1 Database Management Systems 75 3.2 Data Warehouse and Big Data Analytics 86 3.3 Data and Text Mining 96 3.4 Business Intelligence 99 3.5 Electronic Records Management 102 Case 3.2, Business Case: Financial Intelligence Fights Fraud 108 Case 3.3, Video Case: Hertz Finds Gold in Integrated Data 108
4 Networks for Efficient Operations and Sustainability 110 Case 4.1, Opening Case: Sony Builds an IPv6 Network to Fortify Competitive Edge 111
4.1 Data Networks, IP Addresses, and APIs 113 4.2 Wireless Networks and Mobile Infrastructure 123
4.3 Collaboration and Communication Technologies 127 4.4 Sustainability and Ethical Issues 130 Case 4.2, Business Case: Google Maps API for Business 139 Case 4.3, Video Case: Fresh Direct Connects for Success 140
5 Cybersecurity and Risk Management 141 Case 5.1, Opening Case: BlackPOS Malware Steals Target’s Customer Data 142 5.1 The Face and Future of Cyberthreats 144
5.2 Cyber Risk Management 152
5.3 Mobile, App, and Cloud Security 163
5.4 Defending Against Fraud 166
5.5 Compliance and Internal Control 169 Case 5.2, Business Case: Lax Security at LinkedIn Exposed 177 Case 5.3, Video Case: Botnets, Malware Security, and Capturing Cybercriminals 179
vii
Part 2 Winning, Engaging, and Retaining Consumers with Technology
6 Attracting Buyers with Search, Semantic, and Recommendation Technology 181 Case 6.1, Opening Case: Nike Golf Drives Web Traffic with Search Engine Optimization 182 6.1 Using Search Technology for Business Success 186 6.2 Organic Search and Search Engine Optimization 198 6.3 Pay-Per-Click and Paid Search Strategies 203 6.4 A Search for Meaning—Semantic Technology 205 6.5 Recommendation Engines 209 Case 6.2, Business Case: Recommending Wine to Online Customers 217 Case 6.3, Video Case: Power Searching with Google 218
7 Social Networking, Engagement, and Social Metrics 221 Case 7.1, Opening Case: The Connected Generation Influences Banking Strategy 222 7.1 Web 2.0—The Social Web 225 7.2 Social Networking Services and Communities 235 7.3 Engaging Consumers with Blogs and Microblogs 245 7.4 Mashups, Social Metrics, and Monitoring Tools 250 7.5 Knowledge Sharing in the Social Workplace 255 Case 7.2, Business Case: Social Customer Service 259 Case 7.3, Video Case: Viral Marketing: Will It Blend? 261
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8 Retail, E-commerce, and Mobile Commerce Technology 264 Case 8.1, Opening Case: Macy’s Races Ahead with Mobile Retail Strategies 265 8.1 Retailing Technology 268 8.2 Business to Consumer (B2C) E-commerce 271 8.3 Business to Business (B2B) E-commerce and E-procurement 277 8.4 Mobile Commerce 279 8.5 Mobile Transactions and Financial Services 286 Case 8.2, Business Case: Chegg’s Mobile Strategy 293 Case 8.3, Video Case: Searching with Pictures Using MVS 294
11.4 Geospatial Data and Geographic Information Systems 384 Case 11.2, Visualization Case: Are You Ready for Football? 387 Case 11.3, Video Case: The Beauty of Data Visualization 387
viii Contents
Part 3 Optimizing Performance with Enterprise Systems and Analytics
9 Effective and Efficient Business Functions 297 Case 9.1, Opening Case: Ducati Redesigns Its Operations 299 9.1 Solving Business Challenges at All Management Levels 302 9.2 Manufacturing, Production, and Transportation Management Systems 306 9.3 Sales and Marketing Systems 312 9.4 Accounting, Finance, and Regulatory Systems 315 9.5 Human Resources Systems, Compliance, and Ethics 323 Case 9.2, Business Case: HSBC Combats Fraud in Split-second Decisions 329 Case 9.3, Video Case: United Rentals Optimizes Its Workforce with Human Capital Management 330
10 Strategic Technology and Enterprise Systems 331 Case 10.1, Opening Case: Strategic Technology Trend— 3D Printing 332 10.1 Enterprise Systems 337 10.2 Enterprise Social Platforms 341 10.3 Enterprise Resource Planning Systems 346 10.4 Supply Chain Management Systems 352 10.5 Customer Relationship Management Systems 358 Case 10.2, Business Case: Avon’s Failed SAP Implementation: Enterprise System Gone Wrong 364 Case 10.3, Video Case: Procter & Gamble: Creating Conversations in the Cloud with 4.8 Billion Consumers 365
11 Data Visualization and Geographic Information Systems 367 Case 11.1, Opening Case: Safeway and PepsiCo Apply Data Visualization to Supply Chain 369 11.1 Data Visualization and Learning 371 11.2 Enterprise Data Mashups 377 11.3 Digital Dashboards 380
Part 4 Managing Business Relationships, Projects, and Codes of Ethics
12 IT Strategy and Balanced Scorecard 389 Case 12.1, Opening Case: Intel’s IT Strategic Planning Process 390 12.1 IT Strategy and the Strategic Planning Process 392 12.2 Aligning IT with Business Strategy 397 12.3 Balanced Scorecard 400 12.4 IT Sourcing and Cloud Strategy 403 Case 12.2, Business Case: AstraZeneca Terminates $1.4B Outsourcing Contract with IBM 409 Case 12.3, Data Analysis: Third-Party versus Company-Owned Offshoring 410
13 Project Management and SDLC 412 Case 13.1, Opening Case: Keeping Your Project on Track, Knowing When It Is Doomed, and DIA Baggage System Failure 413 13.1 Project Management Concepts 417
13.2 Project Planning, Execution, and Budget 421
13.3 Project Monitoring, Control, and Closing 428
13.4 System Development Life Cycle 432 Case 13.2, Business Case: Steve Jobs’ Shared Vision Project Management Style 436 Case 13.3, Demo Case: Mavenlink Project Management and Planning Software 437
14 Ethical Risks and Responsibilities of IT Innovations 438 Case 14.1, Opening Case: Google Glass and Risk, Privacy, and Piracy Challenges 439 14.1 Privacy Paradox, Privacy, and Civil Rights 442 14.2 Responsible Conduct 448 14.3 Technology Addictions and the Emerging Trend of Focus Management 453 14.4 Six Technology Trends Transforming Business 454 Case 14.2, Business Case: Apple’s CarPlay Gets Intelligent 458 Case 14.3, Video Case: Vehicle-to-Vehicle Technology to Prevent Collisions 459
Glossary G-1
Organizational Index O-1
Name Index N-1
Subject Index S-1
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Business strategy and operations are driven by data, digi-
tal technologies, and devices. Five years from now, we will
look back upon today as the start of a new era in business
and technology. Just like the way e-business started with
the emergence of the Web, this new era is created by the
convergence of social, mobile, big data, analytics, cloud,
sensor, software-as-a-service, and data visualization tech-
nologies. These technologies enable real-time insights,
business decisions, and actions. Examples of how they
determine tomorrow’s business outcomes are:
• Insight. Combining the latest capabilities in big data analytics, reporting, collaboration, search, and
machine-to-machine (M2M) communication helps
enterprises build an agility advantage, cut costs, and
achieve their visions.
• Action. Fully leveraging real-time data about opera- tions, supply chains, and customers enables managers
to make decisions and take action in the moment.
• Sustainable performance. Deploying cloud services, managing projects and sourcing agreements, respect-
ing privacy and the planet, and engaging customers
across channels are now fundamental to sustaining
business growth.
• Business optimization. Embedding digital capability into products, services, machines, and business pro-
cesses optimizes business performance—and creates
strategic weapons.
In this tenth edition, students learn, explore, and analyze
the three dimensions of business performance improve-
ment: digital technology, business processes, and people.
What Is New in the Tenth Edition—and Why It Matters Most Relevant Content. Prior to and during the writing process, we attended practitioner conferences and con-
sulted with managers who are hands-on users of leading
technologies, vendors, and IT professionals to learn about
their IT/business successes, challenges, experiences, and
recommendations. For example, during an in-person
interview with a Las Vegas pit boss, we learned how
real-time monitoring and data analytics recommend
the minimum bets in order to maximize revenue per
minute at gaming tables. Experts outlined opportunities
and strategies to leverage cloud services and big data
PREFACE
to capture customer loyalty and wallet share and justify
significant investments in leading IT.
More Project Management with Templates. In response to reviewers’ requests, we have greatly increased cover-
age of project management and systems development
lifecycle (SDLC). Students are given templates for writing
a project business case, statement of work (SOW), and
work breakdown structure (WBS). Rarely covered, but
critical project management issues included in this edition
are project post-mortem, responsibility matrix, go/no go
decision factors, and the role of the user community.
New Technologies and Expanded Topics. New to this edition are 3D printing and bioprinting, project portfolio
management, the privacy paradox, IPv6, outsource rela-
tionship management (ORM), and balanced scorecard.
With more purchases and transactions starting online
and attention being a scarce resource, students learn how
search, semantic, and recommendation technologies func-
tion to improve revenue. The value of Internet of Things
(IoT) has grown significantly as a result of the compound
impact of connecting people, processes, data, and things.
Easier to Grasp Concepts. A lot of effort went into mak- ing learning easier and longer-lasting by outlining content
with models and text graphics for each opening case (our version of infographics) as shown in Figure P-1—from the
Chapter 12 opening case.
Engaging Students to Assure Learning The tenth edition of Information Technology for Management engages students with up-to-date cover- age of the most important IT trends today. Over the
years, this IT textbook had distinguished itself with an
emphasis on illustrating the use of cutting edge business
technologies for achieving managerial goals and objec-
tives. The tenth edition continues this tradition with more
hands-on activities and analyses.
Each chapter contains numerous case studies and
real world examples illustrating how businesses increase
productivity, improve efficiency, enhance communica-
tion and collaboration, and gain a competitive edge
through the use of ITs. Faculty will appreciate a variety
of options for reinforcing student learning, that include
three Case Studies per chapter, including an opening case, a business case and a video case.
ix
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x Preface
Throughout each chapter are various learning aids,
which include the following:
• Learning Outcomes are listed at the beginning of each chapter to help students focus their efforts and alert
them to the important concepts that will be discussed.
• The Chapter Snapshot provides students with an over- view of the chapter content.
• IT at Work boxes spotlight real-world cases and inno- vative uses of IT.
• Definitions of Key Terms appear in the margins throughout the book.
• Tech Note boxes explore topics such as “4G and 5G Networks in 2018” and “Data transfers to main-
frames.”
• Career Insight boxes highlight different jobs in the IT for management field.
At the end of each chapter are a variety of features
designed to assure student learning:
• Critical Thinking Questions are designed to facilitate student discussion.
• Online and Interactive Exercises encourage students to explore additional topics.
• Analyze and Decide questions help students apply IT concepts to business decisions.
Details of New and Enhanced Features of the Tenth Edition The textbook consists of fourteen chapters organized
into four parts. All chapters have new sections as well as
updated sections, as shown in Table P-1.
Strategic
directional
statements
Strategic
plan
gic
egic
2. Technology & Business Outlook. A team of
senior management, IT, and business unit
representatives develop the two-to-five-year
business outlook & technology outlook.
3. Current State Assessment & Gap Analysis.
Analysis of the current state of IT, enterprise
systems, & processes, which are compared with
results of step 2 to identify gaps and necessary
adjustments to IT investment plans.
4. Strategic Imperatives, Strategies, & Budget for
Next Year. Develop next year’s priorities, road
map, budget, & investment plan. Annual budget
approved.
5. Governance Decisions & IT Road Map. The
budget guides the governance process, including
supplier selection and sourcing.
6. Balanced Scorecard Reviews.
Performance is measured monthly.
1. Enterprise Vision. Senior management &
leaders develop & communicate the enterprise’s
two-to-five-year strategic vision & mission and
identify the direction & focus for upcoming year.
Figure P-1 Model of Intel’s 6-step IT strategic planning process, from Chapter 12.
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Preface xi
TABLE P-1 Overview of New and Expanded IT Topics and Innovative Enterprises Discussed in the Chapters
Chapter New and Expanded IT and Business Topics Enterprises in a Wide Range of Industries
1: Doing Business in
Digital Times
• Era of Mobile-Social-Cloud-Big Data • Digital connectivity and convergence
• Internet of Things (IoT), or machine-to-machine
(M2M) technology
• Farm-to-fork traceability
• Business process management
• Near-fi eld communication (NFC)
• McCain Foods Ltd
• Zipcar
• Pei Wei Asian Diner
• Teradata
2: Data Governance and
IT Architecture Support
Long-Term Performance
• Data governance and quality
• Master data management (MDM)
• Cloud services
• Collaboration
• Virtualization and business continuity
• software-, platform-, infrastructure-, and data-
as-a-service
• Intel Security
• Liberty Wines
• Unilever
• Vanderbilt University
Medical Center
3: Data Management,
Big Data Analytics and
Records Management
• Big data analytics and machine-generated data
• Business intelligence (BI)
• Hadoop
• NoSQL systems
• Active data warehouse apps
• Compliance
• Coca-Cola
• Hertz
• First Wind
• Argo Corp.
• Wal-Mart
• McDonalds
• Infi nity Insurance
• Quicken Loans, Inc.
• U.S. military
• CarMax
4: Networks for Effi cient
Operations and Sustain-
ability
• IPv6
• API
• 4G and 5G networks
• Net neutrality
• Location-aware technologies
• Climate change
• Mobile infrastructure
• Sustainable development
• Sony
• Google Maps
• Fresh Direct
• Apple
• Spotify
• Caterpillar, Inc.
5: Cyber Security and
Risk Management
• BYOD and social risks
• Advanced persistent threats (APT), malware,
and botnets
• IT governance
• Cloud security
• Fraud detection and prevention
• Target
• Boeing
6: Attracting Buyers with
Search, Semantic and
Recommendation
Technology
• Search technology
• Search engine optimization (SEO)
• Google Analytics
• Paid search strategies
• Nike
• Netfl ix
• Wine.com
(continued)
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xii Preface
TABLE P-1 Overview of New and Expanded IT Topics and Innovative Enterprises Discussed in the Chapters (continued )
Chapter New and Expanded IT and Business Topics Enterprises in a Wide Range of Industries
7: Social Networking,
Engagement and Social
Metrics
• Social network services (SNS)
• Web 2.0 tools for business collaboration
• Crowdfunding
• Privacy
• Citibank
• American Express
• Cisco
8: Retail, E-commerce
and Mobile Commerce
Technology
• Innovation in traditional and web-based retail
• Omni-channel retailing
• Visual search
• Mobile payment systems
• Macys
• Chegg
• Amazon
9: Effective and Effi cient
Business Functions
• Customer experience (CX)
• eXtensible Business Reporting Language
(XBRL)
• Order fulfi llment process
• Transportation management systems
• Computer-integrated manufacturing (CIM)
• SaaS
• TQM
• Auditing information systems
• Ducati Motor Holding
• HSBC
• SAS
• United Rentals
• First Choice Ski
10: Strategic Technology
and Enterprise Systems
• 3D printing, additive manufacturing
• Enterprise social platforms
• Yammer, SharePoint, and Microsoft Cloud
• Avon
• Procter & Gamble
• Organic Valley Family
of Farms
• Red Robin Gourmet
Burgers, Inc.
• Salesforce.com
• Food and Drug Administra-
tion (FDA)
• U.S. Army Materiel
Command (AMC)
• 1-800-Flowers
11: Data Visualization
and Geographic Informa-
tion Systems
• Data visualization
• Mobile dashboards
• Geospatial data and geocoding
• Geographic Information Systems (GIS)
• Supply chain visibility
• Reporting tools; analytical tools
• Self-service mashup capabilities
• Safeway
• PepsiCo
• eBay
• Tableau
• Hartford Hospital
• General Motors (GM)
12: IT Strategy and
Balanced Scorecard
• IT strategic planning process
• Value drivers
• Outsource relationship management (ORM)
• Service level agreements (SLAs)
• Outsourcing lifecycle
• Applications portfolio
• Intel
• AstraZeneca
• IBM
• Commonwealth Bank of
Australia (CBA)
(continued)
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Preface xiii
TABLE P-1 Overview of New and Expanded IT Topics and Innovative Enterprises Discussed in the Chapters (continued)
Chapter New and Expanded IT and Business Topics Enterprises in a Wide Range of Industries
13: Project Management
and SDLC
• Project management lifecycle
• Project Portfolio Management (PPM)
• Project business case
• Project business case, statement of work (SOW),
work breakdown structure (WBS), milestone
schedule, and Gantt chart
• Triple constraint
• Critical path
• Systems feasibility studies
• Denver International
Airport
• U.S. Census
• Mavenlink Project
Management and Planning
Software
14: Ethical Risks and
Responsibilities of IT
Innovations
• Privacy paradox
• Social recruitment and discrimination
• Responsible conduct
• Vehicle-to-vehicle (V2V) technology
• Ethics of 3D printing and bioprinting
• Tech addictions
• Tech trends
• Google Glass
• Apple’s CarPlay
• SnapChat
• Target
Supplementary Materials An extensive package of instructional materials is avail-
able to support this tenth edition. These materials are
accessible from the book companion Web site at www. wiley.com/college/turban.
• Instructor’s Manual. The Instructor’s Manual presents objectives from the text with additional information
to make them more appropriate and useful for the
instructor. The manual also includes practical applica-
tions of concepts, case study elaboration, answers to
end-of-chapter questions, questions for review, ques-
tions for discussion, and Internet exercises.
• Test Bank. The test bank contains over 1,000 ques- tions and problems (about 75 per chapter) consisting
of multiple-choice, short answer, fill-ins, and critical
thinking/essay questions.
• Respondus Test Bank. This electronic test bank is a powerful tool for creating and managing exams
that can be printed on paper or published directly
to Blackboard, ANGEL, Desire2Learn, Moodle, and
other learning systems. Exams can be created offline
using a familiar Windows environment, or moved from
one LMS to another.
• PowerPoint Presentation. A series of slides designed around the content of the text incorporates key points
from the text and illustrations where appropriate.
E-book Wiley E-Textbooks offer students the complete content of the printed textbook on the device of their preference—
computer, iPad, tablet, or smartphone—giving students
the freedom to read or study anytime, anywhere. Students
can search across content, take notes, and highlight key
materials. For more information, go to www.wiley.com/
college/turban.
Acknowledgments Many individuals participated in focus groups or review-
ers. Our sincere thanks to the following reviewers of the
tenth edition who provided valuable feedback, insights,
and suggestions that improved the quality of this text:
Joni Adkins, Northwest Missouri State University
Ahmad Al-Omari, Dakota State University
Rigoberto Chinchilla, Eastern Illinois University
Michael Donahue, Towson University
Samuel Elko, Seton Hill University
Robert Goble, Dallas Baptist University
Eileen Griffin, Canisius College
Binshan Lin, Louisiana State University in Shreveport
Thomas MacMullen, Eastern Illinois University
James Moore, Canisius College
Beverly S. Motich, Messiah College
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xiv Preface
Barin Nag, Towson University
Luis A. Otero, Inter-American University of Puerto
Rico, Metropolitan Campus
John Pearson, Southern Illinois University
Daniel Riding, Florida Institute of Technology
Josie Schneider, Columbia Southern University
Derek Sedlack, South University
Eric Weinstein, The University of La Verne
Patricia White, Columbia Southern University
Gene A. Wright, University of Wisconsin–Milwaukee
We are very thankful to our assistants, Samantha
Palisano and Olena Azarova. Samantha devoted many
hours of research, provided clerical support, and con-
tributed to the writing of Chapter 6. Olena assisted with
research and development of graphics for Chapter 7.
We are fortunate and thankful for the expert and encour-
aging leadership of Margaret Barrett, Beth Golub, Ellen
Keohane, and Mary O’Sullivan. To them we extend our
sincere thanks for your guidance, patience, humor, and
support during the development of this most recent ver-
sion of the book. Finally, we wish to thank our families
and colleagues for their encouragement, support, and
understanding as we dedicated time and effort to cre-
ating this new edition of Information Technology for Management.
Linda Volonino Greg Wood
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Chapter Snapshot
Make no mistake. Businesses are experiencing a digital transformation as digital technology enables changes unimaginable a decade ago. High-performance organi-
zations are taking advantage of what is newly possible
from innovations in mobile, social, cloud, big data, data analytics, and visualization technologies. These digital forces enable unprecedented levels of connectivity, or
connectedness, as listed in Figure 1.1.
Think how much of your day you have your phone
nearby—and how many times you check it. Nearly
80 percent of people carry their phone for all but two
hours of their day; and 25 per cent of 18- to 44-year-olds
cannot remember not having their phone with them (Cooper, 2013).
As a business leader, you will want to know what
steps to take to get a jump on the mobile, social, cloud,
Doing Business in Digital Times1
Chapter
1. Describe the use of digital technology in every facet of business and how digital channels are being leveraged.
2. Explain the types, sources, characteristics, and control of enterprise data, and what can be accomplished with near real time data.
3. Identify the five forces of competitive advantage and evaluate how they are reinforced by IT.
4. Describe enterprise technology trends and explain how they influence strategy and operations.
5. Assess how IT adds value to your career path and per- formance, and the positive outlook for IT management careers.
Learning Outcomes
1
Digital Technology Trends Transforming How Business Is DonePart 1
Chapter Snapshot Case 1.1 Opening Case: McCain Foods’ Success Factors—Dashboards, Innovation, and Ethics
1.1 Every Business Is a Digital Business 1.2 Business Process Management and
Improvement 1.3 The Power of Competitive Advantage 1.4 Enterprise Technology Trends 1.5 How Your IT Expertise Adds Value to Your
Performance and Career
Key Terms
Assuring Your Learning
• Discuss: Critical Thinking Questions • Explore: Online and Interactive Exercises • Analyze & Decide: Apply IT Concepts
to Business Decisions
Case 1.2 Business Case: Restaurant Creates Opportunities to Engage Customers
Case 1.3 Video Case: What Is the Value of Knowing More and Doing More?
References
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big data, analytics, and visualization technologies that will move your businesses
forward. Faced with opportunities and challenges, you need to know how to lever-
age them before or better than your competitors.
In this opening chapter, you read about the powerful impacts of digital technol-
ogy on management, business, government, entertainment, society, and those it will
have on the future. You learn of the latest digital trends taking place across indus-
tries and organizations—small and medium businesses, multinational corporations,
government agencies, the health-care industry, and nonprofits.
Big data are datasets whose size and speed are beyond the ability of typical database software tools to capture, store, manage, and analyze. Examples are machine- generated data and social media texts.
Data analytics refers to the use of software and statistics to find meaningful insight in the data, or better under- stand the data.
Data visualization (viz) tools make it easier to understand data at a glance by display- ing data in summarized formats, such as dashboards and maps, and by enabling drill-down to the detailed data.
Figure 1.1 We are in the era of mobile-social- cloud-big data that shape business strate- gies and day-to-day operations.
C A S E 1 . 1 O P E N I N G C A S E McCain Foods’ Success Factors: Dashboards, Innovation, and Ethics
COMPANY OVERVIEW You most likely have eaten McCain Foods products (Figure 1.2, Table 1.1). McCain is a market leader in the frozen food industry—producing one-third of the world’s
supply of french fries. The company manufactures, distributes, and sells more than
Figure 1.2 McCain Foods, Ltd. overview.
2
An estimated 15 billion
devices are connected to
the Internet—forecasted
to hit 50 billion by 2020
as more devices connect
via mobile networks.
Over 1 million websites
engage in Facebook e-commerce.
Over 200 million social
media users are mobile only, never accessing it from a desktop or laptop.
Mobile use generates 30%
of Facebook’s ad revenue.
More data are collected in
a day now than existed in
the world 10 years ago.
Half of all data are in the
cloud and generated
by mobile and social
activities—known as big
data.
Sales offices in 110 countries
55 production plants on
6 continents
22,000 employees
Global Reach
Good ethics is good
business.
Good food, better life.
Corporate Culture Dashboards
Data analytics
Real time reporting systems
Digital Technology
Frozen food manufacturer
Market leader in french
fries
Brand
McCain Foods, Ltd.
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CASE 1.1 Opening Case 3
TABLE 1.1 Opening Case Overview
Company McCain Foods, Ltd. www.mccain.com
Industry The global company manufactures, sells, and distributes frozen food products.
Product lines More than 100 oven-ready frozen food products
Digital technology Dashboards are implemented throughout the organization from boardrooms to factory fl oors. Dashboards have drill-down
capabilities.
Business challenges The frozen food industry faced tough challenges from health and nutrition trends that are emphasizing fresh foods. Industry
is highly competitive because it is expected to experience slow
growth through 2018.
Taglines “Good food. Better life.” and “It’s all good.”
Figure 1.3 Frozen food is one of the most dynamic and largest sectors of the food industry.
100 oven-ready frozen foods—pizzas, appetizers, meals, and vegetables. McCain is
a global business-to-business (B2B) manufacturer with 55 production facilities on 6 continents. The company sells frozen foods to other businesses—wholesalers, retail-
ers, and restaurants from sales offices in 110 countries. McCain supplies frozen fries
to Burger King and supermarket chains (Figure 1.3).
Business-to-business (B2B) commerce. The selling of products and services to other businesses.
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4 Chapter 1 Doing Business in Digital Times
Food manufacturers must be able to trace all ingredients along their supply chain in case of contamination. Achieving end-to-end traceability is complex given the number of players in food supply chains. Several communication and tracking
technologies make up McCain’s supply chain management (SCM) system to keep
workers informed of actual and potential problems with food quality, inventory,
and shipping as they occur. McCain’s SCM system ensures delivery of the best
products possible at the best value to customers. In addition, the company strives
to prevent food shortages worldwide by analyzing huge volumes of data to predict
crop yields.
Supply chain. All businesses involved in the production and distribution of a product or service.
FROZEN FOOD INDUSTRY CHALLENGES
McCain Foods had to deal with three major challenges and threats:
1. Drop in demand for frozen foods. McCain operated in an industry that was facing tougher competition. Health-conscious trends were shifting customer
demand toward fresh food, which was slowing growth in the frozen foods
market.
2. Perishable inventory. Of all the types of manufacturing, food manufacturers face unique inventory management challenges and regulatory requirements. Their
inventory of raw materials and fi nished goods can spoil, losing all their value, or
food can become contaminated. Regulators require food manufacturers to able
to do recalls quickly and effectively. Food recalls have destroyed brands and
been fi nancially devastating.
3. Technology-dependent. Food manufacturers face the pressures that are common to all manufacturers. They need information reporting systems and
digital devices to manage and automate operations, track inventory, keep
the right people informed, support decisions, and collaborate with business
partners.
McCain Foods worked with Burger King (BK) to develop lower-calorie fries
called Satisfries (Figure 1.4). These crinkle-cut fries have 30 percent less fat and 20 percent fewer calories than BK’s classic fries. This food innovation has shaken
up the fast-food industry and given BK an advantage with end-consumers who are
demanding healthier options.
Figure 1.4 McCain Foods and Burger King jointly developed Satisfries—a french fry innovation with 30 percent less fat and 20 percent fewer calories than BK’s current fries and 40 percent less fat and 30 percent fewer calories than McDonald’s fries.
MCCAIN FOODS’ BUSINESS AND IT STRATEGIES
The McCain brothers, who founded the company, follow this simple philosophy:
“Good ethics is good business.” McCain prides itself on the quality and conve-
nience of its products, which is reflected in the It’s All Good brand image. The It’s All Good branding effort was launched in 2010 after surveys found that customers were concerned about the quality and nutrition of frozen foods. Since then, many of
products have been improved and manufactured in healthier versions.
Managing with Digital Technology McCain had integrated its diverse sources of data into a single environment for analysis. Insights gained from its data analytics helped improve manufacturing processes, innovation, and competitive advantage.
McCain Foods invested in data analytics and visualization technologies to
maximize its capability to innovate and gain insights from its huge volumes of data.
The company tracks, aggregates, and analyzes data from operations and business
customers in order to identify opportunities for innovation in every area of the busi-
ness. The results of data analytics are made available across the organization—from
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CASE 1.1 Opening Case 5
Figure 1.5 Data visualizations of KPIs make them easy to understand at a glance.
executive boardrooms to the factory floors—on dashboards. Dashboards are data visualizations (data viz) that display the current status of key performance indica- tors (KPIs) in easy-to-understand formats (Figure 1.5). KPIs are business metrics used to evaluate performance in terms of critical success factors, or strategic and
operational goals.
Dashboards Create Productive Competition Among Factory Workers McCain implemented 22,000 reports and 3,000 personal reporting systems that
include dashboards. Dashboards display summarized data graphically in a clear and
concise way. By clicking a graph, the user can drill down to the detailed data. The
dashboards reach most of McCain’s 18,000 employees worldwide.
Dashboards have created healthy competition that has led to better perfor-
mance. Ten-foot dashboards hang on factory walls of plants around the world. They
are strategically placed near the cafeteria so employees can see the KPIs and per-
formance metrics of every plant. With this visibility, everyone can know in near real
time exactly how well they are doing compared to other plants. The competition
among factories has totally transformed the work environment—and organizational
culture—in the plants and increased production performance.
Better Predictions, Better Results The CEO, other executives, and managers view their dashboards from mobile devices or computers. They are able to monitor
operations in factories and farms around the globe. Dashboards keep management
informed because they can discover answers to their own questions by drilling
down. Data are used to forecast and predict crop yields—and ultimately combine
weather and geopolitical data to predict and avoid food shortages. By integrating
all of its data into one environment and making the results available in near real
time to those who need it, the organization is increasing its bottom line and driving
innovation.
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6 Chapter 1 Doing Business in Digital Times
Questions 1. All it takes is one compromised ingredient to contaminate food and
to put human lives at risk. Delays in communicating contaminated food increase the health risk and fi nes for violating the Food Safety Mod- ernization Act. How can the SCM system help McCain Foods reduce the risks related to low-quality or contaminated frozen foods reaching consumers?
2. What three challenges or threats facing McCain Foods and what is the reason for each challenge or threat?
3. How have dashboards on the factory fl oors impacted performance at McCain Foods?
4. What might be the KPIs of a frozen food manufacturer such as McCain Foods?
5. Explain how visibility about operations and performance created healthy competition among McCain’s factory workers.
6. Being able to make reliable predictions can improve business perfor- mance. Explain why.
Sources: Compiled from Smith (2013), Transparency Market Research (2013), and McCain Foods Teradata video (2013).
Digital business is a social, mobile, and Web-focused business.
Business model is how a business makes money. Digital business model defines how a business makes money digitally.
Customer experience (CX) is about building the digital infrastructure that allows cus- tomers to do whatever they want to do, through whatever channel they choose to do it.
Today, a top concern of well-established corporations, global financial institutions,
born-on-the-Web retailers, and government agencies is how to design their digital business models in order to:
• deliver an incredible customer experience;
• turn a profit;
• increase market share; and
• engage their employees.
In the digital (online) space, the customer experience (CX) must measure up to the very best the Web has to offer. Stakes are high for those who get it right—or
get it wrong. Forrester research repeatedly confirms there is a strong relationship
between the quality of a firm’s CX and loyalty, which in turn increases revenue
(Schmidt-Subramanian et al., 2013).
This section introduces the most disruptive and valuable digital technologies,
which you will continue to read about throughout this book.
1.1 Every Business Is a Digital Business
DIGITAL TECHNOLOGIES OF THE 2010S—IN THE CLOUD, HANDHELD, AND WEARABLE
Consumers expect to interact with businesses anytime anywhere via mobile
apps or social channels using technology they carry in their pockets. Mobile apps
have changed how, when, and where work is done. Employees can be more produc-
tive when they work and collaborate effortlessly from their handheld or wearable
devices.
Cloud Computing
Enterprises can acquire the latest apps and digital services as they are needed and
without large upfront investments by switching from owning IT resources to cloud computing (Figure 1.6). Cloud computing ranges from storing your files in Dropbox to advanced cloud services. In short, with the cloud, resources no longer depend
on buying that resource. For example, Amazon Elastic Compute Cloud, known as
Cloud computing is a style of computing in which IT services are delivered on- demand and accessible via the Internet. Common exam- ples are Dropbox, Gmail, and Google Drive.
Food Safety Modernization Act (FSMA), signed into law in early 2011, requires all companies in food supply chains to be able to trace foods back to the point of origin (farm) and forward to the consumer’s plate (fork). The term for the effort is farm-to-fork traceability. Public health is the chief con- cern, followed by potential liability and brand protection issues.
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1.1 Every Business Is a Digital Business 7
Figure 1.6 Cloud computing is an important evolution in data storage, software, apps, and delivery of IT services. An example is Apple iCloud—a cloud service used for online storage and synchronization of mail, media fi les, contacts, calendar, and more.
EC2, eliminates the need to invest in hardware up front, so companies can develop
and deploy applications faster. EC2 enables companies to quickly add storage
capacity as their computing requirements change. EC2 reduces the time it takes to
acquire server space from weeks to minutes.
Machine-to-Machine Technology
Sensors can be embedded in most products. Objects that connect themselves to
the Internet include cars, heart monitors, stoplights, and appliances. Sensors are
designed to detect and react, such as Ford’s rain-sensing front wipers that use
an advanced optical sensor to detect the intensity of rain or snowfall and adjust
wiper speed accordingly. Machine-to-machine (M2M) technology enables sensor- embedded products to share reliable real time data via radio signals. M2M and
the Internet of Things (IoT) are widely used to automate business processes in industries ranging from transportation to health care. By adding sensors to trucks,
turbines, roadways, utility meters, heart monitors, vending machines, and other
equipment they sell, companies can track and manage their products remotely.
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Internet of things (IoT) refers to a set of capabilities enabled when physical things are connected to the Internet via sensors.
TECH NOTE 1.1 The Internet of Things
The phrase Internet of Things was coined by Kevin Ashton in 1999 while he was em- ployed at Procter & Gamble. It refers to objects (e.g., cars, refrigerators, roadways)
that can sense aspects of the physical world, such as movement, temperature, light-
ing, or the presence or absence of people or objects, and then either act on it or re-
port it. Instead of most data (text, audio, video) on the Internet being produced and
used by people, more data are generated and used by machines communicating with
other machines—or M2M, as you read at the start of this chapter. Smart devices use
IP addresses and Internet technologies like Wi-Fi to communicate with each other
or directly with the cloud. Recent advances in storage and computing power avail-
able via cloud computing are facilitating adoption of the IoT.
The IoT opens new frontiers for improving processes in retail, health care,
manufacturing, energy, and oil and gas exploration. For instance, manufacturing
processes with embedded sensors can be controlled more precisely or monitored
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8 Chapter 1 Doing Business in Digital Times
Big Data
There is no question that the increasing volume of data can be valuable, but only if
they are processed and available when and where they are needed. The problem is
that the amount, variety, structure, and speed of data being generated or collected
by enterprises differ significantly from traditional data. Big data are what high-
volume, mostly text data are called. Big data stream in from multiple channels and
sources, including:
• mobile devices and M2M sensors embedded in everything from airport
runways to casino chips. Later in this chapter, you will read more about the
Internet of Things.
• social content from texts, tweets, posts, blogs.
• clickstream data from the Web and Internet searches.
• video data and photos from retail and user-generated content.
• financial, medical, research, customer, and B2B transactions.
Big data are 80 to 90 per cent unstructured. Unstructured data do not have a pre- dictable format like a credit card application form. Huge volumes of unstructured data
flooding into an enterprise are too much for traditional technology to process and ana-
lyze quickly. Big data tend to be more time-sensitive than traditional (or small) data.
The exploding field of big data and analytics is called data science. Data sci- ence involves managing and analyzing massive sets of data for purposes such as
target marketing, trend analysis, and the creation of individually tailored products
and services. Enterprises that want to take advantage of big data use real time data
from tweets, sensors, and their big data sources to gain insights into their custom-
ers’ interests and preference, to create new products and services, and to respond
to changes in usage patterns as they occur. Big data analytics has increased the
demand for data scientists, as described in Career Insight 1.1.
for hazards and then take corrective action, which reduces injuries, damage, and
costs. IoT combined with big data analytics can help manufacturers improve the
effi ciency of their machinery and minimize energy consumption, which often is the
manufacturing industry’s second-biggest expense.
The health sector is another area where IoT can help signifi cantly. For example,
a person with a wearable device that carries all records of his health could be monitored
constantly. This connectivity enables health services to take necessary measures for
maintaining the wellbeing of the person.
Data Scientist
Big data, analytics tools, powerful networks, and greater
processing power have contributed to growth of the
field of data science. Enterprises need people who are
capable of analyzing and finding insights in data cap-
tured from sensors, M2M apps, social media, wearable
technology, medical testing, and so on. Demand for data
scientists is outpacing the supply of talent. It is projected
that the data scientist career option will grow 19 per
cent by 2020—surpassed only by video game design-
ers. Talent scarcity has driven up salaries. According to
C A R E E R I N S I G H T 1 . 1 H O T C A R E E R
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1.1 Every Business Is a Digital Business 9
Glassdoor data (glassdoor.com, 2014), the median salary
for data scientists in the United States is $117,500. By
contrast, a business analyst earns an average of $61,000.
Profiles of Data Scientists at Facebook, LinkedIn, and Bitly
• Facebook’s Jeff Hammerbacher. Jeff helped Facebook make sense out of huge volumes of user
data when he joined the company in 2006. Facebook’s
data science team analyzes the self-reported data on
each user’s Facebook page in order to target ads
based on things the user actually likes.
• LinkedIn’s DJ Patil. DJ worked at LinkedIn as chief data scientist. Many of the cool products on
LinkedIn were built using data from self-reporting
and machine learning.
• Bitly’s Hilary Mason. Hilary was chief scientist at Bitly, which offers URL shortening and redirec-
tion services with real time link tracking. Bitly sees
behavior from billions of people a month by analyz-
ing tens of millions of links shared per day, which are
clicked hundreds of millions times. The clickstreams
generate an enormous amount of real time data.
Using data analytics, Hillary and her team detected
and solved business problems that were not evident.
Data Science Is Both an Art and a Science
In their 2012 Harvard Business Review article titled
“Data Scientist: The Sexiest Job of the 21st Century,”
authors Thomas Davenport and D. J. Patil define a data
scientist as a “high-ranking professional with the train-
ing and curiosity to make discoveries in the world of big
data” (Davenport & Patil, 2012). They described how
data scientist Jonathan Goldman transformed LinkedIn
after joining the company in 2006. At that time, LinkedIn
had less than 8 million members. Goldman noticed that
existing members were inviting their friends and col-
leagues to join, but they were not making connections
with other members at the rate executives had expected.
A LinkedIn manager said, “It was like arriving at a con-
ference reception and realizing you don’t know anyone.
So you just stand in the corner sipping your drink—and
you probably leave early.” Goldman began analyzing
the data from user profiles and looked for patterns that
to predict whose networks a given profile would land
in. While most LinkedIn managers saw no value in
Goldman’s work, Reid Hoffman, LinkedIn’s cofounder
and CEO at the time, understood the power of analytics
because of his experiences at PayPal. With Hoffman’s
approval, Goldman applied data analytics to test what
would happen if member were presented with names
of other members they had not yet connected with, but
seemed likely to know. He displayed the three best new
matches for each member based on his or her LinkedIn
profile. Within days, the click-through rate on those
matches skyrocketed and things really took off. Thanks
to this one feature, LinkedIn’s growth increased dra-
matically.
The LinkedIn example shows that good data sci-
entists do much more than simply try to solve obvious
business problems. Creative and critical thinking are
part of their job—that is, part analyst and part artist.
They dig through incoming data with the goal of dis-
covering previously hidden insights that could lead to
a competitive advantage or detect a business crisis in
enough time to prevent it. Data scientists often need
to evaluate and select those opportunities and threats
that would be of greatest value to the enterprise or
brand.
Sources: Kelly (2013), Lockhard & Wolf (2012), Davenport & Patil (2012), U.S. Department of Labor, Bureau of Labor Statistics (2014).
SOCIAL-MOBILE-CLOUD MODEL
The relationship among social, mobile, and cloud technologies is shown in Figure 1.7. The cloud consists of huge data centers accessible via the Internet and
forms the core by providing 24/7 access to storage, apps, and services. Handhelds
and wearables, such as Google Glass, Pebble, and Sony Smartwatch (Figure 1.8),
and their users form the edge. Social channels connect the core and edge. The
SoMoClo integration creates the technical and services infrastructure needed for
digital business. This infrastructure makes it possible to meet the expectations of
employees, customers, and business partners given that almost everyone is con-
nected (social), everywhere they go (mobile), and has 24/7 access to data, apps, and
other services (cloud).
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10 Chapter 1 Doing Business in Digital Times
Here are three examples of their influence:
1. Powerful social infl uences impact advertising and marketing: Connections and feedback via social networks have changed the balance of infl uence. Consum-
ers are more likely to trust tweets from ordinary people than recommendations
made by celebrity endorsements. And, negative sentiments posted or tweeted
can damage brands.
2. Consumer devices go digital and offer new services. The Nike Fuel- band wristband helps customers track their exercise activities and calories
burned. The device links to a mobile app that lets users post their progress
on Facebook.
3. eBay’s move to cloud technology improves sellers’ and buyers’ experiences. The world’s largest online marketplace, eBay, moved its IT infrastructure to the
cloud. With cloud computing, eBay is able to introduce new types of landing
pages and customer experiences without the delay associated with having to buy
additional computing resources.
The balance of power has shifted as business is increasingly driven by individu-
als for whom mobiles are an extension of their body and mind. They expect to use
location-aware services, apps, alerts, social networks, and the latest digital capabili-
ties at work and outside work. To a growing extent, customer loyalty and revenue
growth depend on a business’s ability to offer unique customer experiences that
wow customers more than competitors can.
Figure 1.7 Model of the integration of cloud, mobile, and social technologies. The cloud forms the core. Mobile devices are the endpoints. Social networks create the connections. ©
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1.1 Every Business Is a Digital Business 11
Figure 1.8 Strong interest in smart wearable technology refl ects growing consumer desire to be more digitally connected at all times using a collection of multiple devices. A smartwatch used at work, such as in a retail store, can provide shop fl oor staff with a screen to check stock availability.
DIGITAL BUSINESS MODELS
Business models are the ways enterprises generate revenue or sustain themselves.
Digital business models define how businesses make money via digital technology.
Companies that adopt digital business models are better positioned to take advan-
tage of business opportunities and survive, according to the Accenture Technology Vision 2013 report (Accenture, 2013). Figure 1.9 contains examples of new tech- nologies that destroyed old business models and created new ones.
Figure 1.9 Digital business models refer to how companies engage their customers digitally to create value via websites, social channels, and mobile devices.
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Location-aware technologies
track items through
production and delivery to
reduce wasted time and
inefficiency in supply chains
and other business-to-
business (B2B) transactions.
Twitter dominates the
reporting of news and events
as they are still happening.
Facebook became the most
powerful sharing network
in the world.
Smartphones, tablets, other
touch devices, and their apps
reshaped how organizations
interact with customers—and
how customers want
businesses to interact with
them.
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12 Chapter 1 Doing Business in Digital Times
The ways in which market leaders are transitioning to digital business models
include the following:
• Amazon gains a competitive edge with high-tech tech support. Amazon is well known for radically changing online shopping and e-book reading
experiences. Amazon’s CEO Jeffrey Bezos set a new standard for tech sup-
port with MayDay (Figure 1.10). Within 15 seconds of touching the MayDay button on their Kindle Fire HDX tablet, customers get free, 24/7/365 tech
support via video chat. MayDay works by integrating all customer data and
instantly displaying the results to a tech agent when a customer presses the
MayDay button. Plus, tech agents can control and write on a customer’s Fire
screen. By circling and underlining various buttons on the display, it is dead
simple for new Fire owners to become expert with their devices. Amazon’s
objective is to educate the consumer rather than just fix the problem. In the
highly competitive tablet wars, Amazon has successfully differentiated its
tablet from those of big players like Apple, Samsung, and Asus (manufac-
turer of Google’s Nexus 7) with the MayDay button.
• NBA talent scouts rely on sports analytics and advanced scouting systems. NBA talent scouts used to crunch players’ stats, watch live player perfor-
mances, and review hours of tapes to create player profiles (Figure 1.11).
Now software that tracks player performance has changed how basketball
and soccer players are evaluated. For example, STATS’ SportVU tech-
nology is revolutionizing the way sports contests are viewed, understood,
played, and enjoyed. SportVU uses six palm-sized digital cameras that
track the movement of every player on the court, record ball movement
25 times per second, and convert movements into statistics. SportVU
produces real time and highly complex statistics to complement the tra-
ditional play-by-play. Predictive sport analytics can provide a 360-degree
view of a player’s performance and help teams make trading decisions.
Figure 1.10 MayDay video chat tech support.
Figure 1.11 Sports analytics and advanced scouting systems evaluate talent and performance for the NBA—offering teams a slight but critical competitive advantage.
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1.1 Every Business Is a Digital Business 13
Sports analytics bring about small competitive advantages that can shift
games and even playoff series.
• Dashboards keep casino floor staff informed of player demand. Competition in the gaming industry is fierce, particularly during bad economic condi-
tions. The use of manual spreadsheets and gut-feeling decisions did not lead
to optimal results. Casino operators facing pressure to increase their bottom
line have invested in analytic tools, such as Tangam’s Yield Management
solution (TYM). TYM is used to increase the yield (profitability) of black-
jack, craps, and other table games played in the pit (Figure 1.12). The
analysis and insights from real time apps are used to improve the gaming
experience and comfort of players.
Figure 1.12 Casinos are improving the profi tability of table games by monitoring and analyzing betting in real time.
THE RECENT PAST AND NEAR FUTURE—2010S DECADE
We have seen great advances in digital technology since the start of this decade.
Figure 1.13 shows releases by tech leaders that are shaping business and everyday
life. Compare the role of your mobiles, apps, social media, and so on in your per-
sonal life and work in 2010 to how you use them today. You can expect greater
changes going forward to the end of this decade with the expansion of no-touch
interfaces, mobility, wearable technology, and the IoT.
Companies are looking for ways to take advantage of new opportunities in
mobile, big data, social, and cloud services to optimize their business processes.
The role of the IT function within the enterprise has changed significantly—and
will evolve rapidly over the next five years. As you will read throughout this book,
the IT function has taken on key strategic and operational roles that determine the
enterprise’s success or failure.
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Figure 1.13 Digital technology released since 2010.
• Google launched Android mobile
OS to compete
with iPhones
By 2014, became
the first billion-user
mobile OS
• App Store opened on July 10, 2008
via an update to iTunes
By mid-2011, over 15 billion apps
downloaded from App Store
2008
• Apple launched iPad
100 million
iPads sold
in 2 years
2010 2011–2012
• No tough interfaces to communicate
by simply gesturing
or talking
Microsoft’s Kinect for
Windows Apple’s Siri
Google’s Glass
• iWatch released integrates with
iOS devices
2014
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14 Chapter 1 Doing Business in Digital Times
More objects are being embedded with sensors and gaining the ability to communicate with the Internet. This communi- cation improves business processes while reducing costs and risks. For example, sensors and network connections can be embedded in rental cars. Zipcar has pioneered the car rental by the hour business model. See Figure 1.14. Cars are leased for short time spans to registered members, making retail rental centers unnecessary. Traditional car rental agencies are starting to experiment with sensors so that each car’s use can be optimized to increase revenue.
When devices or products are embedded with sensors, companies can track their movements or monitor interac- tions with them. Business models can be adjusted to take advantage of what is learned from this behavioral data. For example, an insurance company offers to install location sensors in customers’ cars. By doing so, the company develops the ability to price the drivers’ policies on how a car is driven and where it travels. Pricing is customized to match the actual risks of operating a vehicle rather than based on general proxies—driver’s age, gender, or location of residence.
Opportunities for Improvement
Other applications of embedded physical things are:
• In the oil and gas industry, exploration and development rely on extensive sensor networks placed in the earth’s crust. The sensors produce accurate readings of the location, structure, and dimensions of potential fields.
The payoff is lower development costs and improved oil flows.
• In the health-care industry, sensors and data links can monitor patients’ behavior and symptoms in real time and at low cost. This allows physicians to more precisely diagnose disease and prescribe treatment regimens. For example, sensors embedded in patients with heart disease or chronic illnesses can be monitored continu- ously as they go about their daily activities. Sensors placed on congestive heart patients monitor many of these signs remotely and continuously, giving doctors early warning of risky conditions. Better management of congestive heart failure alone could reduce hospi- talization and treatment costs by $1 billion per year in the U.S.
• In the retail industry, sensors can capture shoppers’ pro- file data stored in their membership cards to help close purchases by providing additional information or offering discounts at the point of sale.
• Farm equipment with ground sensors can take into account crop and field conditions, and adjust the amount of fertilizer that is spread on areas that need more nutrients.
• Billboards in Japan scan people passing by, assessing how they fit consumer profiles, and instantly change the displayed messages based on those assessments.
• The automobile industry is developing systems that can detect imminent collisions and take evasive action. Certain basic applications, such as automatic braking systems, are available in high-end autos. The potential accident reduction savings resulting from wider deploy- ment of these sensor systems could exceed $100 billion annually.
Questions
1. Research Zipcar. How does this company’s business model differ from that of traditional car rental companies, such as Hertz or Avis?
2. Think of two physical things in your home or office that, if they were embedded with sensors and linked to a net- work, would improve the quality of your work or personal life. Describe these two scenarios.
3. What might the privacy concerns be?
IT at Work 1 . 1 Zipcar and Other Connected Products
Figure 1.14 A Zipcar-reserved parking sign in Washington, DC.
© W
is ke
rk e /A
la m
y
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1.2 Business Process Management and Improvement 15
By 2016 wearable electronics in shoes, tattoos, and accessories will become a $10 billion industry, according to Gartner (2012).
Wearable technology builds computing, connectivity, and sensor capabilities into materials. The latest wearables are lightweight and may be found in athletic shoes, golf accessories, and fitness trackers. The wearables can include data analysis apps or services that send feedback or insights to the wearer. For example, Zepp Labs manufactures sensor- embedded gloves for golf, tennis, and baseball that analyze 1,000 data points per second to create 3D representations of a player’s swing. The sensors track every inch of a golfer’s swing, analyzes the movements, and then sends the wearers advice on how to improve their game. Sensors that weigh only half an ounce clip onto the glove. Another example is Sony’s SmartBand, a wristband that synchs with your phone to track how many steps you take, the number of calories you burn each day, and how well you sleep. The Lifelog app is the key to the Smartband. The app gives a visual display of a timeline and your activity, with boxes monitoring your steps, calories, kilometers walked, and more. Lifelog goes beyond
just fitness by also monitoring time spent on social networks and photos taken.
The major sources of revenue from wearable smart electronics are items worn by athletes and sports enthusiasts and devices used to monitor health conditions, such as auto- matic insulin delivery for diabetics.
Applications and services are creating new value for consumers, especially when they are combined with personal preferences, location, biosensing, and social data. Wearable electronics can provide more detailed data to retailers for targeting advertisements and promotions.
Questions
1. Discuss how wearable electronics and the instant feedback they send to your mobile device could be valuable to you.
2. How can data from wearable technology be used to improve worker productivity or safety?
3. What are two other potentially valuable uses of instant feedback or data from wearable technology?
4. How can wearable devices impact personal privacy?
IT at Work 1 . 2 Wearable Technology
All functions and departments in the enterprise have tasks that they need to com-
plete to produce outputs, or deliverables, in order to meet their objectives. Business processes are series of steps by which organizations coordinate and organize tasks to get work done. In the simplest terms, a process consists of activities that convert inputs into outputs by doing work.
The importance of efficient business processes and continuous process improve-
ment cannot be overemphasized. Why? Because 100 per cent of an enterprise’s perfor-
mance is the result of its processes. Maximizing the use of inputs in order to carry out
similar activities better than one’s competitors is a critical success factor. IT at Work 1.3
describes the performance gains at AutoTrader.com, the automobile industry’s largest
online shopping marketplace, after it redesigned its order-to-cash process.
1.2 Business Process Management and Improvement
Objectives define the desired benefits or expected per- formance improvements. They do not and should not describe what you plan to do, how you plan to do it, or what you plan to produce, which is the function of processes.
Questions 1. What are the benefi ts of cloud computing? 2. What is machine-to-machine (M2M) technology? Give an example of a
business process that could be automated with M2M. 3. Describe the relationships in the SoMoClo model. 4. Explain the cloud. 5. Why have mobile devices given consumers more power in the marketplace? 6. What is a business model? 7. What is a digital business model? 8. Explain the Internet of Things.
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16 Chapter 1 Doing Business in Digital Times
AutoTrader.com is the leading automotive marketplace, list- ing several million new and pre-owned vehicles, as shown in Figure 1.15. AutoTrader.com is one of the largest local online advertising entities, with profits of $300 million on $1.2 billion in revenues in 2013. The site attracts over 15 mil- lion unique visitors each month.
Outdated Order-to-Cash Process
AutoTrader processes thousands of orders and contracts each month. Its cross-functional order fulfillment process, or order-to-cash process, was outdated and could not handle the sales volume. The legacy process was run on My AutoTrader (MAT), a system based on Lotus Notes/Domino. MAT took an average of 6.3 to 8.3 days to fulfill orders and process contracts, as Figure 1.16 shows. MAT created a bot- tleneck that slowed the time from order to cash, or revenue generation. With over 100 coordinated steps, the process was bound to be flawed, resulting in long and error-prone cycle times. Cycle time is the time required to complete a given process. At AutoTrader, cycle time is the time between
the signing and delivery of a contract. Customers were aggravated by the unnecessary delay in revenue.
Redesigning the Order Fulfillment Process with BPM
Management had set three new objectives for the company: to be agile, to generate revenue faster, and to increase customer satisfaction. They invested in a BPM (business pro- cess management) solution—selecting webMethods from Software AG (softwareag.com, 2011). The BPM software was used to document how tasks were performed using the legacy system. After simplifying the process as much as possible, remaining tasks were automated or optimized. The new system cuts down the order fulfillment process to 1 day, as shown in Figure 1.17. Changes and benefits resulting from the redesigned process are:
• There are only six human tasks even though the pro- cess interacts with over 20 different data sources and systems, including the inventory, billing, and contract fulfillment.
• Tasks are assigned immediately to the right people, who are alerted when work is added to their queues.
• Fewer than five percent of orders need to go back to sales for clarification—a 400 percent improvement.
• Managers can check order fulfillment status anytime using webMethods Optimize for Process, which provides real time visibility into performance. They can measure key performance indicators (KPIs) in real time to see where to make improvements.
• Dealers can make changes directly to their contracts, which cut costs for personnel. Software and hardware costs are decreasing as the company retires old systems.
Sources: Compiled from Walsh (2012), softwareag.com (2011), Alesci &
Saitto (2012).
IT at Work 1 . 3 AutoTrader Redesigns Its Order-to-Cash Process
Figure 1.15 AutoTrader.com car search site.
Figure 1.16 AutoTrader’s legacy order fulfi llment process had an average cycle time of up to 8.3 days.
© N
e tP
h o
to s/
A la
m y
Fulfillment Total Avg
Quality
Assurance
Contract
Delivered
Data EntryFax
2.8 days
2.8 days
.5 day 4 days 8.3 days
6.3 days
1 day
1 day2 days.5 day
New
Up-sell
Contract
Signed
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1.2 Business Process Management and Improvement 17
Figure 1.17 AutoTrader’s objective is to process and fi ll orders within one day.
Questions
1. Discuss how the redesigned order process supports the company’s three new business objectives.
2. How does the reduced cycle time of the order fulfillment pro- cess improve revenue generation and customer satisfaction?
3. Does reducing the cycle time of a business process also reduce errors? Why or why not?
THREE COMPONENTS OF BUSINESS PROCESSES
Business processes have three basic components, as shown in Figure 1.18. They
involve people, technology, and information.
Examples of common business processes are:
• Accounting: Invoicing; reconciling accounts; auditing
• Finance: Credit card or loan approval; estimating credit risk and financing terms
• Human resources (HR): Recruiting and hiring; assessing compliance with regulations; evaluating job performance
• IT or information systems: Generating and distributing reports and data visualizations; data analytics; data archiving
• Marketing: Sales; product promotion; design and implementation of sales campaigns; qualifying a lead
• Production and operations: Shipping; receiving; quality control; inventory management
• Cross-functional business processes: Involving two or more functions, for example, order fulfillment and product development
Designing an effective process can be complex because you need a deep under-
standing of the inputs and outputs (deliverables), how things can go wrong, and how to prevent things from going wrong. For example, Dell had implemented a new
process to reduce the time that tech support spent handling customer service calls. In
an effort to minimize the length of the call, tech support’s quality dropped so much
that customers had to call multiple times to solve their problems. The new process
had backfired—increasing the time to resolve computer problems and aggravating
Dell customers.
Figure 1.18 Three components of a business process.
Deliverables are the outputs or tangible things that are produced by a business pro- cess. Common deliverables are products, services, actions, plans, or decisions, such as to approve or deny a credit application. Deliverables are produced in order to achieve specific objectives.
Submit sales
order
electronically
Day 1:
Live online
processing of orders
Day 2:
Order fulfillment
1 day elapsed
raw materials,
data,
knowledge,
expertise
work that
transforms
inputs & acts on
data and
knowledge
products,
services,
plans,
or actions
Inputs Activities
Business Process
Deliverables
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18 Chapter 1 Doing Business in Digital Times
Characteristics of Business Processes
Processes can be formal or informal. Formal processes are documented and have well-established steps. Order taking and credit approval processes are examples.
Routine formal processes are referred to as standard operating procedures, or SOPs. A SOP is a well-defined and documented way of doing something. An effec- tive SOP documents who will perform the tasks; what materials to use; and where,
how, and when the tasks are to be performed. SOPs are needed for the handling of
food, hazardous materials, or situations involving safety, security, or compliance. In
contrast, informal processes are typically undocumented, have inputs that may not yet been identified, and are knowledge-intensive. Although enterprises would pre-
fer to formalize their informal processes in order to better understand, share, and
optimize them, in many situations process knowledge remains in people’s heads.
Processes range from slow, rigid to fast-moving, adaptive. Rigid processes
can be structured to be resistant to change, such as those that enforce security or
compliance regulations. Adaptive processes are designed to respond to change or
emerging conditions, particularly in marketing and IT.
Process Improvement
Given that a company’s success depends on the efficiency of its business processes,
even small improvements in key processes have significant payoff. Poorly designed,
flawed, or outdated business processes waste resources, increasing costs, causing
delays, and aggravating customers. For example, when customers’ orders are not
filled on time or correctly, customer loyalty suffers, returns increase, and reship-
ping increases costs. The blame may be flawed order fulfilment processes and not
employee incompetence, as described in IT at Work 1.2.
Simply applying IT to a manual or outdated process will not optimize it.
Processes need to be examined to determine whether they are still necessary.
After unnecessary processes are identified and eliminated, the remaining ones are
redesigned (or reengineered) in order to automate or streamline them. Methods
and efforts to eliminate wasted steps within a process are referred to as business process reengineering (BPR). The goal of BPR is to eliminate the unnecessary, non-value-added processes, then to simplify and automate the remaining processes
to significantly reduce cycle time, labor, and costs. For example, reengineering the
credit approval process cuts time from several days or hours to minutes or less.
Simplifying processes naturally reduces the time needed to complete the process,
which also cuts down on errors.
After eliminating waste, digital technology can enhance processes by (1) auto-
mating existing manual processes; (2) expanding the data flows to reach more func-
tions in order to make it possible for sequential activities to occur in parallel; and
(3) creating innovative business processes that, in turn, create new business models.
For instance, consumers can scan an image of a product and land on an e-commerce
site, such as Amazon.com, selling that product. This process flips the traditional
selling process by making it customer-centric.
Business Process Management
BPR is part of the larger discipline of business process management (BPM), which consists of methods, tools, and technology to support and continuously improve
business processes. The purpose of BPM is to help enterprises become more agile
and effective by enabling them to better understand, manage, and adapt their busi-
ness processes. Vendors, consulting and tech firms offer BPM expertise, services,
software suites, and tools.
BPM software is used to map processes performed either by computers or
manually—and to design new ones. The software includes built-in templates show-
ing workflows and rules for various functions, such as rules for credit approval. These
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1.3 The Power of Competitive Advantage 19
templates and rules provide consistency and high-quality outcomes. For example, Oracle’s
WebLogic Server Process Edition includes server software and process integration
tools for automating complex business processes, such as handling an insurance claim.
But, BPM initiatives can be extremely challenging, and in order to be suc-
cessful, BPM requires buy-in from a broad cross section of the business, the right
technology selection, and highly effective change management processes. You will
read more about optimizing business processes and BPM’s role in the alignment of
IT and business strategy in Chapter 13.
Questions 1. What is a business process? Give three examples. 2. What is the difference between business deliverables and objectives? 3. List and give examples of the three components of a business process. 4. Explain the differences between formal and informal processes. 5. What is a standard operating procedure (SOP)? 6. What is the purpose of business process management (BPM)?
In business, as in sports, companies want to win—customers, market share, and so
on. Basically, that requires gaining an edge over competitors by being first to take
advantage of market opportunities, providing great customer experiences, doing
something well that others cannot easily imitate, or convincing customers why it is
a more valuable alternative than the competition.
1.3 The Power of Competitive Advantage
Agility means being able to respond quickly.
Responsiveness means that IT capacity can be easily scaled up or down as needed, which essentially requires cloud computing.
Flexibility means having the ability to quickly integrate new business functions or to easily reconfigure software or apps.
BUILDING BLOCKS OF COMPETITIVE ADVANTAGE
Having a competitive edge means possessing an advantage over your competition.
Once an enterprise has developed a competitive edge, maintaining it is an ongoing
challenge. It requires forecasting trends and industry changes and what the company
needs to do to stay ahead of the game. It demands that you continuously track your
competitors and their future plans and promptly take corrective action. In summary,
competitiveness depends on IT agility and responsiveness. The benefit of IT agility is being able to take advantage of opportunities faster or better than competitors.
Closely related to IT agility is flexibility. For example, mobile networks are flexible—able to be set up, moved, or removed easily, without dealing with cables
and other physical requirements of wired networks. Mass migration to mobile
devices from PCs has expanded the scope of IT beyond traditional organizational
boundaries—making location practically irrelevant.
IT agility, flexibility, and mobility are tightly interrelated and fully dependent
on an organization’s IT infrastructure and architecture, which are covered in greater
detail in Chapter 2.
With mobile devices, apps, platforms, and social media becoming inseparable parts
of work life and corporate collaboration and with more employees working from home,
the result is the rapid consumerization of IT. IT consumerization is the migration of consumer technology into enterprise IT environments. This shift has occurred because
personally owned IT is as capable and cost-effective as its enterprise equivalents.
COMPETITIVE ADVANTAGE
Two key components of corporate profitability are:
1. Industry structure: An industry’s structure determines the range of profi tability of the average competitor and can be very diffi cult to change.
2. Competitive advantage: This is an edge that enables a company to outperform its average competitor. Competitive advantage can be sustained only by con-
tinually pursuing new ways to compete.
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20 Chapter 1 Doing Business in Digital Times
IT plays a key role in competitive advantage, but that advantage is short-lived
if competitors quickly duplicate it. Research firm Gartner defines competitive
advantage as a difference between a company and its competitors that matters to customers.
It is important to recognize that some types of IT are commodities, which do
not provide a special advantage. Commodities are basic things that companies need to function, such as electricity and buildings. Computers, databases, and network
services are examples of commodities. In contrast, how a business applies IT to sup-
port business processes transforms those IT commodities into competitive assets.
Critical business processes are those that improve employee performance and profit
margins.
STRATEGIC PLANNING AND COMPETITIVE MODELS
Strategy planning is critical for all organizations, including government agencies,
health care providers, educational institutions, the military, and other nonprofits.
We start by discussing strategic analysis and then explain the activities or compo-
nent parts of strategic planning.
What Is Strategic (SWOT) Analysis?
There are many views on strategic analysis. In general, strategic analysis is the scan-
ning and review of the political, social, economic, and technical environments of an
organization. For example, any company looking to expand its business operations
into a developing country has to investigate that country’s political and economic
stability and critical infrastructure. That strategic analysis would include reviewing
the U.S. Central Intelligence Agency’s (CIA) World Factbook. The World Factbook provides information on the history, people, government, economy, geography,
communications, transportation, military, and transnational issues for 266 world
entities. Then the company would need to investigate competitors and their poten-
tial reactions to a new entrant into their market. Equally important, the company
would need to assess its ability to compete profitably in the market and impacts of
the expansion on other parts of the company. For example, having excess production
capacity would require less capital than if a new factory needed to be built.
The purpose of this analysis of the environment, competition, and capacity is
to learn about the strengths, weaknesses, opportunities, and threats (SWOT) of the
expansion plan being considered. SWOT analysis, as it is called, involves the evalu- ation of strengths and weaknesses, which are internal factors, and opportunities and
threats, which are external factors. Examples are:
• Strengths: Reliable processes; agility; motivated workforce
• Weaknesses: Lack of expertise; competitors with better IT infrastructure
• Opportunities: A developing market; ability to create a new market or product
• Threats: Price wars or other fierce reaction by competitors; obsolescence
SWOT is only a guide. The value of SWOT analysis depends on how the analy-
sis is performed. Here are several rules to follow:
• Be realistic about the strengths and weaknesses of your organization.
• Be realistic about the size of the opportunities and threats.
• Be specific and keep the analysis simple, or as simple as possible.
• Evaluate your company’s strengths and weaknesses in relation to those of
competitors (better than or worse than competitors).
• Expect conflicting views because SWOT is subjective, forward-looking, and
based on assumptions.
SWOT analysis is often done at the outset of the strategic planning process.
Now you will read answers to the question, “What is strategic planning?”
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1.3 The Power of Competitive Advantage 21
What Is Strategic Planning?
Strategic planning is a series of processes in which an organization selects and arranges its businesses or services to keep the organization healthy or able to func-
tion even when unexpected events disrupt one or more of its businesses, markets,
products, or services. Strategic planning involves environmental scanning and pre-
diction, or SWOT analysis, for each business relative to competitors in that business’s
market or product line. The next step in the strategic planning process is strategy.
What Is Strategy?
Strategy defines the plan for how a business will achieve its mission, goals, and objectives. The plan specifies the necessary financial requirements, budgets, and
resources. Strategy addresses fundamental issues such as the company’s position
in its industry, its available resources and options, and future directions. A strategy
addresses questions such as:
• What is the long-term direction of our business?
• What is the overall plan for deploying our resources?
• What trade-offs are necessary? What resources will need to be shared?
• What is our position compared to that of our competitors?
• How do we achieve competitive advantage over rivals in order to achieve or
maximize profitability?
Two of the most well-known methodologies were developed by Michael Porter.
Porter’s Competitive Forces Model and Strategies
Michael Porter’s competitive forces model, also called the five-forces model, has been used to identify competitive strategies. The model demonstrates how IT
can enhance competitiveness. Professor Porter discusses this model in detail in a
13-minute YouTube video from Harvard Business School.
The model recognizes five major forces (think of them as pressures or drivers) that
influence a company’s position within a given industry and the strategy that manage-
ment chooses to pursue. Other forces, including new regulations, affect all companies
in the industry, and have a rather uniform impact on each company in an industry.
According to Porter, an industry’s profit potential is largely determined by the
intensity of competitive forces within the industry, shown in Figure 1.19. A good
understanding of the industry’s competitive forces and their underlying causes is a
crucial component of strategy formulation.
Basis of the competitive forces model Before examining the model, it is helpful to understand that it is based on the fundamental concept of profitability
and profit margin:
PROFIT TOTAL REVENUES minus TOTAL COSTS
Profit is increased by increasing total revenues and/or decreasing total costs. Profit
is decreased when total revenues decrease and/or total costs increase:
PROFIT MARGIN SELLING PRICE minus COST OF THE ITEM
Profit margin measures the amount of profit per unit of sales, and does not take into
account all costs of doing business.
Five industry forces According to Porter’s competitive forces model, the five major forces in an industry affect the degree of competition, which impact profit
margins and ultimately profitability. These forces interact, so while you read about
them individually, their interaction determines the industry’s profit potential. For
example, while profit margins for pizzerias may be small, the ease of entering that
Video 1-1 Five Competitive Forces that Shape Strategy, by Michael Porter: youtube.com/ watch?v mYF2_FBCvXw
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22 Chapter 1 Doing Business in Digital Times
Threat of New Entrants
(Bargaining Power of Suppliers and Brands)
(Bargaining Power of Buyers and Distribution Channels)
Rivalry
Competing Companies
Our Company
Threat of Substitute Products or Services
Supplier Power Buyer Power
Figure 1.19 Porter’s competitive forces model.
industry draws new entrants. Conversely, profit margins for delivery services may
be large, but the cost of the IT needed to support the service is a huge barrier to
entry into the market.
The five industry (or market) forces are:
1. Threat of entry of new competitors. Industries that have large profi t margins attract entrants into the market to a greater degree than industries with small
margins. The same principle applies to jobs—people are attracted to higher-paying
jobs, provided that they can meet the criteria or acquire the skills for that job.
In order to gain market share, entrants usually need to sell at lower prices as an
incentive. Their tactics can force companies already in the industry to defend
their market share by lowering prices—reducing profi t margin. Thus, this threat
puts downward pressure on profi t margins by driving down prices.
This force also refers to the strength of the barriers to entry into an industry, which is how easy it is to enter an industry. The threat of entry is lower (less pow-
erful) when existing companies have ITs that are diffi cult to duplicate or very
expensive. Those ITs create barriers to entry that reduce the threat of entry.
2. Bargaining power of suppliers. Bargaining power is high where the supplier or brand is powerful, such as Apple, Microsoft, and auto manufacturers. Power is
determined by how much a company purchases from a supplier. The more pow-
erful company has the leverage to demand better prices or terms, which increase
its profi t margin. Conversely, suppliers with very little bargaining power tend to
have small profi t margins.
3. Bargaining power of customers or buyers. This force is the reverse of the bar- gaining power of suppliers. Examples are Walmart and government agencies.
This force is high when there are few large customers or buyers in a market.
4. Threat of substituting products or services. Where there is product-for-product substitution, such as Kindle for Nook, there is downward pressure on prices. As
the threat of substitutes increases, the profi t margin decreases because sellers
need to keep prices competitively low.
5. Competitive rivalry among existing fi rms in the industry. Fierce competition in- volves expensive advertising and promotions, intense investments in research
and development (R&D), or other efforts that cut into profi t margins. This force
is most likely to be high when entry barriers are low, the threat of substitute
products is high, and suppliers and buyers in the market attempt to control it.
That is why this force is placed in the center of the model.
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1.3 The Power of Competitive Advantage 23
The strength of each force is determined by the industry’s structure. Existing
companies in an industry need to protect themselves against these forces.
Alternatively, they can take advantage of the forces to improve their position or to
challenge industry leaders. The relationships are shown in Figure 1.19.
Companies can identify the forces that influence competitive advantage in their
marketplace and then develop their strategy. Porter (1985) proposed three types of
strategies—cost leadership, differentiation, and niche strategies. In Table 1.2, Porter’s
three classical strategies are listed first, followed by a list of nine other general
strategies for dealing with competitive advantage. Each of these strategies can be
enhanced by IT.
TABLE 1.2 Strategies for Competitive Advantage
Strategy Description
Cost leadership Produce product/service at the lowest cost in the
industry.
Differentiation Offer different products, services, or product
features.
Niche Select a narrow-scope segment (market niche) and
be the best in quality, speed, or cost in that segment.
Growth Increase market share, acquire more customers, or
sell more types of products.
Alliance Work with business partners in partnerships, alli-
ances, joint ventures, or virtual companies.
Innovation Introduce new products/services; put new features
in existing products/services; develop new ways to
produce products/services.
Operational effectiveness Improve the manner in which internal business
processes are executed so that the fi rm performs
similar activities better than its rivals.
Customer orientation Concentrate on customer satisfaction.
Time Treat time as a resource, then manage it and use it
to the fi rm’s advantage.
Entry barriers Create barriers to entry. By introducing innovative
products or using IT to provide exceptional service,
companies can create entry barriers to discourage
new entrants.
Customer or supplier Encourage customers or suppliers to stay with
lock-in you rather than going to competitors. Reduce
customers’ bargaining power by locking them in.
Increase switching costs Discourage customers or suppliers from going to
competitors for economic reasons.
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24 Chapter 1 Doing Business in Digital Times
Primary activities are those business activities directly involved in the production of goods. Primary activities involve the purchase of materials, the processing of materi-
als into products, and delivery of products to customers. The five primary activities are:
1. Inbound logistics, or acquiring and receiving of raw materials and other inputs
2. Operations, including manufacturing and testing
3. Outbound logistics, which includes packaging, storage, delivery, and distribution
4. Marketing and sales to customers
5. Services, including customer service
The primary activities usually take place in a sequence from 1 to 5. As work
progresses, value is added to the product in each activity. To be more specific, the
incoming materials (1) are processed (in receiving, storage, etc.) in activities called
inbound logistics. Next, the materials are used in operations (2), where significant
value is added by the process of turning raw materials into products. Products need
to be prepared for delivery (packaging, storing, and shipping) in the outbound logis-
tics activities (3). Then marketing and sales (4) attempt to sell the products to cus-
tomers, increasing product value by creating demand for the company’s products.
The value of a sold item is much larger than that of an unsold one. Finally, after-
sales service (5), such as warranty service or upgrade notification, is performed for
the customer, further adding value.
Primary activities rely on the following support activities:
1. The fi rm’s infrastructure, accounting, fi nance, and management
2. Human resources (HR) management (For an IT-related HR trend, see IT at Work 1.4.)
3. Technology development, and research and development (R&D)
4. Procurement, or purchasing
Each support activity can be applied to any or all of the primary activities.
Support activities may also support each other, as shown in Figure 1.20.
Innovation and adaptability are critical success factors, or CSFs, related to Porter’s models. CSFs are those things that must go right for a company to achieve
its mission.
Accounting, legal &
finance
Human resources
management
INBOUND
LOGISTICS
Quality control,
receiving,
raw materials
control
OPERATION
Manufacturing,
packaging,
production
control, quality
control
OUTBOUND
LOGISTICS
Order handling,
delivery,
invoicing
SALES &
MARKETING
Sales
campaigns,
order taking,
social
networking,
sales analysis,
market
research
SERVICING
Warranty,
maintenance
Procurement
Product and
technology
development
Legal, accounting, financial management
Personnel, recruitment, training, staff planning, etc.
Supplier management, funding, subcontracting
Product and process design, production
engineering, market testing, R&D
S u
p p
o rt
A c ti
v it
ie s
P ri
m a ry
A c ti
v it
ie s
Figure 1.20 A fi rm’s value chain. The arrows represent the fl ow of goods, services, and data.
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1.4 Enterprise Technology Trends 25
Questions 1. What are the characteristics of an agile organization? 2. Explain IT consumerization. 3. What are two key components of corporate profi tability? 4. Defi ne competitive advantage. 5. Describe strategic planning. 6. Describe SWOT analysis. 7. Explain Porter’s fi ve-forces model, and give an example of each force.
Managers at a global energy services company could not find or access their best talent to solve clients’ technical problems because of geographic boundaries and business unit barriers. The company’s help desks supported engineers well enough for common problems, but not for difficult issues that needed creative solutions. Using Web technolo- gies to expand access to experts worldwide, the company set up new innovation communities across its business units, which have improved the quality of its services.
Dow Chemical set up its own social network to help managers identify the talent they need to carry out projects across its diverse business units and functions. To expand its talent pool, Dow extended the network to include former employees and retirees.
Other companies are using networks to tap external tal- ent pools. These networks include online labor markets such as Amazon Mechanical Turk and contest services such as InnoCentive that help solve business problems.
• Amazon Mechanical Turk is a marketplace for work that requires human intelligence. Its web service enables companies to access a diverse, on-demand workforce.
• InnoCentive is an “open innovation” company that takes R&D problems in a broad range of areas such as engineering, computer science, and business and frames them as “challenge problems” for anyone to solve. It gives cash awards for the best solutions to solvers who meet the challenge criteria.
Sources: Compiled from McKinsey Global Institute (mckinsey.com/
insights/mgi.aspx), Amazon Mechanical Turk (aws.amazon.com/
mturk), and InnoCentive (Innocentive.com).
Questions
1. Visit and review the Amazon Mechanical Turk website. Explain HITs. How do they provide an on-demand work- force?
2. Visit and review the InnoCentive website. Describe what the company does and how.
IT at Work 1 . 4 Finding Qualified Talent
At the end of his iPhone presentation at MacWorld 2007, Apple’s visionary leader
Steve Jobs displayed advice once expressed by legendary hockey player Wayne
Gretzky (Figure 1.21): “I skate to where the puck is going to be, not where it has
been.” Steve Jobs added: “And we’ve always tried to do that at Apple. Since the
very very beginning. And we always will.” He was telling us that Apple always
moves toward where it expects the future will be.
Looking at Apple’s history, you see innovative products and services that shaped
the future. For example, launching the iTunes store in April 2003 jumpstarted the
1.4 Enterprise Technology Trends
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26 Chapter 1 Doing Business in Digital Times
digital music industry. iTunes was a significant breakthrough that forever changed
the music industry and the first representation of Apple’s future outside its traditional
computing product line. You are familiar with the success of that future-driven busi-
ness model.
Three IT directions for the late 2010s are outlined next. Throughout all the
chapters in this book, you will learn how these and other digital technology are
transforming business and society.
Figure 1.21 Wayne Gretzky’s strategy for success in hockey was to skate to where the puck was going to be. Steve Jobs followed a similar forward-looking strategy. In October, 2003, Jobs announced a Windows version of the iTunes store, saying “Hell froze over,” which brought a big laugh from the audience in San Francisco.
MORE MOBILE BUSINESS APPS, FEWER DOCS ON DESKTOPS
The direction is away from the traditional desktop and documents era and toward business apps in the cloud. Why? Google Apps offers apps that provide work-
ers with information and answers with low effort—instead of having to complete
tedious actions, such as logging in or doing extensive searches. This ongoing move to mobile raises data security issues. Data stored on mobiles are at higher risk, in part because the devices can be stolen or lost.
MORE SOCIALLY ENGAGED—BUT SUBJECT TO REGULATION
Engaging customers via mobiles and social media sites—and those customers who
do not tolerate delays—is the norm. However, customers probably do not know of
restrictions on financial institutions and health–care providers that make it illegal
to respond to individuals publicly via social media. That is, for regulatory purposes,
financial institutions cannot post or respond to comments or e-mails through social
media sites because of privacy and security.
MORE NEAR-FIELD COMMUNICATION (NFC) TECHNOLOGY
Near-field communication (NFC) technology is an umbrella description covering several technologies that communicate within a limited distance. Using radio fre- quency identification (RFID) chip-based tags, as shown in Figure 1.22, devices relay identifying data, such as product ID, price, and location, to a nearby reader that
captures the data. It is projected that the global market for NFC handsets will reach
1.6 billion units by 2018, according to a recent Global Industry Analysts research
report. According to the report, strong demand is “driven by growing penetration
of mobile phones, continued rise in demand and production of smartphones, rising
penetration of NFC in consumer devices, and chip level technology developments”
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1.5 How Your IT Expertise Adds Value to Your Performance and Career 27
(NFC World, 2014). Innovative ways in which businesses are applying NFC include the following:
• Amsterdam’s Schiphol Airport has installed an NFC boarding gate allowing pas-
sengers to validate their boarding pass with a touch of their NFC smartphone.
• French leather goods brand Delage has partnered with NFC object identi-
fication specialist Selinko to integrate NFC tags into its range of premium
leather bags. Each bag will have a unique chip and a unique digital serial
number. Consumers with an NFC smartphone equipped with Selinko’s free
mobile app will be able to use the tag to access information about their
product and confirm its authenticity as well as access marketing offers.
• iPhone owners in the United States can make Isis payments following
AT&T’s introduction of a range of phone cases that add NFC functionality
to the devices. To use the Isis Mobile Wallet on an iPhone, the owner selects
the Isis-ready NFC case, slides the iPhone in, downloads the Isis Mobile
Wallet app from the App Store, and taps the iPhone at hundreds of thou-
sands of merchants nationwide for a quick way to pay.
These trends are forces that are changing competition, business models, how
workers and operations are managed, and the skills valuable to a career in business.
Figure 1.22 NFC technology relies on sensors or RFID chips. NFC is used for tracking wine and liquor to manage the supply chain effi ciently. NFC smartphones are being integrated into payment systems in supermarkets so customers can pay for purchases without cash or credit cards.
Questions 1. What was the signifi cance of Apple’s introduction of the iPhones music
store? 2. What are three IT trends? 3. What are three business applications of NFC?
Every tech innovation triggers opportunities and threats to business models and
strategies. With rare exceptions, every business initiative depends on the mix of IT,
knowledge of its potential, the requirements for success, and, equally important, its
limitations. Staying current in emerging technologies affecting markets is essential to
the careers of knowledge workers, entrepreneurs, managers, and business leaders—
not just IT and chief information officers (CIOs).
1.5 How Your IT Expertise Adds Value to Your Performance and Career
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28 Chapter 1 Doing Business in Digital Times
WHAT COMPANIES CAN DO DEPENDS ON THEIR IT
What companies can do depends on what their information technology and data
management systems can do. For over a decade, powerful new digital approaches
to doing business—and getting through your day—have emerged. And there is
sufficient proof to expect even more rapid and dramatic changes due to IT break-
throughs and advances. Understanding trends that affect the ways business is done
and getting in front of those trends give you a career edge.
Key strategic and tactical questions that determine an organization’s profitabil-
ity and management performance are shown in Figure 1.23. Answers to each ques-
tion will entail understanding the capabilities of mundane to complex ITs, which
ones to implement, and how to manage them.
IT CAREERS OUTLOOK Having a feel for the job market helps you improve your career options. According to the U.S. Department of Labor, and the University of California Los Angeles
(UCLA), the best national jobs in terms of growth, advancement, and salary
increases in 2013 are in the fields of IT, engineering, health care, finance, construc-
tion, and management. It is projected that these job categories will see above-
average national growth over the next several years. The U.S. Department of Labor
projections are generally 6–10 years in reference.
With big data, data science, and M2M, companies are increasing their IT staff.
In addition, many new businesses are seeking more programmers and designers.
Data security threats continue to get worse. The field of IT covers a wide range that
includes processing of streaming data, data management, big data analytics, app
development, system analysis, information security, and more.
Job growth is estimated at 53 percent by 2018, according to the U.S. Department
of Labor; and salaries in many IT jobs will increase by 4 to 6 percent. The lack of
skilled IT workers in the U.S. is a primary reason for the outsourcing of IT jobs.
Digital Technology Defines and Creates Businesses and Markets
Digital technology creates markets, businesses, products, and careers. As you con-
tinue to read this book, you will see that exciting IT developments are changing how
organizations and individuals do things. New technologies and IT-supported func-
tions, such as 4G or 5G networks, embedded sensors, on-demand workforces, and
e-readers, point to ground-breaking changes. CNN.com, one of the most respected
Figure 1.23 Key strategic and tactical questions.
Business
processes,
producers,
and technology
Strategic direction;
industry, markets,
and customers
Business model
• What do we do?
• What is our direction?
• What markets & customers should we be targeting and how do we
prepare for them?
• How do we do it?
• How do we generate revenues & profits to sustain ourselves and
build our brand?
• How well do we do it?
• How can we be more efficient?
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Key Terms 29
news media, has created a new market whose impacts are yet to be realized. Visit
iReport.com where a pop-up reads, “iReport is the way people like you report the
news. The stories in this section are not edited, fact-checked or screened before
they post.”
IT as a Career: The Nature of IS and IT Work
IT managers play a vital role in the implementation and administration of digital
technology. They plan, coordinate, and direct research on the computer-related
activities of firms. In consultation with other managers, they help determine the
goals of an organization and then implement technology to meet those goals.
Chief technology officers (CTOs) evaluate the newest and most innovative technologies and determine how they can be applied for competitive advantage.
CTOs develop technical standards, deploy technology, and supervise workers
who deal with the daily IT issues of the firm. When innovative and useful new
ITs are launched, the CTO determines implementation strategies, performs
cost-benefit or SWOT analysis, and reports those strategies to top management,
including the CIO.
IT project managers develop requirements, budgets, and schedules for their firm’s information technology projects. They coordinate such projects from devel-
opment through implementation, working with their organization’s IT workers, as
well as clients, vendors, and consultants. These managers are increasingly involved
in projects that upgrade the information security of an organization.
IT Job Prospects
Workers with specialized technical knowledge and strong communications and
business skills, as well as those with an MBA with a concentration in an IT area, will
have the best prospects. Job openings will be the result of employment growth and
the need to replace workers who transfer to other occupations or leave the labor
force (Bureau of Labor Statistics, 2012–2013).
Questions 1. Why is IT a major enabler of business performance and success? 2. Explain why it is benefi cial to study IT today. 3. Why are IT job prospects strong?
Key Terms
agility
barriers to entry
big data
business model
business process
business process
management (BPM)
business process
reengineering (BPR)
business-to-business
(B2B)
chief technology offi cer
(CTO)
cloud computing
commodity
competitive advantage
competitive forces model
(fi ve-forces model)
critical success factor (CSF)
cross-functional business
process
customer experience (CX)
cycle time
dashboards
data analytics
data science
dashboard
deliverables
digital business model
formal process
inbound logistics
industry structure
informal process
Internet of Things (IoT)
IT consumerization
IT project manager
key performance
indicators (KPIs)
machine-to-machine
(M2M) technology
near-fi eld communication
(NFC) technology
objectives
operations
process
productivity
radio frequency
identifi cation (RFID)
real time system
responsiveness
services
social, mobile, and cloud
(SoMoClo)
standard operating
procedures (SOPs)
supply chain
support activities
SWOT analysis
unstructured data
wearable technology
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30 Chapter 1 Doing Business in Digital Times
Assuring Your Learning
1. Why are businesses experiencing a digital transfor- mation?
2. More data are collected in a day now than existed in the world 10 years ago. What factors have contrib-
uted to this volume of data?
3. Assume you had no smartphone, other mobile de- vice, or mobile apps to use for 24 hours. How would
that mobile blackout disrupt your ability to function?
4. What were three highly disruptive digital technolo- gies? Give an example of one disruption for each
technology.
5. Why are enterprises adopting cloud computing?
6. What is the value of M2M technology? Give two examples.
7. Starbucks monitors tweets and other sources of big data. How might the company increase revenue
from big data analytics?
8. Select three companies in different industries, such as banking, retail store, supermarket, airlines, or
package delivery, that you do business with. What
digital technologies does each company use to
engage you, keep you informed, or create a unique
customer experience? How effective is each use of
digital technology to keeping you a loyal customer?
9. Describe two examples of the infl uence of SoMoClo on the fi nancial industry.
10. What is a potential impact of the Internet of things on the health-care industry?
11. How could wearable technology be used to create a competitive edge in the athletic and sportswear
industry?
12. Why does reducing the cycle time of a business process also help to reduce errors?
13. Research fi rm Gartner defi nes competitive advantage as a difference between a company and its competi-
tors that matters to customers. Describe one use of M2M technology that could provide a manufacturer
with a competitive advantage.
14. What IT careers are forecasted to be in high de- mand? Explain why.
15. Why or how would understanding the latest IT trends infl uence your career?
DISCUSS: Critical Thinking Questions
16. Research the growing importance of big data ana- lytics. Find two forecasts of big data growth. What
do they forecast?
17. Go to the U.S. Department of Commerce website and search for U.S. Economy at a Glance: Perspec-
tive from the BEA Accounts.
a. Review the BEA homepage to learn the types of information, news, reports, and interactive data
available. Search for the page that identifi es who
uses BEA measures. Identify two users of indus-
try data and two users of international trade and
investment data.
b. Click on the Glossary. Use the Glossary to explain GDP in your own words.
c. Under the NEWS menu, select U.S. Economy at a Glance. Review the GDP current numbers for
the last two reported quarters. How did GDP
change in each of these two quarters?
EXPLORE: Online and Interactive Exercises
18. A transportation company is considering investing in a truck tire with embedded sensors—the Internet
of Things. Outline the benefi ts of this investment.
Would this investment create a long-term competi-
tive advantage for the transportation company?
19. Visit the website of UPS (ups.com), Federal Express (fedex.com), and one other logistics and delivery
company.
a. At each site, what information is available to customers before and after they send a package?
b. Compare the three customer experiences.
20. Visit YouTube.com and search for two videos on Michael Porter’s strategic or competitive forces
models. For each video, report what you learned.
Specify the complete URL, video title, who uploaded
the video and the date, video length, and number of
views.
21. Visit Dell.com and Apple.com to simulate buying a laptop computer. Compare and contrast the selection
process, degree of customization, and other buying
features. What are the barriers to entry into this mar-
ket, based on what you learned from this exercise?
ANALYZE & DECIDE: Apply IT Concepts to Business Decisions
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CASE 1.2 Business Case 31
C A S E 1 . 2 Business Case: Restaurant Creates Opportunities to Engage Customers
Back when phones were used only to make calls, few retailers and restaurants could have predicted that mo- bile technology was going to transform their industries. Smartphones and other portable devices are access points to customers. Companies can push real time, personally targeted ads to customers’ phones using text messages, or interact with them using location-aware mobile apps. And potential customers can access product or brand informa- tion using 2D codes, and comparison-shop right in the store. Brands are always looking for more effective ways to integrate social media with traditional media, such as print and TV, when implementing marketing campaigns. Managing these campaigns and interactions requires specialized software, and possibly support from the vendor or consulting firm if the company lacks in-house expertise.
Pei Wei Asian Diner’s Mobile and Cloud Campaign Pei Wei Asian Diner (www.peiwei.com), a fast-food casual restaurant chain owned by P.F. Chang’s China Bistro, is an example of a company that invested in technology to manage multichannel (also called cross-channel) marketing campaigns. In mid-2011 Pei Wei introduced a new entrée, Caramel Chicken. The company integrated traditional in-store promotions with mobile and Web-based marketing efforts to motivate people to subscribe to its e-mail marketing campaign. It also reached out to fans via Facebook and Twitter. The success of the new campaign depended on investing in appropriate software and expertise. With thousands of tweets, Facebook posts, and Google searches per second, companies need IT support to understand what people are saying about their brands.
Campaign Management Software Vendor Software vendor ExactTarget was selected to run and manage Pei Wei’s marketing campaigns. Famous brands— like Expedia, Best Buy, Nike, and Papa John’s—also used ExactTarget to power their mission-critical messages. With ExactTarget’s software, Pei Wei invited guests to join (register) its e-mail list via text, the Web, Twitter, or Face- book in order to receive a buy-one, get-one free (BOGO) coupon. Using ExactTarget’s software and infrastructure, clients such as Pei Wei can send more than thousands of e-mails per second and millions of messages in 15 minutes. A massive
infrastructure and architecture are needed to meet the de- mands of high-volume senders. Another feature of ExactTarget is the ability to respond in a real time environment. Companies need to be able to react to the real time actions that their customers are taking across all channels. That is why it is necessary to be able to quickly and easily confi gure messages that are triggered by external events like purchases or website interactions. Finally, software helps companies immediately respond to customers with burst sending capabilities—sending millions of e-mails in a few minutes.
Why the Campaign Was a Success Within two weeks, about 20,000 people had responded to the offer by registering. The BOGO coupon redemption rate at Pei Wei’s 173 locations was 20 percent. It was the restau- rant chain’s most successful new e-mail list growth effort to date. Effective marketing requires companies or brands to cre- ate opportunities with which to engage customers. Pei Wei was successful because it used multiple interactive channels to engage—connect with—current and potential custom- ers. Brands have a tremendous opportunity to connect with consumers on their mobiles in stores and on Twitter and Facebook. A 2010 ExactTarget study of more than 1,500 U.S. consumers entitled The Collaborative Future found that:
• 27 percent of consumers said they are more likely to purchase from a brand after subscribing to e-mail.
• 17 percent of consumers are more likely to purchase after liking a brand on Facebook.
A study by Forrester Consulting found that 48 percent of interactive marketing executives ranked understanding customers’ cross-channel interactions as one of the top challenges facing marketing today.
Questions 1. What software capabilities did Pei Wei need to launch its
marketing campaign? 2. What factors contributed to the success of Pei Wei’s
campaign? 3. Why is a high-capacity (massive) infrastructure needed to
launch e-mail or text campaigns? 4. Visit ExactTarget.com. Identify and describe how the
vendor makes it easy for companies to connect via e-mail and Twitter.
5. What solutions for small businesses does ExactTarget offer?
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32 Chapter 1 Doing Business in Digital Times
C A S E 1 . 3 Video Case: What Is the Value of Knowing More and Doing More?
Teradata (Teradata.com) is a leading provider of big data and data analytics solutions. In a video, Teradata explains that when you know the right thing to do, you can do more of what truly matters for your business and your customers. View the video entitled “What Would You Do
If You Knew?”™ at http://www.teradata.com/Resources/ Videos/What-would-you-do-if-you-knew/
Questions
1. What did you learn from the video? 2. What is the value of knowing and doing more?
Accenture Technology Vision 2013.
Alesci, C. & S. Saitto. “AutoTrader.Com Said to Be in Talks
About Possible IPO of Buyer-Seller Site.” Bloomberg, February 4, 2012.
Bureau of Labor Statistics. Occupational Outlook Handbook. U.S. Department of Labor, 2012–2013.
Central Intelligence Agency (CIA). World Factbook.
Cooper, B. B. “10 Surprising Social Media Statistics That Will
Make You Rethink Your Social Strategy.” Fast Company, November 18, 2013.
Davenport, T.H. & D.J. Patil. “Data Scientist: The Sexiest Job
of the 21st Century.” Harvard Business Review Magazine. October 2012.
“Gartner Reveals Top Predictions for IT Organizations and
Users for 2013 and Beyond.” Gartner Newsroom. October 24, 2012.
glassdoor.com. “Data Analyst Salaries.” May 8, 2014.
Gnau, S. “Putting Big Data in Context.” Wired, September 10, 2013.
Joy, O. “What Does It Mean to Be a Digital Native?” CNN, December 8, 2012.
Kelly, M. “Data Scientists Needed: Why This Career Is
Exploding Right Now.” VentureBeat.com, November 11, 2013.
Lockard, C.B. & M. Wolf. “Occupational Employment Projec-
tions to 2020.” Monthly Labor Review, January 2012.
McCain Foods. “McCain Foods: Integrating Data from the
Plant to the Boardroom to Increase the Bottom Line.”
Teradata.com, 2013.
NFC World. “News in Brief.” February 2014.
Pogue, D. “Embracing the Mothers of Invention.” The New York Times, January 25, 2012.
Porter, M. E. The Competitive Advantage: Creating and Sustaining Superior Performance. NY: Free Press. 1985.
Porter, M. E. “Strategy and the Internet.” Harvard Business Review, March 2001.
Schmidt-Subramanian, M., H. Manning, J. Knott, & M. Murphy.
“The Business Impact of Customer Experience, 2013.” For- rester Research, June 10, 2013.
Smith, Gavin. “Frozen Food Production in the US Industry
Market Research Report from IBISWorld Has Been Updated.”
PRWeb, March 27, 2013.
softwareag.com. “Orders Are in the Fast Lane at AutoTrader.
com—Thanks to BPM.” 2011.
Transparency Market Research. “Frozen Food Market—Global
Industry Analysis, Size, Share, Growth, Trends and Forecast,
2013–2019.” September 2013.
U.S. Department of Labor, Bureau of Labor Statistics. 2014.
Walsh, M. “Autotrader.com Tops In Local Online Ad Dollars.”
MediaPost.com, April 3, 2012.
References
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Chapter Snapshot
High performance is about outperforming rivals again and again, even as the basis of competition in an indus-
try changes. Markets do not stand still and the basis of
competition is changing at a faster pace. By the time a
company’s financial performance starts tapering off, it
might be too late to start building new market-relevant
capabilities. To stay ahead, today’s leaders seek out new
ways to grow their businesses during rapid technology
changes, more empowered consumers and employees,
and more government intervention.
Effective ways to thrive over the long term are to
launch new business models and strategies or devise new
ways to outperform competitors. In turn, these perfor-
mance capabilities depend on a company’s enterprise IT
Data Governance and IT Architecture Support Long-Term Performance2
Chapter
1. Explain the business benefits of information management and how data quality determines system success or failure.
2. Describe how enterprise architecture (EA) and data governance play leading roles in guiding IT growth and sustaining long-term performance.
3. Map the functions of various types of information systems to the type of support needed by business operations and decision makers.
4. Describe the functions of data centers, cloud computing, and virtualization and their strengths, weaknesses, and cost considerations.
5. Explain the range of cloud services, their benefits, and business and legal risks that they create.
Learning Outcomes
33
Chapter Snapshot Case 2.1 Opening Case: Detoxing Dirty Data with Data Governance at Intel Security
2.1 Information Management 2.2 Enterprise Architecture and Data
Governance 2.3 Information Systems: The Basics 2.4 Data Centers, Cloud Computing, and
Virtualization 2.5 Cloud Services Add Agility
Key Terms
Assuring Your Learning
• Discuss: Critical Thinking Questions • Explore: Online and Interactive Exercises • Analyze & Decide: Apply IT Concepts
to Business Decisions
Case 2.2 Business Case: Data Chaos Creates Risk Case 2.3 Video Case: Cloud Computing: Three Case Studies
References
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architecture and data governance. The enterprise IT archi- tecture, or simply the enterprise architecture (EA), guides the evolution and expansion of information systems,
digital technology, and business processes. This guide is
needed in order to leverage IT capability for competitive
advantage and growth. Data governance, or information governance, is the control of enterprise data through for- mal policies and procedures. A goal of data governance
is to provide employees and business partners with high-
quality data they trust and can access on demand.
C A S E 2 . 1 O P E N I N G C A S E Detoxing Dirty Data with Data Governance at Intel Security
COMPANY OVERVIEW
CUSTOMER-CENTRIC BUSINESS MODEL
Intel Security protects data and IT resources from attack and unauthorized access.
The company provides cybersecurity services to large enterprises, governments,
small- and medium-sized businesses, and consumers. A significant portion of its
revenues comes from postsales service, support, and subscriptions to its software
and managed services. The company sells directly and also through resellers to
corporations and consumers in the United States, Europe, Asia, and Latin America.
Intel Security management recognized that it needed to implement a best-practices
customer-centric business model. In the fiercely competitive industry, the ability to connect with customers, anticipate their needs, and provide flawless customer
service is essential to loyalty and long-term growth. Why? Mostly because social
and mobile technology is forcing businesses to offer excellent customer experiences
(CX) across every available touchpoint, including chat, video, mobile apps, and
alerts (Figure 2.2). A touchpoint is “any influencing action initiated through com- munication, human contact or physical or sensory interaction” (De Clerck, 2013).
Most customers search for and exchange detailed information about the good
and bad of their encounters with companies. (You will read about Yelp and the
34
Customer-centric business models strive to create the best solution or experience for the customer. In contrast, product-centric models are internally focused on creating the best product.
TABLE 2.1 Opening Case Overview
Company McAfee was renamed Intel Security in 2014. It is a sub- sidiary of Intel Corp. headquartered in Santa Clara, CA.
Has more than $2 billion in revenues annually, over 7,600
employees, and over 1 million customers.
Industry Cybersecurity software, hardware, and services.
Product lines The company develops, markets, distributes, and supports cybersecurity products that protect computers, networks,
and mobile devices. They offer managed security services
to protect endpoints, servers, networks, and mobile devices.
Consulting, training and support services are also provided.
Digital technology Data governance and master data management (MDM) in order to build a best-in-class customer data management
capability to facilitate the company’s vision.
Business vision To become the fastest-growing dedicated security company in the world.
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CASE 2.1 Opening Case 35
BUSINESS CHALLENGES FROM POOR-QUALITY CUSTOMER DATA
Intel Security is following a growth-driven business strategy. Its vision is to become
the fastest-growing dedicated security company in the world. Management rec-
ognized that accurate customer data are the foundation of top-notch customer
service. But, they faced a common business problem—poor-quality customer data.
Characteristics of poor-quality data, also known as dirty data, are listed in Table 2.2. Duplicate customer records and incomplete customer data were harming
sales. The company could not effectively cross-sell (sell complementary products or services) or up-sell (sell more expensive models or features). Opportunities to get customers to renew their software licenses—and keep them loyal—were being
lost. Data errors degraded sales forecasts and caused order-processing mistakes.
Time was wasted trying to find, validate, and correct customer records and manu-
ally reconcile month-end sales and calculate sales commissions. Until the causes of
dirty data were identified and corrected, the growth strategy could not be achieved.
Dirty data are data of such poor quality that they cannot be trusted or relied upon for decisions.
Figure 2.2 Providing excellent service to customers via their preferred touchpoints, such as online chat, has never been more important as consumers use social media to rate brands, expose bad service, and vent their frustrations.
Intel Security (formerly McAfee, Inc.)
Data governance
Master data management (MDM)
Digital Technology
Delivers proactive cybersecurity
solutions and services for
information systems, networks,
and mobile devices around the
world.
Brand Aligned Data Management with Business Strategy
Implemented data governance to
build a best-in-class customer data
management capability in order to
achieve the company’s strategic
vision.
Figure 2.1 Intel Security overview.
United Breaks Guitar video in Chapter 7.) This transparency gives companies a strong incentive to work harder to make customers happy before, during, and after
their purchases.
By creating a customer-centric business model, Intel Security can track what is
working for its customers and what is not. Using digital technology and data analytics
to understand customer touchpoints would enable the company to connect with
customers in meaningful ways. Committing to a better experience for customers can
increase revenue and promote loyalty—and achieve the company’s growth objective.
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36 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
TABLE 2.2 Characteristics of Poor-Quality or Dirty Data
Characteristic of Dirty Data Description
Incomplete Missing data.
Outdated or invalid Too old to be valid or useful.
Incorrect Too many errors.
Duplicated or in confl ict Too many copies or versions of the same
data—and the versions are inconsistent or in
confl ict with each other.
Nonstandardized Data are stored in incompatible formats—and
cannot be compared or summarized.
Unusable Data are not in context to be understood or
interpreted correctly at the time of access.
DATA QUALITY SOLUTION: DATA GOVERNANCE
Working with consulting company First San Francisco Partners, Intel Security
planned and implemented data governance and master data management (MDM). Master data are the business-critical information on customers, products, accounts, and other things that is needed for operations and business transactions. Master
data were stored in disparate systems spread across the enterprise. MDM would
link and synchronize all critical data from those disparate systems into one file,
called a master file, that provided a common point of reference. Data governance and MDM manage the availability, usability, integrity, and security of the data used
throughout the enterprise. Intel Security’s data governance strategy and MDM
were designed after a thorough review of its 1.3 million customer records, sales
processes, and estimated future business requirements.
BENEFITS OF DATA GOVERNANCE AND MDM
Data governance and MDM have improved the quality of Intel Security’s customer
data, which were essential for its customer-centric business model. With high-quality
data, the company is able to identify up-sell and cross-sell sales opportunities. Best
practices for customer data management improved customer experiences that
translated into better customer retention and acquisition. The key benefits achieved
after implementing data governance and the MDM architecture to improve data
quality are:
• Better customer experience
• Greater customer loyalty and retention
• Increased sales growth
• Accurate sales forecasts and order processing
Intel Security has successfully aligned its IT capabilities to meet business needs. All
these efforts benefit the business by improving productivity as a result of reduced
data-cleansing efforts, and by increasing sales as a result of better customer experi-
ences.
Sources: Compiled from mcafee.com (2013), De Clerck, (2013), First San Francisco Partners (2009), and Rich (2013).
Data governance is the control of enterprise data through formal policies and procedures to help ensure that data can be trusted and are accessible.
Master data management (MDM) methods synchronize all business-critical data from disparate systems into a master file, which provides a trusted data source.
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2.1 Information Management 37
Questions 1. What is the difference between customer-centric and product-centric
business models? 2. Explain the business challenges caused by Intel Security’s dirty data. 3. What is the function of data governance? 4. Describe the function of master data. 5. Why is it important to keep data synchronized across disparate systems? 6. Why did Intel Security need master data management (MDM)? 7. How did MDM and data governance enable the company to achieve its
vision? 8. What benefi ts did the company achieve as a result of implementing
data governance and MDM?
Most business initiatives succeed or fail based on the quality of their data. Effective
planning and decisions depend on systems being able to make data available to
decision makers in usable formats on a timely basis. Most everyone manages infor-
mation. You manage your social and cloud accounts across multiple mobile devices
and computers. You update or synchronize (“synch”) your calendars, appoint-
ments, contact lists, media files, documents, and reports. Your productivity depends
on the compatibility of devices and apps and their ability to share data. Not being
able to transfer and synch whenever you add a device or app is bothersome and
wastes your time. For example, when you switch to the latest mobile device, you
might need to reorganize content to make dealing with data and devices easier. To
simplify add-ons, upgrades, sharing, and access, you might leverage cloud services
such as iTunes, Instagram, Diigo, and Box.
This is just a glimpse of the information management situations that organiza- tions face today—and why a continuous plan is needed to guide, control, and govern
IT growth. As with building construction (Figure 2.3), blueprints and models help
guide and govern future IT and digital technology investments.
2.1 Information Management
Information management is the use of IT tools and methods to collect, process, consolidate, store, and secure data from sources that are often fragmented and inconsistent.
INFORMATION MANAGEMENT HARNESSES SCATTERED DATA
Business information is generally scattered throughout an enterprise, stored in
separate systems dedicated to specific purposes, such as operations, supply chain
management, or customer relationship management. Major organizations have over
100 data repositories (storage areas). In many companies, the integration of these
disparate systems is limited—as is users’ ability to access all the information they
Figure 2.3 Blueprints and models, like those used for building construction, are needed to guide and govern an enterprise’s IT assets. ©
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38 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
need. Therefore, despite all the information flowing through companies, executives,
managers, and workers often struggle to find the information they need to make
sound decisions or do their jobs. The overall goal of information management is to
eliminate that struggle through the design and implementation of data governance
and a well-planned enterprise architecture.
Providing easy access to large volumes of information is just one of the chal-
lenges facing organizations. The days of simply managing structured data are over.
Now, organizations must manage semistructured and unstructured content from
social and mobile sources even though that data may be of questionable quality.
Information management is critical to data security and compliance with con-
tinually evolving regulatory requirements, such as the Sarbanes-Oxley Act, Basel III,
the Computer Fraud and Abuse Act (CFAA), the USA PATRIOT Act, and the
Health Insurance Portability and Accountability Act (HIPAA).
Issues of information access, management, and security must also deal with
information degradation and disorder—where people do not understand what data
mean or how they can be useful.
REASONS FOR INFORMATION DEFICIENCIES
Companies’ information and decision support technologies have developed over
many decades. During that time span, there have been different management teams
with their own priorities and understanding of the role of IT; technology advanced
in unforeseeable ways, and IT investments were cut or increased based on compet-
ing demands on the budget. These are some of the contributing factors. Other com-
mon reasons why information deficiencies are still a problem include:
1. Data silos. Information can be trapped in departments’ data silos (also called information silos), such as marketing or production databases. Data silos are illustrated in Figure 2.4. Since silos are unable to share or exchange data, they
cannot consistently be updated. When data are inconsistent across multiple
enterprise applications, data quality cannot (and should not) be trusted without
extensive verifi cation. Data silos exist when there is no overall IT architecture
to guide IS investments, data coordination, and communication. Data silos sup-
port a single function and, as a result, do not support an organization’s cross-
functional needs.
For example, most health-care organizations are drowning in data, yet they
cannot get reliable, actionable insights from these data. Physician notes, regis-
tration forms, discharge summaries, documents, and more are doubling every
five years. Unlike structured machine-ready data, these are messy data that take
Data silos are stand-alone data stores. Their data are not accessible by other ISs that need it or outside that department.
Information Requirements: Understandable Relevant Timely Accurate Secure
Parts Replenish
Procuring
Design
Build
Ship
Sales
Fulfillment
Billing
Support
Customer data Product data Procurement data Contract data Data order Parts inventory data Engineering data Logistics data
Data Types
Operations silos
Sourcing silos
Customer-facing silos
Figure 2.4 Data (or information) silos are ISs that do not have the capability to exchange data with other ISs, making timely coordination and communication across functions or departments diffi cult.
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2.1 Information Management 39
too much time and effort for health-care providers to include in their business
analysis. So, valuable messy data are routinely left out. Millions of patient notes
and records sit inaccessible or unavailable in separate clinical data silos because
historically there has been no easy way to analyze the information.
2. Lost or bypassed data. Data can get lost in transit from one IS to another. Or, data might never get captured because of inadequately tuned data collection
systems, such as those that rely on sensors or scanners. Or, the data may not get
captured in suffi cient enough detail, as described in Tech Note 2.1.
3. Poorly designed interfaces. Despite all the talk about user-friendly interfaces, some ISs are horrible to deal with. Poorly designed interfaces or formats that
require extra time and effort to fi gure out increase the risk of errors from misun-
derstanding the data or ignoring them.
4. Nonstandardized data formats. When users are presented with data in inconsis- tent or nonstandardized formats, errors increase. Attempts to compare or ana-
lyze data are more diffi cult and take more time. For example, if the Northeast
division reports weekly gross sales revenues per product line and the South-
west division reports monthly net sales per product, you cannot compare their
performance without converting the data to a common format. Consider the
extra effort needed to compare temperature-related sales, such as air condition-
ers, when some temperatures are expressed in degrees Fahrenheit and others in
Centigrade.
5. Cannot hit moving targets. The information that decision makers want keeps changing—and changes faster than ISs can respond to because of the fi rst four
reasons in this list. Tracking tweets, YouTube hits, and other unstructured con-
tent requires expensive investments—which managers fi nd risky in an economic
downturn.
Without information management, these are the data challenges managers
have to face. Companies undergoing fast growth or merger activity or those with
decentralized systems (each division or business unit manages its own IT) will end
up with a patchwork of reporting processes. As you would expect, patchwork sys-
tems are more complicated to modify, too rigid to support an agile business, and yet
more expensive to maintain.
TECH NOTE 2.1 Need to Measure in Order to Manage
A residential home construction company had two divisions: standard homes and
luxury homes. The company was not capturing material, labor, and other costs
associated with each type of construction. Instead, these costs were pooled, making
it impossible to allocate costs to each type of construction and then to calculate the
profi t margins of each division. They had no way of calculating profi t margins on
each type of home within the divisions. Without the ability to measure costs, they did
not have any cost control.
After upgrading their ISs, they began to capture detailed data at the house level.
They discovered a wide profi t margin on standard homes, which was hiding the nega-
tive margins (losses) of the luxury home division. Without cost control data, the prof-
itable standard homes division had been subsidizing the luxury home division for
many years.
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40 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
Executives at a large chemical corporation were supported by an information system specifically designed for their needs—called an executive information system (EIS). The EIS was designed to provide senior managers with internal and external data and key performance indicators (KPIs) that were relevant to their specific needs. Tech Note 2.2 describes KPIs. As with any system, the value of the EIS depends on the data quality.
Too Much Irrelevant Data
The EIS was a failure. Executives found that only half of the data available through the EIS related to their level of analysis and decision making—the corporate level. A worse problem was that the data they needed were not available when and how they wanted them. For example, executives needed current detailed sales revenue and cost data for every strategic business unit (SBU), product line, and operat- ing business. Sales and cost data were needed for analysis and to compare performance. But, data were not in stan- dardized format as is needed for accurate comparisons and analysis. A large part of the problem was that SBUs reported sales revenues in different time frames (e.g., daily, weekly, monthly, or quarterly), and many of those reports were not available because of delays in preparing them. As a result, senior management could not get a trusted view of the com- pany’s current overall performance and did not know which products were profitable.
There were two reasons for the failure of the EIS:
1. IT architecture was not designed for customized reporting. The design of the IT architecture had been based on financial accounting rules. That is, the data were organized to make it easy to collect and consolidate the data needed to prepare financial statements and reports that had to be submitted to the SEC (Securities and Exchange Commission) and other regulatory agen- cies. These statements and reports have well-defined or standardized formats and only need to be prepared at specific times during the year, typically annually or quar- terly. The organization of the data (for financial reporting)
did not have the flexibility needed for the customized ad hoc (unplanned) data needs of the executives. For exam- ple, it was nearly impossible to generate customized sales performance (nonfinancial) reports or do ad hoc analyses, such as comparing inventory turnover rates by product for each region for each sales quarter. Because of lags in reports from various SBUs, executives did not trust the underlying data.
2. Complicated user interface. Executives could not easily review the KPIs. Instead, they had to sort through screens packed with too much data—some of interest and some irrelevant. To compensate for poor interface design, sev- eral IT analysts themselves had to do the data and KPI analyses for the executives—delaying response time and driving up the cost of reporting.
Solution: New Enterprise IT Architecture with Standardized Data Formats
The CIO worked with a task force to design and implement an entirely new EA. Data governance policies and proce- dures were implemented to standardize data formats com- panywide. Data governance eliminated data inconsistencies to provide reliable KPI reports on inventory turns, cycle times, and profit margins of all SBUs.
The new architecture was business-driven instead of financial reporting-driven. It was easy to modify reports— eliminating the costly and time-consuming ad hoc analyses. Fewer IT resources are needed to maintain the system. Because the underlying data are now relatively reliable, EIS use by executives increased significantly.
Questions
1. Why was an EIS designed and implemented? 2. What problems did executives have with the EIS? 3. What were the two reasons for those EIS problems? 4. How did the CIO improve the EIS? 5. What are the benefits of the new IT architecture? 6. What are the benefits of data governance?
IT at Work 2 . 1 Data Quality Determines Systems Success and Failure
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2.1 Information Management 41
TECH NOTE 2.2 KPIs
KPIs are performance measurements. These measures demonstrate the effective-
ness of a business process at achieving organizational goals. KPIs present data in
easy-to-comprehend and comparison-ready formats. Examples of key comparisons
are actual vs. budget, actual vs. forecasted, and this year vs. prior years. KPIs help
reduce the complex nature of organizational performance to a small number of
understandable measures, including:
• Financial KPIs: current ratio; accounts payable turnover; inventory turn-
over; net profit margin
• Social media KPIs: social traffic and conversions (number of visitors who
are converted to customers); likes; new followers per week; social visits and
leads
• Sales and marketing KPIs: cost per lead; how much revenue a marketing
campaign generates
• Operational and supply chain KPIs: units per transaction; carrying cost of
inventory; order status; back order rate
• Environmental and carbon-footprint KPIs: energy, water, or other resource
use; spend by utility; weight of landfill waste
FACTORS DRIVING THE SHIFT FROM SILOS TO SHARING AND COLLABORATION
BUSINESS BENEFITS OF INFORMATION MANAGEMENT
Senior executives and managers know about their data silos and information
management problems, but they also know about the huge cost and disruption
associated with converting to newer IT architectures. A Tech CEO Council Report
estimated that Fortune 500 companies waste $480 billion every year on inefficient
business processes (techceocouncil.org, 2010). However, business process improve-
ments are being made. An IBM study of more than 3,000 CIOs showed that more
than 80 percent plan to simplify internal processes, which includes integrated siloed
global applications (IBM Institute, 2011). Companies are struggling to integrate
thousands of siloed global applications, while aligning them to business operations.
To remain competitive, they must be able to analyze and adapt their business pro-
cesses quickly, efficiently and without disruption.
Greater investments in collaboration technologies have been reported by the
research firm Forrester (Keitt, 2011). The three factors that Forrester identified as
driving the trend toward collaboration and data sharing technology are shown in
Figure 2.5.
Based on the examples you have read, the obvious benefits of information manage-
ment are the following:
1. Improves decision quality. Decision quality depends on accurate and complete data.
2. Improves the accuracy and reliability of management predictions. It is essential for managers to be able to predict sales, product demand, opportunities, and
competitive threats. Management predictions focus on “what is going to happen”
as opposed to fi nancial reporting on “what has happened.”
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42 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
3. Reduces the risk of noncompliance. Government regulations and compliance requirements have increased signifi cantly in the past decade. Companies that
fail to comply with laws on privacy, fraud, anti-money laundering, cybersecurity,
occupational safety, and so on face harsh penalties.
4. Reduces the time and cost of locating and integrating relevant information.
Figure 2.5 Factors that are increasing demand for collaboration technology.
Questions 1. Explain information management. 2. Why do organizations still have information defi ciency problems? 3. What is a data silo? 4. Explain KPIs and give an example. 5. What three factors are driving collaboration and information sharing? 6. What are the business benefi ts of information management?
Every enterprise has a core set of information systems and business processes
that execute the transactions that keep it in business. Transactions include
processing orders, order fulfillment and delivery, purchasing inventory and sup-
plies, hiring and paying employees, and paying bills. The enterprise architecture (EA) helps or impedes day-to-day operations and efforts to execute business strategy.
Success of EA and data governance is measured in financial terms of prof-
itability and return on investment (ROI), and in the nonfinancial terms of
improved customer satisfaction, faster speed to market, and lower employee
turnover.
2.2 Enterprise Architecture and Data Governance
MAINTAINING IT– BUSINESS ALIGNMENT
As you read in Chapter 1, the volume, variety, and velocity of data being collected
or generated have grown exponentially. As enterprise information systems become
more complex, the importance of long-range IT planning increases dramatically.
Companies cannot simply add storage, new apps, or data analytics on an as-needed
basis and expect those additions to work with the existing systems.
62% of the workforce works
outside an office at some
point. This number is
increasing.
Global, mobile workforce
Growing number of cloud
collaboration services
Mobility-driven consumerization
Growing need to connect
anybody, anytime, anywhere
on any device
Principle of “any”
Enterprise architecture (EA) is the way IT systems and processes are structured. EA is an ongoing process of cre- ating, maintaining, and lever- aging IT. It helps to solve two critical challenges: where an organization is going and how it will get there.
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2.2 Enterprise Architecture and Data Governance 43
The relationship between complexity and planning is easier to see in physical
things such as skyscrapers and transportation systems. If you are constructing a
simple cabin in a remote area, you do not need a detailed plan for expansion or
to make sure that the cabin fits into its environment. If you are building a simple,
single-user, nondistributed system, you would not need a well-thought-out growth
plan either. Therefore, it is no longer feasible to manage big data, content from
mobiles and social networks, and data in the cloud without the well-designed set of
plans, or blueprint, provided by EA. The EA guides and controls software add-ons
and upgrades, hardware, systems, networks, cloud services, and other digital tech-
nology investments.
ONGOING PROCESS OF LEVERAGING IT
According to consulting firm Gartner, enterprise architecture is the ongoing process
of creating, maintaining, and leveraging IT. It helps to solve two critical challenges:
where an organization is going and how it will get there.
Shared Vision of the Future
EA has to start with the organization’s target–where it is going—not with where it is. Gartner recommends that an organization begin by identifying the strategic direc-
tion in which it is heading and the business drivers to which it is responding. The
goal is to make sure that everyone understands and shares a single vision. As soon
as managers have defined this single shared vision of the future, they then consider
the implications of this vision on the business, technical, information, and solutions
architectures of the enterprise. The shared vision of the future will dictate changes
in all these architectures, assign priorities to those changes, and keep those changes
grounded in business value.
Strategic Focus
There are two problems that the EA is designed to address:
1. IT systems’ complexity. IT systems have become unmanageably complex and expensive to maintain.
2. Poor business alignment. Organizations fi nd it diffi cult to keep their increasingly expensive IT systems aligned with business needs.
Business and IT Benefits of EA
Having the right architecture in place is important for the following reasons:
• EA cuts IT costs and increases productivity by giving decision makers access
to information, insights, and ideas where and when they need them.
• EA determines an organization’s competitiveness, flexibility, and IT eco-
nomics for the next decade and beyond. That is, it provides a long-term view
of a company’s processes, systems, and technologies so that IT investments
do not simply fulfill immediate needs.
• EA helps align IT capabilities with business strategy—to grow, innovate,
and respond to market demands, supported by an IT practice that is 100
percent in accord with business objectives.
• EA can reduce the risk of buying or building systems and enterprise apps
that are incompatible or unnecessarily expensive to maintain and integrate.
Basic EA components are listed and described in Table 2.3. IT at Work 2.2 describes
Gartner’s view of EA.
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44 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
TABLE 2.3 Components of Enterprise Architecture
Business architecture The processes the business uses to meet its goals.
Application architecture How specifi c applications are designed and how they interact with each other.
Data architecture How an enterprise’s data stores are organized and accessed.
Technical architecture The hardware and software infrastructure that supports applications and their interactions.
In order to keep IT and business in alignment, the EA must be a dynamic plan. As shown in the model in Figure 2.6, the EA evolves toward the target architecture, which represents the company’s future IT needs. According to this model, EA defines the following:
1. The organization’s mission, business functions, and future direction
2. Information and information flows needed to perform the mission
3. The current baseline architecture 4. The desired target architecture 5. The sequencing plan or strategy to progress from the
baseline to the target architecture.
IT at Work 2 . 2 EA Is Dynamic
Figure 2.6 The importance of viewing EA as a dynamic and evolving plan. The purpose of the EA is to maintain IT–business alignment. Changes in priorities and business are refl ected in the target architecture to help keep IT aligned with them (GAO, 2010).
Baseline Transition Target
Im p
le m
e n
ta ti
o n
S ta
tu s
Baseline architecture
Sequencing plan
Target architecture
Essential Skills of an Enterprise Architect
Enterprise architects need much more than technol-
ogy skills. The job performance and success of such
an architect—or anyone responsible for large-scale IT
projects—depend on a broad range of skills.
• Interpersonal or people skills. The job requires inter-
acting with people and getting their cooperation.
• Ability to influence and motivate. A large part of
the job is motivating users to comply with new pro-
cesses and practices.
• Negotiating skills. The project needs resources—
time, money, and personnel—that must be negoti-
ated to get things accomplished.
C A R E E R I N S I G H T 2 . 1
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2.2 Enterprise Architecture and Data Governance 45
• Critical-thinking and problem-solving skills. Architects
face complex and unique problems. Being able to
expedite solutions prevents bottlenecks.
• Business and industry expertise. Knowing the busi-
ness and industry improves the outcomes and the
architect’s credibility.
Managing EA implementations requires someone who
is able to handle multiple aspects of a project at one
time. Project management is covered in Chapter 13.
DATA GOVERNANCE: MAINTAINING DATA QUALITY AND COST CONTROL
Data governance is the process of creating and agreeing to standards and require-
ments for the collection, identification, storage, and use of data. The success of
every data-driven strategy or marketing effort depends on data governance. Data
governance policies must address structured, semistructured, and unstructured data
(discussed in Section 2.3) to ensure that insights can be trusted.
Enterprisewide Data Governance
With an effective data governance program, managers can determine where their
data are coming from, who owns them, and who is responsible for what—in order
to know they can trust the available data when needed. Data governance is an
enterprise-wide project because data cross boundaries and are used by people
throughout the enterprise. New regulations and pressure to reduce costs have increased
the importance of effective data governance. Governance eliminates the cost of
maintaining and archiving bad, unneeded, or wrong data. These costs grow as the
volume of data grows. Governance also reduces the legal risks associated with
unmanaged or inconsistently managed information.
Three industries that depend on data governance to comply with regulations or
reporting requirements are the following:
• Food industry. In the food industry, data governance is required to comply with food safety regulations. Food manufacturers and retailers have sophis-
ticated control systems in place so that if a contaminated food product, such
as spinach or peanut butter, is detected, they are able to trace the problem
back to a particular processing plant or even the farm at the start of the food
chain.
• Financial services industry. In the financial services sector, strict report- ing requirements of the Dodd–Frank Wall Street Reform and Consumer
Protection Act of 2010 are leading to greater use of data governance. The
Dodd–Frank Act regulates Wall Street practices by enforcing transparency
and accountability in an effort to prevent another significant financial crisis
like the one that occurred in 2008.
• Health-care industry. Data are health care’s most valuable asset. Hospitals have mountains of electronic patient information. New health-care account-
ability and reporting obligations require data governance models for trans-
parency to defend against fraud and to protect patients’ information.
As you read in the Intel Security opening case, data governance and MDM are
a powerful combination. As data sources and volumes continue to increase, so does
the need to manage data as a strategic asset in order to extract its full value. Making
business data consistent, trusted, and accessible across the enterprise is a critical
first step in customer-centric business models. With data governance, companies
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46 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
are able to extract maximum value from their data, specifically by making better
use of opportunities that are buried within behavioral data. According to Adele
Pugliese, data governance director of Toronto-based Scotiabank, “If we are able
to leverage and understand the data, and achieve integrity and a level of accuracy
with that data, in terms of our touchpoints with the customers, we should be able to
change that customer experience and take it to the next level where we know a lot
more about our customers” (Hamilton, 2013).
Master Data and MDM
Master data describe key entities such as customers, products and services, vendors,
locations, and employees around which business is conducted. Master data are typi-
cally quite stable—and fundamentally different from the high volume, velocity, and
variety of big data and traditional data. For example, when a customer applies for
automobile insurance, data provided on the application become the master data
for that customer. In contrast, if the customer’s vehicle has a device that sends data
about his or her driving behavior to the insurer, those machine-generated data are
transactional or operational, but not master data.
Data are used in two ways—both depend on high-quality trustworthy data:
1. For running the business: Transactional or operational use
2. For improving the business: Analytic use
Strong data governance is needed to manage the availability, usability, integrity,
and security of the data used throughout the enterprise so that data are of sufficient
quality to meet business needs. The characteristics and consequences of weak or
nonexistent data governance are listed in Table 2.4.
MDM solutions can be complex and expensive. Given their complexity and
cost, most MDM solutions are out of reach for small and medium companies.
Vendors have addressed this challenge by offering cloud-managed MDM ser-
vices. For example, in 2013 Dell Software launched its next-generation Dell Boomi
MDM. Dell Boomi provides MDM, data management, and data quality services
(DQS)—and they are 100 percent cloud-based with near real time synchronization.
Politics: The People Conflict
In an organization, there may be a culture of distrust between the technology and
business employees. No enterprise architecture methodology or data governance
can bridge this divide unless there is a genuine commitment to change. That com-
mitment must come from the highest level of the organization—senior management.
Methodologies cannot solve people problems; they can only provide a framework in
which those problems can be solved.
TABLE 2.4 Characteristics and Consequences of Weak or Nonexistent Data Governance
• Data duplication causes isolated data silos.
• Inconsistency exists in the meaning and level of detail of data elements.
• Users do not trust the data and waste time verifying the data rather than
analyzing them for appropriate decision making.
• Leads to inaccurate data analysis.
• Bad decisions are made on perception rather than reality, which can negatively
affect the company and its customers.
• Results in increased workloads and processing time.
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2.3 Information Systems: The Basics 47
Questions 1. Explain the relationship between complexity and planning. Give an
example. 2. Explain enterprise architecture. 3. What are the four components of EA? 4. What are the business benefi ts of EA? 5. How can EA maintain alignment between IT and business strategy? 6. What are the two ways that data are used in an organization? 7. What is the function of data governance? 8. Why has interest in data governance and MDM increased? 9. What role does personal confl ict or politics play in the success of data
governance?
Information systems (ISs) are built to achieve specific goals, such as processing cus-
tomer orders and payroll. In general, ISs process data into meaningful information
and knowledge.
2.3 Information Systems: The Basics
DATA, INFORMATION, AND KNOWLEDGE
Data, or raw data, describe products, customers, events, activities, and transactions that are recorded, classified, and stored. Data are the raw material from which
information is produced; the quality, reliability, and integrity of the data must be
maintained for the information to be useful. Examples are the number of hours an
employee worked in a certain week or the number of new Toyota vehicles sold in
the first quarter of 2015.
A database is a repository or data store that is organized for efficient access, search, retrieval, and update.
Information is data that have been processed, organized, or put into context so that they have meaning and value to the person receiving them. For example,
the quarterly sales of new Toyota vehicles from 2010 through 2014 is information
because it would give some insight into how the vehicle recalls during 2009 and 2010
impacted sales. Information is an organization’s most important asset, second only
to people.
Knowledge consists of data and/or information that have been processed, organized, and put into context to be meaningful, and to convey understanding,
experience, accumulated learning, and expertise as they apply to a current problem
or activity. Knowing how to manage a vehicle recall to minimize negative impacts
on new vehicle sales is an example of knowledge. Figure 2.7 shows the differences
in data, information, and knowledge.
ISs collect or input and process data, distribute reports or other outputs that
support decision making and business processes. Figure 2.8 shows the input-
processing-output (IPO) model.
Figure 2.9 shows how major types of ISs relate to one another and how data
flow among them. In this example,
1. Data from online purchases are captured and processed by the TPS, or transac- tion processing system and then stored in the transactional database.
2. Data needed for reporting purposes are extracted from the database and used by the MIS (management information system) to create periodic, ad hoc, or
other types of reports.
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48 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
Figure 2.7 Examples of data, information, and knowledge.
Figure 2.8 Input-processing- output model.
Figure 2.9 Flow of data from the point of sale (POS) through processing, storage, reporting, decision support, and analysis. Also shows the relationships among information systems.
Q1- 2008 2008
Q2- 2008 Q3-
2008 Q4-
2009 Q1-
2009 Q2-
2009 Q3-
2009 Q4-
2010 Q1-
2010 Q2-
2010 Q3-
2010 Q4-
Number of new vehicles sold in the
1st quarter of 2010 (Q1-2010)
Information
Data
Managing a vehicle recall in a way that minimizes
negative impacts on new vehicle sales and net income
Knowledge
Storage
Temporary memory (RAM), hard disks, flash memory, cloud
People
Users, clients, customers, operators, technicians, governments, companies
Sending
results,
collecting
data,
feedback
Communication
Working with
information,
changing,
calculating,
manipulating
Processing
Data collected,
captured,
scanned,
snapped from
transactions
Input
Showing
results on
screen,
hardcopy, digital
copy, archive
Output
Data
Data Data
Data are extracted, transformed, &
loaded (ETL)
Data from online purchases
of transactional data
Database
Reporting MIS
Models applied to data for analysis
DSS
Processes raw data
TPS
Analytical processing of data to discover
trends and learn insights
Data Warehouse
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2.3 Information Systems: The Basics 49
TRANSACTION PROCESSING SYSTEMS
Transaction processing systems (TPSs) are designed to process specific types of data input from ongoing transactions. TPSs can be manual, as when data are typed
into a form on a screen, or automated by using scanners or sensors to capture bar-
codes or other data (Figure 2.10).
Organizational data are processed by a TPS—sales orders, payroll, accounting,
financial, marketing, purchasing, inventory control, and so forth. Transactions are
either:
• Internal transactions that originate within the organization or that occur within the organization. Examples are payroll, purchases, budget trans-
fers, and payments (in accounting terms, they are referred to as accounts payable).
• External transactions that originate from outside the organization, for example, from customers, suppliers, regulators, distributors, and financing
institutions.
TPSs are essential systems. Transactions that are not captured can result in lost
sales, dissatisfied customers, and many other types of data errors with finan-
cial impacts. For example, if the accounting department issued a check to pay
an invoice (bill) and it was cashed by the recipient, but information about that
transaction was not captured, then two things happen. First, the amount of cash
listed on the company’s financial statements is wrong because no deduction was
made for the amount of the check. Second, the accounts payable (A/P) system
3. Data are output to a decision-support system (DSS) where they are analyzed using formulas, fi nancial ratios, or models.
Data collected by the TPS are converted into reports by the MIS and analyzed by
the DSS to support decision making. Corporations, government agencies, the mili-
tary, health care, medical research, major league sports, and nonprofits depend on
their DSSs at all levels of the organization. Innovative DSSs create and help sustain
competitive advantages. DSSs reduce waste in production operations, improve
inventory management, support investment decisions, and predict demand. The
model of a DSS consists of a set of formulas and functions, such as statistical, finan-
cial, optimization, and/or simulation models.
Customer data, sales, and other critical data are selected for additional analy-
sis, such as trend analysis or forecasting demand. These data are extracted from
the database, transformed into a standard format, and then loaded into a data
warehouse.
Figure 2.10 Scanners automate the input of data into a transaction processing system (TPS). ©
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50 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
will continue to show the invoice as unpaid, so the accounting department might
pay it a second time. Likewise, if services are provided, but the transactions are
not recorded, the company will not bill for them and thus not collect that service
revenue.
Batch vs. Online Real Time Processing
Data captured by a TPS are processed and stored in a database; they then become
available for use by other systems. Processing of transactions is done in one of two
modes:
1. Batch processing: A TPS in batch processing mode collects all transaction for a day, shift, or other time period, and then processes the data and updates the
data stores. Payroll processing done weekly or bi-weekly is an example of batch
mode.
2. Online transaction processing (OLTP) or real time processing: The TPS pro- cesses each transaction as it occurs, which is what is meant by the term real time processing. In order for OLTP to occur, the input device or website must be directly linked via a network to the TPS. Airlines need to process fl ight reserva-
tions in real time to verify that seats are available.
Batch processing costs less than real time processing. A disadvantage is that data
are inaccurate because they are not updated immediately, in real time.
Processing Impacts Data Quality
As data are collected or captured, they are validated to detect and correct obvi-
ous errors and omissions. For example, when a customer sets up an account with a
financial services firm or retailer, the TPS validates that the address, city, and postal
code provided are consistent with one another and also that they match the credit
card holder’s address, city, and postal code. If the form is not complete or errors
are detected, the customer is required to make the corrections before the data are
processed any further.
Data errors detected later may be time-consuming to correct or cause other
problems. You can better understand the difficulty of detecting and correcting
errors by considering identity theft. Victims of identity theft face enormous chal-
lenges and frustration trying to correct data about them.
MANAGEMENT INFORMATION SYSTEMS
Functional areas or departments—accounting, finance, production/operations,
marketing and sales, human resources, and engineering and design—are supported
by ISs designed for their particular reporting needs. General-purpose reporting
systems are referred to as management information systems (MISs). Their objective is to provide reports to managers for tracking operations, monitoring, and control.
Typically, a functional system provides reports about such topics as operational
efficiency, effectiveness, and productivity by extracting information from databases
and processing it according to the needs of the user. Types of reports include the
following:
• Periodic: These reports are created or run according to a pre-set schedule. Examples are daily, weekly, and quarterly. Reports are easily distributed via
e-mail, blogs, internal websites (called intranets), or other electronic media. Periodic reports are also easily ignored if workers do nott find them worth
the time to review.
• Exception: Exception reports are generated only when something is outside the norm, either higher or lower than expected. Sales in hardware stores
prior to a hurricane may be much higher than the norm. Or sales of fresh
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2.3 Information Systems: The Basics 51
produce may drop during a food contamination crisis. Exception reports are
more likely to be read because workers know that some unusual event or
deviation has occurred.
• Ad hoc, or on demand: Ad hoc reports are unplanned reports. They are gen- erated to a mobile device or computer on demand as needed. They are gener- ated on request to learn more about a situation, problem, or opportunity.
Reports typically include interactive data visualizations, such as column and pie
charts, as shown in Figure 2.11.
Functional information systems that support business analysts and other
departmental employees can be fairly complex, depending on the type of employ-
ees supported. The following examples show the support that IT provides to major
functional areas.
1. Bolsa de Comercio de Santiago, a large stock exchange in Chile, processes high-volume trading in microseconds using IBM software. The stock exchange
increased its transaction capacity by 900 percent by 2011. The Chilean stock
exchange system can do the detective work of analyzing current and past
transactions and market information, learning and adapting to market trends
and connecting its traders to business information in real time. Immediate
throughput in combination with analytics allows traders to make more accu-
rate decisions.
2. According to the New England Journal of Medicine, 1 in 5 patients suffers from preventable readmissions, which cost taxpayers over $17 billion a year. Begin-
ning in 2012, hospitals have been penalized for high readmission rates with cuts
to the payments they receive from the government (Miliard, 2011). Using a DSS
and predictive analytics, the health-care industry can leverage unstructured in-
formation in ways not possible before, according to Charles J. Barnett, president/
CEO of Seton Health Care. “With this solution, we can access an integrated view
of relevant clinical and operational information to drive more informed decision
making. For example, by predicting which patients might be readmitted, we can
reduce costly and preventable readmissions, decrease mortality rates, and ulti-
mately improve the quality of life for our patients” (Miliard, 2011).
DECISION SUPPORT SYSTEMS
Decision support systems (DSSs) are interactive applications that support decision making. Configurations of a DSS range from relatively simple applications that
support a single user to complex enterprisewide systems. A DSS can support the
analysis and solution of a specific problem, evaluate a strategic opportunity, or sup-
port ongoing operations. These systems support unstructured and semistructured
decisions, such as make-or-buy-or-outsource decisions, or what products to develop
and introduce into existing markets.
Figure 2.11 Sample report produced by an MIS. ©
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52 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
Degree of Structure of Decisions
Decisions range from structured to unstructured. Structured decisions are those
that have a well-defined method for solving and the data necessary to reach a sound
decision. An example of a structured decision is determining whether an applicant
qualifies for an auto loan, or whether to extend credit to a new customer—and the
terms of those financing options. Structured decisions are relatively straightforward and made on a regular basis, and an IS can ensure that they are done consistently.
At the other end of the continuum are unstructured decisions that depend on human intelligence, knowledge, and/or experience—as well as data and models to
solve. Examples include deciding which new products to develop or which new mar-
kets to enter. Semistructured decisions fall in the middle of the continuum. DSSs
are best suited to support these types of decisions, but they are also used to support
unstructured ones. To provide such support, DSSs have certain characteristics to
support the decision maker and the overall decision-making process.
Three Defining DSS Characteristics
These characteristics of DSSs include:
1. An easy-to-use interactive interface
2. Models or formulas that enable sensitivity analysis, what-if analysis, goal seek- ing, and risk analysis
3. Data from multiple sources—internal and external sources plus data added by the decision maker who may have insights relevant to the decision situation
Having models is what distinguishes DSS from MIS. Some models are devel-
oped by end users through an interactive and iterative process. Decision makers can
manipulate models to conduct experiments and sensitivity analyses, for example,
what-if and goal seeking. What-if analysis refers to changing assumptions or data in the model to observe the impacts of those changes on the outcome. For example, if
sale forecasts are based on a 5 percent increase in customer demand, a what-if anal-
ysis would replace the 5 percent with higher and/or lower estimates to determine
what would happen to sales if demand changed. With goal seeking, the decision maker has a specific outcome in mind and needs to figure out how that outcome
could be achieved and whether it is feasible to achieve that desired outcome. A DSS
can also estimate the risk of alternative strategies or actions.
California Pizza Kitchen (CPK) uses a DSS to support inventory decisions.
CPK has 77 restaurants located in various states in the United States. Maintaining
optimal inventory levels at all restaurants was challenging and time-consuming. A
DSS was built to make it easy for the chain’s managers to maintain updated records
and make decisions. Many CPK restaurants increased sales by 5 percent after
implementing a DSS.
Building DSS Applications
Planners Lab is an example of software for building DSSs. The software is free to academic institutions and can be downloaded from plannerslab.com. Planners Lab
includes:
• An easy-to-use model-building language
• An easy-to-use option for visualizing model output, such as answers to what-if and goal-seeking questions, to analyze the impacts of different assumptions
These tools enable managers and analysts to build, review, and challenge the
assumptions upon which their decision scenarios are based. With Planners Lab,
decision makers can experiment and play with assumptions to assess multiple views
of the future.
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2.4 Data Centers, Cloud Computing, and Virtualization 53
DATABASE VOLATILITY AND DATA WAREHOUSING
Given the huge number of transactions, the data in databases are constantly in use
or being updated. This characteristic of databases—referred to as volatility—makes it impossible to use them for complex decision-making and problem-solving tasks.
For this reason, data are extracted from the database transformed (processed to
standardize the data), and then loaded into a data warehouse. As a result of the
extract, transformation, and load (ETL), operations data in the data warehouse are
better formatted for analyses.
ISS EXIST WITHIN A CULTURE
ISs do not exist in isolation. They have a purpose and a social (organizational)
context. A common purpose is to provide a solution to a business problem. The social context of the system consists of the values and beliefs that determine what is admissible and possible within the culture of the organization and among the
people involved. For example, a company may believe that superb customer service
and on-time delivery are critical success factors. This belief system influences IT
investments, among other factors.
The business value of IT is determined by the people who use them, the busi-
ness processes they support, and the culture of the organization. That is, IS value
is determined by the relationships among ISs, people, and business processes—all
of which are influenced strongly by organizational culture, as shown in Figure 2.12.
Figure 2.12 Organizational culture plays a signifi cant role in the use and benefi ts of Information systems.
Questions 1. Contrast data, information, and knowledge. 2. Defi ne TPS and give an example. 3. When is batch processing used? 4. When are real time processing capabilities needed? 5. Explain why TPSs need to process incoming data before they are
stored. 6. Defi ne MIS and DSS and give an example of each. 7. Why are databases inappropriate for doing data analysis?
On-premises data centers, virtualization, and cloud computing are types of IT infrastructures or computing systems. Long ago, there were few IT infrastructure options. Mostly, companies owned their servers, storage, and network com-
ponents to support their business applications and these computing resources
were on their premises. Now, there are several choices for an IT infrastructure
2.4 Data Centers, Cloud Computing, and Virtualization
Organizational Culture
Information Systems:
hardware, software, networks, and data
Business Processes
People
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54 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
strategy—including virtualization and cloud computing. As is common to IT
investments, each infrastructure configuration has strengths, weaknesses, and cost
considerations.
DATA CENTERS A data center consists of a large number of network servers (Figure 2.13) used for the storage, processing, management, distribution, and archiving of data, systems, Web
traffic, services, and enterprise applications. Data center also refers to the building or
facility that houses the servers and equipment. Here are some examples of data centers:
• National Climatic Data Center. The National Climatic Data Center is an example of a public data center that stores and manages the world’s largest
archive of weather data.
• U.S. National Security Agency. The National Security Agency’s (NSA) data center in Bluffdale, UT, shown in Figure 2.14, opened in the fall of 2013. It
is the largest spy data center for the NSA. People who think their corre-
spondence and postings through sites like Google, Facebook, and Apple are
safe from prying eyes should rethink that belief. You will read more about
reports exposing government data collection programs in Chapter 5.
• Apple. Apple has a 500,000-square-foot data center in Maiden, NC, that houses servers for various iCloud and iTunes services. The center plays
a vital role in the company’s back-end IT infrastructure. In 2014 Apple
expanded this center with a new, smaller 14,250 square-foot tactical data
center that also includes office space, meeting areas, and breakrooms.
Companies may own and manage their own on-premises data centers or pay
for the use of their vendors’ data centers, such as in cloud computing, virtualization,
and software, as service arrangements (Figure 2.15).
Figure 2.13 A row of network servers in data center.
Figure 2.14 The NSA data center (shown under construction) opened in the fall of 2013 in Bluffdale, UT. It is the largest spy data center for the NSA. People who believe their correspondence and postings through sites like Google, Facebook, and Apple are safe from prying eyes should think again.
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2.4 Data Centers, Cloud Computing, and Virtualization 55
Since only the company owns the infrastructure, a data center is more suitable
for organizations that run many different types of applications and have complex
workloads. A data center, like a factory, has limited capacity. Once it is built, the
amount of storage and the workload the center can handle does not change without
purchasing and installing more equipment.
When a Data Center Goes Down, so Does Business
Data center failures disrupt all operations regardless of who owns the data center.
Here are two examples.
• Uber. The startup company Uber experienced an hour-long outage in February 2014 that brought its car-hailing service to a halt across the coun-
try. The problem was caused by an outage at its vendor’s West Coast data
center. Uber users flooded social media sites with complaints about prob-
lems kicking off Uber’s app to summon a driver-for-hire.
• WhatsApp. WhatsApp also experienced a server outage in early 2014 that took the service offline for 2.5 hours. WhatsApp is a smartphone text-messaging
service that had been bought by Facebook for $19 billion. “Sorry we currently
experiencing server issues. We hope to be back up and recovered shortly,”
WhatsApp said in a message on Twitter that was retweeted more than 25,000
times in just a few hours. The company has grown rapidly to 450 million active
users within five years, nearly twice as many as Twitter. More than two-thirds of
these global users use the app daily. WhatsApp’s’ server failure drove millions
of users to a competitor. Line, a messaging app developed in Japan, added 2
million new registered users within 24 hours of WhatsApp’s outage—the big-
gest increase in Line’s user base within a 24-hour period.
These outages point to the risks of maintaining the complex and sophisticated
technology needed to power digital services used by millions or hundreds of mil-
lions of people.
Figure 2.15 Data centers are the infrastructure underlying cloud computing, virtualization, networking, security, delivery systems, and software as a service. Many of these issues are discussed in this chapter. ©
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INTEGRATING DATA TO COMBAT DATA CHAOS
An enterprise’s data are stored in many different or remote locations—creating
data chaos at times. And some data may be duplicated so that they are available
in multiple locations that need a quick response. Therefore, the data needed for
planning, decision making, operations, queries, and reporting are scattered or dupli-
cated across numerous servers, data centers, devices, and cloud services. Disparate
data must be unified or integrated in order for the organization to function.
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56 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
Unified Data Center
One solution is Cisco’s Unified Data Center (UDC). UDC can significantly speed
up the integration and consolidation of data and cut data center costs. UDC inte-
grates compute, storage, networking, virtualization, and management into a single
or unified platform. That platform provides an infrastructure that simplifies data
management and improves business agility or responsiveness. UDC can run appli-
cations more quickly in virtual and cloud computing environments.
Data Virtualization
Cisco provides data virtualization, which gives greater IT flexibility. Using virtual-
ization methods, enterprises can respond to change more quickly and make better
decisions in real time without physically moving their data, which significantly cuts
costs. Cisco Data Virtualization makes it possible to:
• Have instant access to data at any time and in any format.
• Respond faster to changing data analytics needs.
• Cut complexity and costs.
Compared to traditional (nonvirtual) data integration and replication methods,
Cisco Data Virtualization accelerates time to value with:
• Greater agility: speeds 5 to 10 times faster than traditional data integration methods
• Streamlined approach: 50 to 75 percent time savings over data replication and consolidation methods
• Better insight: instant access to data
Cisco offers videos on cloud computing, virtualization, and other IT infrastruc-
tures at its video portal at video.cisco.com.
CLOUD COMPUTING INCREASES AGILITY
In a business world where first movers gain the advantage, IT responsiveness and
agility provide a competitive edge. Yet, many IT infrastructures are extremely
expensive to manage and too complex to easily adapt. A common solution is cloud
computing. Cloud computing is the general term for infrastructures that use the Internet and private networks to access, share, and deliver computing resources.
The National Institute of Standards and Technology (NIST) more precisely defines
cloud computing as “a model for enabling convenient, on-demand network access to
a shared pool of configuration computing resources that can be rapidly provisioned
and released with minimal management effort or service provider interaction”
(NIST, 2012).
SELECTING A CLOUD VENDOR
Because cloud is still a relatively new and evolving business model, the decision to
select a cloud service provider should be approached with even greater diligence
than other IT decisions. As cloud computing becomes an increasingly important
part of the IT delivery model, assessing and selecting the right cloud provider also
become the most strategic decisions that business leaders undertake. Providers are
not created equally, so it is important to investigate each provider’s offerings prior to
subscribing. When selecting and investing in cloud services, there are several service
factors a vendor needs to address. These evaluation factors are listed in Table 2.5.
Vendor Management and Service-Level Agreements
The move to the cloud is also a move to vendor-managed services and cloud service- level agreements (SLAs). An SLA is a negotiated agreement between a company
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2.4 Data Centers, Cloud Computing, and Virtualization 57
and service provider that can be a legally binding contract or an informal contract.
You can review an example of the Google Apps SLA by visiting its website at
Google.com and searching for “SLA.” Staff experienced in managing outsourcing
projects may have the necessary expertise for managing work in the cloud and polic-
ing SLAs with vendors. The goal is not building the best SLA terms, but getting the
terms that are most meaningful to the business.
The Cloud Standards Customer Council published the Practical Guide to Cloud Service Level Agreements (2012), which brings together numerous customer experiences into a single guide for IT and business leaders who are considering
cloud adoption. According to this guide, an SLA serves:
as a means of formally documenting the service(s), performance expectations,
responsibilities and limits between cloud service providers and their users. A
typical SLA describes levels of service using various attributes such as: availability,
TABLE 2.5 Service Factors to Consider when Evaluating Cloud Vendors or Service Providers
Factors Examples of Questions to Be Addressed
Delays What are the estimated server delays and network
delays?
Workloads What is the volume of data and processing that can be
handled during a specifi c amount of time?
Costs What are the costs associated with workloads across
multiple cloud computing platforms?
Security How are data and networks secured against attacks?
Are data encrypted and how strong is the encryption?
What are network security practices?
Disaster recovery How is service outage defi ned? What level of
and business redundancy is in place to minimize outages, including
continuity backup services in different geographical regions? If a
natural disaster or outage occurs, how will cloud
services be continued?
Technical expertise Does the vendor have expertise in your industry or
and understanding business processes? Does the vendor understand what
you need to do and have the technical expertise to
fulfi ll those obligations?
Insurance in case Does the vendor provide cloud insurance to mitigate
of failure user losses in case of service failure or damage? This is
a new and important concept.
Third-party audit, or Can the vendor show objective proof with an audit
an unbiased assessment that it can live up to the promises it is making?
of the ability to rely on
the service provided by
the vendor
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58 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
serviceability, performance, operations, billing, and penalties associated with
violations of such attributes. (Cloud Standards Customer Council, 2012, pp. 5–6.)
Implementing an effective management process is an important step in ensur-
ing internal and external user satisfaction with cloud services.
CLOUD VS. DATA CENTER: WHAT IS THE DIFFERENCE?
A main difference between a cloud and data center is that a cloud is an off-premise
form of computing that stores data on the Internet. In contrast, a data center refers
to on-premises hardware and equipment that store data within an organization’s
local network. Cloud services are outsourced to a third-party cloud provider who
manages the updates, security, and ongoing maintenance. Data centers are typically
run by an in-house IT department.
Cloud computing is the delivery of computing and storage resources as a ser-
vice to end-users over a network. Cloud systems are scalable. That is, they can be adjusted to meet changes in business needs. At the extreme, the cloud’s capacity is
unlimited depending on the vendor’s offerings and service plans. A drawback of the
cloud is control because a third party manages it. Companies do not have as much
control as they do with a data center. And unless the company uses a private cloud within its network, it shares computing and storage resources with other cloud users
in the vendor’s public cloud. Public clouds allow multiple clients to access the same virtualized services and utilize the same pool of servers across a public network. In
contrast, private clouds are single-tenant environments with stronger security and
control for regulated industries and critical data. In effect, private clouds retain all
the IT security and control provided by traditional data center infrastructures with
the advantage of cloud computing.
Companies often use an arrangement of both on-premises data centers and
cloud computing (Figure 2.16).
A data center is physically connected to a local network, which makes it easier
to restrict access to apps and information to only authorized, company-approved
people and equipment. However, the cloud is accessible by anyone with the proper
credentials and Internet connection. This accessibility arrangement increases expo-
sure to company data at many more entry and exit points.
CLOUD INFRASTRUCTURE
The cloud has greatly expanded the options for enterprise IT infrastructures
because any device that accesses the Internet can access, share, and deliver data.
Cloud computing is a valuable infrastructure because it:
1. Provides a dynamic infrastructure that makes apps and computing power avail- able on demand. Apps and power are available on demand because they are
provided as a service. For example, any software that is provided on demand is referred to as software as a service, or SaaS. Typical SaaS products are Google Apps and Salesforce.com. Section 2.5 discussed SaaS and other cloud services.
2. Helps companies become more agile and responsive while signifi cantly reducing IT costs and complexity through improved workload optimization and service
delivery.
Move to Enterprise Clouds
A majority of large organizations have hundreds or thousands of software licenses
that support business processes, such as licenses for Microsoft Office, Oracle database
management, IBM CRM (customer relationship management), and various network
security software. Managing software and their licenses involves deploying, provi-
sioning, and updating them—all of which are time-consuming and expensive. Cloud
computing overcomes these problems.
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2.4 Data Centers, Cloud Computing, and Virtualization 59
Figure 2.16 Corporate IT infrastructures can consist of an on-premises data center and off-premises cloud computing.
ISSUES IN MOVING WORKLOADS FROM THE ENTERPRISE TO THE CLOUD
Building a cloud strategy is a challenge, and moving existing apps to the cloud is
stressful. Despite the business and technical benefits, the risk exists of disrupting
operations or customers in the process. With the cloud, the network and WAN
(wide area network) become an even more critical part of the IT infrastructure.
Greater network bandwidth is needed to support the increase in network traffic.
And, putting part of the IT architecture or workload into the cloud requires dif-
ferent management approaches, different IT skills, and knowing how to manage
vendor relationships and contracts.
Infrastructure Issues
There is a big difference because cloud computing runs on a shared infrastructure,
so the arrangement is less customized to a specific company’s requirements. A
comparison to help understand the challenges is that outsourcing is like renting an
apartment, while the cloud is like getting a room at a hotel.
With cloud computing, it may be more difficult to get to the root of perfor-
mance problems, like the unplanned outages that occurred with Google’s Gmail
and Workday’s human resources apps. The trade-off is cost vs. control.
Increasing demand for faster and more powerful computers, and increases in
the number and variety of applications are driving the need for more capable IT
architectures.
VIRTUALIZATION AND VIRTUAL MACHINES
Computer hardware had been designed to run a single operating system (OS) and
a single app, which leaves most computers vastly underutilized. Virtualization is a
technique that creates a virtual (i.e., nonphysical) layer and multiple virtual machines
(VMs) to run on a single physical machine. The virtual (or virtualization) layer makes
it possible for each VM to share the resources of the hardware. Figure 2.17 shows the
relationship among the VMs and physical hardware.
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60 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
What Is a Virtual Machine?
Just as virtual reality is not real, but a software-created world, a virtual machine is a software-created computer. Technically, a virtual machine (VM) is created by a software layer, called the virtualization layer, as shown in Figure 2.17. That layer has its own Windows or other OS and apps, such as Microsoft Office, as if it were
an actual physical computer. A VM behaves exactly like a physical computer and
contains its own virtual—that is, software-based—CPU, RAM (random access memory), hard drive, and network interface card (NIC). An OS cannot tell the dif-
ference between a VM and a physical machine, nor can apps or other computers
on a network tell the difference. Even the VM thinks it is a “real” computer. Users
can set up multiple real computers to function as a single PC through virtualization
to pool resources to create a more powerful VM.
Virtualization is a concept that has several meanings in IT and therefore sev- eral definitions. The major type of virtualization is hardware virtualization, which
remains popular and widely used. Virtualization is often a key part of an enter-
prise’s disaster recovery plan. In general, virtualization separates business applica-
tions and data from hardware resources. This separation allows companies to pool
hardware resources—rather than dedicate servers to applications—and assign those
resources to applications as needed.
The major types of virtualization are the following:
• Storage virtualization is the pooling of physical storage from multiple net- work storage devices into what appears to be a single storage device man-
aged from a central console.
• Network virtualization combines the available resources in a network by splitting the network load into manageable parts, each of which can be
assigned (or reassigned) to a particular server on the network.
• Hardware virtualization is the use of software to emulate hardware or a total computer environment other than the one the software is actually running
in. It allows a piece of hardware to run multiple operating system images at
once. This kind of software is sometimes known as a virtual machine.
Virtualization Characteristics and Benefits
Virtualization increases the flexibility of IT assets, allowing companies to consoli-
date IT infrastructure, reduce maintenance and administration costs, and prepare
for strategic IT initiatives. Virtualization is not primarily about cost-cutting, which
Figure 2.17 Virtual machines running on a simple computer hardware layer.
Application
Virtualization Layer
Hardware Layer
Operating
System
Application
Operating
System
Application
Operating
System
Virtual Machines
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2.4 Data Centers, Cloud Computing, and Virtualization 61
is a tactical reason. More importantly, for strategic reasons, virtualization is used
because it enables flexible sourcing and cloud computing.
The characteristics and benefits of virtualization are as follows:
1. Memory-intensive. VMs need a huge amount of RAM (random access memory, or primary memory) because of their massive processing requirements.
2. Energy-effi cient. Minimizes energy consumed running and cooling servers in the data center—representing up to a 95 percent reduction in energy use per
server.
3. Scalability and load balancing. When a big event happens, such as the Super Bowl, millions of people go to a website at the same time. Virtualization pro-
vides load balancing to handle the demand for requests to the site. The VMware
infrastructure automatically distributes the load across a cluster of physical serv-
ers to ensure the maximum performance of all running VMs. Load balancing is
key to solving many of today’s IT challenges.
Virtualization consolidates servers, which reduces the cost of servers, makes
more efficient use of data center space, and reduces energy consumption. All of
these factors reduce the total cost of ownership (TCO). Over a three-year life cycle,
a VM costs approximately 75 percent less to operate than a physical server.
Liberty Wines supplies to restaurants, supermarkets, and independent retailers from its headquarters in central London. Recipient of multiple international wine awards— including the International Wine Challenge on Trade Supplier of the Year for two years running—Liberty Wines is one of the United Kingdom’s foremost wine importers and distributors.
IT Problems and Business Needs
As the business expanded, the existing servers did not have the capacity to handle increased data volumes, and main- tenance of the system put a strain on the IT team of two employees. Existing systems were slow and could not pro- vide the responsiveness that employees expected.
Liberty Wines had to speed up business processes to meet the needs of customers in the fast-paced world of fine dining. To provide the service their customers expect, employees at Liberty Wines needed quick and easy access to customer, order, and stock information. In the past, the company relied on 10 physical servers for apps and services, such as order processing, reporting, and e-mail.
Virtualized Solution
Liberty Wines deployed a virtualized server solution incor- porating Windows Server 2008 R2. The 10 servers were
replaced with 3 physical servers, running 10 virtual servers. An additional server was used as part of a backup system, further improving resilience and stability.
By reducing the number of physical servers from 10 to 4, power use and air conditioning costs were cut by 60 percent. Not only was the bottom line improved, but the carbon footprint was also reduced, which was good for the environment.
The new IT infrastructure cut hardware replacement costs by £45,000 (U.S. $69,500) while enhancing stability with the backup system. Apps now run faster, too, so employees can provide better customer service with improved productivity. When needed, virtual servers can be added quickly and eas- ily to support business growth.
Questions
1. What business risks had Liberty Wines faced? 2. How does Liberty Wines’ IT infrastructure impact its com-
petitive advantage? 3. How did server virtualization benefit Liberty Wines and
the environment?
IT at Work 2 . 3 Business Continuity with Virtualization
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62 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
Managers want streamlined, real time data-driven enterprises, yet they may face
budget cuts. Sustaining performance requires the development of new business
apps and analytics capabilities, which comprise the front end—and the data stores and digital infrastructure, or back end, to support them. The back end is where the
data reside. The problem is that data may have to navigate through a congested
IT infrastructure that was first designed decades ago. These network or database
bottlenecks can quickly wipe out the competitive advantages from big data, mobility,
and so on. Traditional approaches to increasing database performance—manually tun-
ing databases, adding more disk space, and upgrading processors—are not enough
when you are have streaming data and real time big data analytics. Cloud services
help to overcome these limitations.
2.5 Cloud Services Add Agility
XAAS: “AS A SERVICE” MODELS
The cloud computing model for on-demand delivery of and access to various types
of computing resources also extends to the development of business apps. Figure 2.18
shows four “as a service” (XaaS) solutions based on the concept that the resource—
software, platform, infrastructure, or data–can be provided on demand regardless
of geolocation.
CLOUD COMPUTING STACK
Figure 2.19 shows the cloud computing stack, which consists of the following three categories:
• SaaS apps are designed for end-users.
• PaaS is a set of tools and services that make coding and deploying these apps faster and more efficient.
• IaaS consists of hardware and software that power computing resources— servers, storage, operating systems, and networks.
Questions 1. What is a data center? 2. Describe cloud computing. 3. What is the difference between data centers and cloud computing? 4. What are the benefi ts of cloud computing? 5. How can cloud computing solve the problems of managing software
licenses? 6. What is an SLA? Why are SLAs important? 7. What factors should be considered when selecting a cloud vendor or
provider? 8. When are private clouds used instead of public clouds? 9. Explain three issues that need to be addressed when moving to cloud
computing or services. 10. How does a virtual machine (VM) function? 11. Explain virtualization. 12. What are the characteristics and benefi ts of virtualization? 13. When is load balancing important?
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2.5 Cloud Services Add Agility 63
Software as a Service
Software as a service (SaaS) is a widely used model in which software is available to users as needed. Specifically, in SaaS, a service provider hosts the application at
its data center and customers access it via a standard Web browser. Other terms for
SaaS are on-demand computing and hosted services. The idea is basically the same: Instead of buying and installing expensive packaged enterprise applications, users
can access software apps over a network, with an Internet browser being the only
necessity.
A SaaS provider licenses an application to customers either on-demand,
through a subscription, based on usage (pay-as-you-go), or increasingly at no cost
when the opportunity exists to generate revenue from advertisements or through
other methods.
Figure 2.19 The cloud computing stack consists of SaaS, PaaS, and IaaS.
Figure 2.18 Four as-a-service solutions: software, platform, infrastructure, and data as a service. ©
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64 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
The SaaS model was developed to overcome the common challenge to an
enterprise of being able to meet fluctuating demands on IT resources efficiently.
It is used in many business functions, primarily customer relationship management
(CRM), accounting, human resources (HR), service desk management, and col-
laboration.
There are thousands of SaaS vendors. Salesforce.com is one of the most widely
known SaaS providers. Other examples are Google Docs and collaborative presen-
tation software Prezi. For instance, instead of installing Microsoft Word on your own computer, and then loading Word to create a document, you use a browser to
log into Google Docs. Only the browser uses your computer’s resources.
Platform as a Service
Platform as a service (PaaS) benefits software development. PaaS provides a stan- dard unified platform for app development, testing, and deployment. This comput-
ing platform allows the creation of Web applications quickly and easily without the
complexity of buying and maintaining the underlying infrastructure. Without PaaS,
the cost of developing some apps would be prohibitive. The trend is for PaaS to
be combined with IaaS. For an example of the value of SaaS and PaaS, see IT at
Work 2.4.
Within only 12 weeks, Unilever had its new digital social platform built and implemented. The platform was designed to support Unilever Global Marketing by connecting its mar- keters, brand managers, and partners in 190 countries. The new social platform is built on the Salesforce Platform and leverages Salesforce Chatter, which is an enterprise social
networking technology. It enables Unilever marketers to share knowledge, best practices, and creative assets across the net- work. According to Mark McClennon, the CIO Consumer at Unilever, “We’ve gone from a blank piece of paper all the way through to rolling out the first release of the platform in about three months using Salesforce technology” (Accenture, 2013).
IT at Work 2 . 4 Unilever
Infrastructure as a Service
Infrastructure as a service (IaaS) is a way of delivering cloud computing infrastruc- ture as an on-demand service. Rather than purchasing servers, software, data center
space, or networks, companies instead buy all computing resources as a fully out-
sourced service. IaaS providers are Amazon Web Services (AWS) and Rackspace.
Data as a Service
Similar to SaaS, PaaS, and IaaS, data as a service (DaaS) enables data to be shared among clouds, systems, apps, and so on regardless of the data source or
where they are stored. DaaS makes it easier for data architects to select data from
different pools, filter out sensitive data, and make the remaining data available
on demand.
A key benefit of DaaS is the elimination of the risks and burdens of data man-
agement to a third-party cloud provider. This model is growing in popularity as data
become more complex, difficult, and expensive to maintain.
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Key Terms 65
At-a-Service Models are Enterprisewide and Can Trigger Lawsuits
The various at-a-service models are used in various aspects of business. You will
read how these specific services, such as CRM and HR management, are being
used for operational and strategic purposes in later chapters. Companies are fre-
quently adopting software, platform, infrastructure, data management and starting
to embrace mobility as a service and big data as a service because they typically no longer have to worry about the costs of buying, maintaining, or updating their own
data servers. Both hardware and human resources expenses can be cut significantly.
Service arrangements all require that managers understand the benefits and trade-
offs—and how to negotiate effective SLAs. Regulations mandate that confidential
data be protected regardless of whether the data are on-premises on in the cloud.
Therefore, a company’s legal department needs to get involved in these IT deci-
sions. Put simply, moving to cloud services is not simply an IT decision because the
stakes around legal and compliance issues are very high.
GOING CLOUD Cloud services can advance the core business of delivering superior services to optimize business performance. Cloud can cut costs and add flexibility to the
performance of critical business apps. And, it can improve responsiveness to end-
consumers, application developers, and business organizations. But to achieve these
benefits, there must be IT, legal, and senior management oversight because a com-
pany still must meet its legal obligations and responsibilities to employees, customers,
investors, business partners, and society.
Questions 1. What is SaaS? 2. Describe the cloud computing stack. 3. What is PaaS? 4. What is IaaS? 5. Why is DaaS growing in popularity? 6. How might companies risk violating regulation or compliance requirements with
cloud services?
Key Terms
ad hoc report
batch processing
cloud computing
cloud computing stack
cross-sell
customer-centric
data
data as a service (DaaS)
data center
data governance
data silo
database
decision support system
(DSS)
dirty data
enterprise architecture (EA)
exception report
executive information
system (EIS)
goal seeking
information
information management
infrastructure as a service
(IaaS)
IT infrastructure
knowledge
management information
system (MIS)
master data
master data management
(MDM)
master fi le
model
online transaction
processing (OLTP)
platform as a service
(PaaS)
private cloud
public cloud
real time processing
service-level agreements
(SLAs)
software as a service
(SaaS)
structured decisions
transaction processing
system (TPS)
touchpoint
unstructured decisions
up-sell
virtualization
virtual machine (VM)
volatility
what-if analysis
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66 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
Assuring Your Learning
1. Why is a strong market position or good profi t performance only temporary?
2. Explain the difference between customer-centric and product-centric business models.
3. Assume you had:
a. A tall ladder with a sticker that listed a weight allowance only 5 pounds more than you
weighed. You know the manufacturer and
model number.
b. Perishable food with an expiration date 2 days into the future.
c. A checking account balance that indicated you had suffi cient funds to cover the balance due on
an account.
In all three cases, you cannot trust the data to be ex-
actly correct. The data could be incorrect by about 20
percent. How might you fi nd the correct data for each
instance? Which data might not be possible to verify?
How does dirty data impact your decision making?
4. If business data are scattered throughout the enter- prise and not synched until the end of the month,
how does that impact day-to-day decision making
and planning?
5. Assume a bank’s data are stored in silos based on fi nancial product—checking accounts, saving accounts,
mortgages, auto loans, and so on. What problems do
these data silos create for the bank’s managers?
6. Why do managers and workers still struggle to fi nd information that they need to make decisions or
take action despite advances in digital technology?
That is, what causes data defi ciencies?
7. According to a Tech CEO Council Report, Fortune 500 companies waste $480 billion every year on
ineffi cient business processes. What factors cause
such huge waste? How can this waste be reduced?
8. Explain why organizations need to implement enterprise architecture (EA) and data governance.
9. What two problems can EA solve?
10. Name two industries that depend on data gov- ernance to comply with regulations or reporting
requirements. Given an example of each.
11. Why is it important for data to be standardized? Given an example of unstandardized data.
12. Why are TPSs critical systems?
13. Explain what is meant by data volatility. How does it affect the use of databases for data analysis?
14. Discuss why the cloud acts as the great IT delivery frontier.
15. What are the immediate benefi ts of cloud computing?
16. What are the functions of data centers?
17. What factors need to be considered when selecting a cloud vendor?
18. What protection does an effective SLA provide?
19. Why is an SLA a legal document?
20. How can virtualization reduce IT costs while improving performance?
DISCUSS: Critical Thinking Questions
21. When selecting a cloud vendor to host your en- terprise data and apps, you need to evaluate the
service level agreement (SLA).
a. Research the SLAs of two cloud vendors, such as Rackspace, Amazon, or Google.
b. For the vendors you selected, what are the SLAs’ uptime percent? Expect them to be
99.9 percent or less.
c. Does each vendor count both scheduled down- time and planned downtime toward the SLA
uptime percent?
d. Compare the SLAs in terms of two other criteria.
e. Decide which SLA is better based on your com- parisons.
f. Report your results and explain your decision.
22. Many organizations initiate data governance pro- grams because of pressing compliance issues that
impact data usage. Organizations may need data
governance to be in compliance with one or more
regulations, such as the Gramm–Leach Bliley Act
(GLB), HIPAA, Foreign Corrupt Practices Act
(FCPA), Sarbanes–Oxley Act, and several state and
federal privacy laws.
a. Research and select two U.S. regulations or privacy laws.
b. Describe how data governance would help an enterprise comply with these regulations or
laws.
23. Visit eWeek.com Cloud Computing Solutions Center for news and reviews at eweek.com/c/s/
Cloud-Computing. Select one of the articles listed
EXPLORE: Online and Interactive Exercises
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CASE 2.2 Business Case 67
under Latest Cloud Computing News. Prepare an
executive summary of the article.
24. Visit Rackspace.com and review the company’s three types of cloud products. Describe each of
those cloud solutions.
25. Visit Oracle.com. Describe the types of virtualiza- tion services offered by Oracle.
26. Visit YouTube.com and search for two videos on virtualization. For each video, report what you
learned. Specify the complete URL, video title, who
uploaded the video and the date, video length, and
number of views.
27. Financial services fi rms experience large fl uctua- tions in business volumes because of the cyclical
nature of fi nancial markets. These fl uctuations
are often caused by crises—such as the subprime
mortgage problems, the discovery of major fraud,
or a slowdown in the economy. These fl uctuations
require that executives and IT leaders have the abil-
ity to cut spending levels in market downturns and
quickly scale up when business volumes rise again.
Research SaaS solutions and vendors for the fi nan-
cial services sector. Would investment in SaaS help
such fi rms align their IT capacity with their business
needs and also cut IT costs? Explain your answer.
28. Despite multimillion-dollar investments, many IT organizations cannot respond quickly to evolving
business needs. Also, they cannot adapt to large-
scale shifts like mergers, sudden drops in sales, or
new product introductions. Can cloud computing
help organizations improve their responsiveness
and get better control of their IT costs? Explain
your answer.
29. Describe the relationship between enterprise archi- tecture and organizational performance.
30. Identify four KPIs for a major airline (e.g., American, United, Delta) or an automobile manufacturer
(e.g., GM, Ford, BMW). Which KPI would be the
easiest to present to managers on an online dash-
board? Explain why.
ANALYZE & DECIDE: Apply IT Concepts to Business Decisions
C A S E 2 . 2 Business Case: Data Chaos Creates Risk
Data chaos often runs rampant in service organizations, such as health care and the government. For example, in many hospitals, each line of business, division, and department has implemented its own IT applications, often without a thorough analysis of its relationship with other departmental or divisional systems. This arrangement leads to the hospital having IT groups that specifi cally manage a particular type of applica- tion suite or data silo for a particular department or division.
Data Management When apps are not well managed, they can generate terabytes of irrelevant data, causing hospitals to drown in such data. This data chaos could lead to medical errors. In the effort to man- age excessive and massive amounts of data, there is increased risk of relevant information being lost (missing) or inaccurate— that is, faulty or dirty data. Another risk is data breaches.
• Faulty data: By 2016 an estimated 80 percent of health- care organizations will adopt electronic health records, or EHRs (IDC MarketScape, 2012). It is well known that an unintended consequence of EHR is faulty data. According to research done at Columbia University, data in EHR sys- tems may not be as accurate and complete as expected (Hripscak & Albers, 2012). Incorrect lab values, imaging results, or physician documentation lead to medical
errors, harm patients, and damage the organization’s ac- creditation and reputation.
• Data breaches: More than 25 million people have been affected by health-care system data breaches since the Offi ce for Civil Rights, a division of the U.S. Department of Health and Human Services, began reporting breaches in 2009. Most breaches involved lost or stolen data on laptops, removable drives, or other portable media. Breaches are extremely expensive and destroy trust.
Accountability in health care demands compliance with strong data governance efforts. Data governance programs verify that data input into EHR, clinical, fi nancial, and opera- tional systems are accurate and complete—and that only authorized edits can be made and logged.
Vanderbilt University Medical Center Adopts EHR and Data Governance Vanderbilt University Medical Center (VUMC) in Nashville, TN, was an early adopter of EHR and implemented data governance in 2009. VUMC’s experience provides valuable lessons. VUMC consists of three hospitals and the Vanderbilt Clinic, which have 918 beds, discharge 53,000 patients each year, and count 1.6 million clinic visits each year. On average, VUMC has an 83 percent occupancy rate and has achieved HIMSS Stage 6
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68 Chapter 2 Data Governance and IT Architecture Support Long-Term Performance
hospital EHR adoption. HIMSS (Healthcare Information and Management Systems Society, himss.org) is a global, nonprofi t organization dedicated to better health-care outcomes through IT. There are seven stages of EHR adoption, with Stage 7 be- ing a fully paperless environment. That means all clinical data are part of an electronic medical record and, as a result, can be shared across and outside the enterprise. At Stage 7, the health-care organization is getting full advantage of the health information exchange (HIE). HIE provides interoperability so that information can fl ow back and forth among physicians, patients, and health networks (Murphy, 2012). VUMC began collecting data as part of its EHR efforts in 1997. By 2009 the center needed stronger, more disciplined data management. At that time, hospital leaders initiated a project to build a data governance infrastructure.
Data Governance Implementation VUMC’s leadership team had several concerns.
1. IT investments and tools were evolving rapidly, but they were not governed by HIM (Healthcare Information and Management) policies.
2. As medical records became electronic so they might be transmitted and shared easily, they became more vulner- able to hacking.
3. As new uses of electronic information were emerging, the medical center struggled to keep up.
Health Record Executive Committee Initially, VUMC’s leaders assigned data governance to their tra- ditional medical records committee, but that approach failed. Next, they hired consultants to help develop a data governance structure and organized a health record executive committee to oversee the project. The committee reports to the medi- cal board and an executive committee to ensure executive involvement and sponsorship. The committee is responsible for developing the strategy for standardizing health record prac- tices, minimizing risk, and maintaining compliance. Members include the chief medical information offi cer (CMIO), CIO, legal counsel, medical staff, nursing informatics, HIM, administration, risk management, compliance, and accreditation. In addition, a legal medical records team was formed to support additions, corrections, and deletions to the EHR. This team defi nes pro- cedures for removal of duplicate medical record numbers and policies for data management and compliance.
Costs of Data Failure Data failures incur the following costs:
• Rework
• Loss of business
• Patient safety errors
• Malpractice lawsuits
• Delays in receiving payments because billing or medical codes data are not available
Measuring the Value of Data Governance One metric to calculate the value of a data governance program is confi dence in data-dependent assumptions, or CIDDA. CIDDA is computed by multiplying three confi dence estimates as follows:
CIDDA G M TS
where
G Confi dence that data are good enough for their intended purpose
M Confi dence that data mean what you think they do TS Confi dence that you know where the data come from
and trust the source
CIDDA is a subjective metric for which there are no industry benchmarks, yet it can be evaluated over time to gauge any improvement in data quality confi dence.
Benefi ts Achieved from Data Governance As in other industries, in health care, data are the most valu- able asset. The handling of data is the real risk. EHRs are effective only if the data are accurate and useful to support patient care. Effective ongoing data governance has achieved that goal at VUMC.
Sources: Compiled from Murphy (2012), HIMSS.org (2014), HIMSSanalytics.org (2014), Reeves & Bowen (2013).
Questions 1. What might happen when each line of business, division,
and department develops its own IT apps? 2. What are the consequences of poorly managed apps? 3. What two risks are posed by data chaos? Explain why. 4. What are the functions of data governance in the health-
care sector? 5. Why is it important to have executives involved in data
governance projects? 6. List and explain the costs of data failure. 7. Calculate the CIDDA over time:
Q1: G 40%, M 50%, TS 20% Q2: G 50%, M 55%, TS 30% Q3: G 60%, M 60%, TS 40% Q4: G 60%, M 70%, TS 45%
8. Why are data the most valuable asset in health care?
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References 69
C A S E 2 . 3 Video Case: Cloud Computing: Three Case Studies
When organizations say they are “using the cloud,” they can mean a number of very different things. Using an IaaS service such as Amazon EC2 or Terremark is different from using Google Apps for outsourced e-mail, which is different again from exposing an API in Facebook. A video shows three cloud computing case studies from Vordel’s customers. The cases cover SaaS, IaaS, and PaaS. In fi rst two examples, customers are connecting to the cloud: fi rst to Google Apps (for single sign-on to Google Apps e-mail) and second to Terremark to manage virtual
servers. In the third example, the connection is from the cloud using a Facebook app to a company’s APIs. You might spot Animal House references. Follow these three steps: Visit SOAtoTheCloud.com/2011/10/video-three-cloud- computing-case.html. View the 11-minute video of the three case studies.
Question 1. Explain the value or benefi ts of each organization’s cloud
investment.
Cloud Standards Customer Council. Practical Guide to Cloud Service Level Agreements. Version 1. April 10, 2012. http:// www.cloudstandardscustomercouncil.org/2012_Practical_
Guide_to_Cloud_SLAs.pdf
De Clerck, J. P. “Optimizing the Digital and Social Customer
Experience.” Social Marketing Forum, February 16, 2013.
Enterprise Architecture Research Forum (EARF). 2012.
First San Francisco Partners. “How McAfee Took Its First
Steps to MDM Success.” 2009.
GAO (General Accounting Offi ce). “Practical Guide to
Federal Enterprise Architecture.” Version 2. August 2010.
Hamilton, N. “Choosing Data Governance Battles.” Inside Reference Data, December 2013.
HIMSS.org (2014)
HIMSSanalytics.org (2014),
Hripscak, G. & D. J. Albers. “Next Generation Phenotyping of
Electronic Health Records.” Journal of the American Medical Informatics Association, Volume 19, Issue 5. September 2012.
IBM Institute for Business Value. “IBM Chief Information
Offi cer Study: The Essential CIO.” May 2011.
IDC MarketScape. “U.S. Ambulatory EMR/EHR for Small
Practices.” 3012 Vendor Assessment. May 2012.
Keitt, T.J. “Demystifying The Mobile Workforce–An Informa-
tion Workplace Report.” Forrester.com. June 7, 2011.
mcafee.com. McAfee Fact Sheet. 2013.
Miliard, M. “IBM Unveils New Watson-based Analytics.”
Healthcare IT News. October 25, 2011.
Murphy, K. “Health Information Exchange.” EHR Intelligence, April 9, 2012.
National Institute of Standards and Technology (NIST). Cloud
Computing Program. 2012.
Reeves, M. G. & R. Bowen. “Developing a Data Governance
Model in Health Care.” Healthcare Finance Management, February 2013.
Rich, R. “Master Data Management or Data Governance? Yes,
Please.” Teradata Magazine Q3, 2013.
Tech CEO Council Report 2010. techceocouncil.org/news/ reports/
References
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70
Chapter Snapshot
Analytics differentiates business in the 21st century.
Transactional, social, mobile, cloud, web, and sensor
data offer enormous potential. But without tools to ana-
lyze these data types and volumes, there would not be
much difference between business in the 20th century
and business today—except for mobile access. High-
quality data and human expertise are essential to the
value of analytics (Figure 3.1).
Human expertise is necessary because analytics alone
cannot explain the reasons for trends or relationships;
1. Describe the functions of database and data warehouse technologies, the differences between centralized and distributed database architecture, how data quality impacts performance, and the role of a master reference file in cre- ating accurate and consistent data across the enterprise.
2. Evaluate the tactical and strategic benefits of big data and analytics.
3. Describe data and text mining, and give examples of mining applications to find patterns, correlations, trends,
or other meaningful relationships in organizational data stores.
4. Explain the operational benefits and competitive advan- tages of business intelligence, and how forecasting can be improved.
5. Describe electronic records management and how it helps companies meet their compliance, regulatory, and legal obligations.
Chapter Snapshot Case 3.1 Opening Case: Coca-Cola Manages at the Point That Makes a Difference
3.1 Database Management Systems 3.2 Data Warehouse and Big Data Analytics 3.3 Data and Text Mining 3.4 Business Intelligence 3.5 Electronic Records Management
Key Terms
Assuring Your Learning
• Discuss: Critical Thinking Questions • Explore: Online and Interactive Exercises • Analyze & Decide: Apply IT Concepts
to Business Decisions
Case 3.2 Business Case: Financial Intelligence Fights Fraud
Case 3.3 Video Case: Hertz Finds Gold in Integrated Data
References
Learning Outcomes
Data Management, Big Data Analytics, and Records Management3
Chapter
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71
know what action to take; or provide sufficient context
to determine what the numbers represent and how to
interpret them.
Database, data warehouse, big data, and business
intelligence (BI) technologies interact to create a new
biz-tech ecosystem. Big data analytics and BI discover
insights or relationships of interest that otherwise might
not have been recognized. They make it possible for
managers to make decisions and act with clarity, speed,
and confidence. Big data analytics is not just about man-
aging more or varied data. Rather, it is about asking new
questions, formulating new hypotheses, exploration and
discovery, and making data-driven decisions. Ultimately,
a big part of big data analytic efforts is the use of new
analytics techniques.
Mining data or text taken from day-to-day busi-
ness operations reveals valuable information, such as
customers’ desires, products that are most important, or
processes that can be made more efficient. These insights
expand the ability to take advantage of opportunities,
minimize risks, and control costs.
While you might think that physical pieces of paper
are a relic of the past, in most offices the opposite is
true. Aberdeen Group’s survey of 176 organizations
worldwide found that the volume of physical documents
is growing by up to 30 percent per year. Document man-
agement technology archives digital and physical data
to meet business needs, as well as regulatory and legal
requirements (Rowe, 2012).
C A S E 3 . 1 O P E N I N G C A S E Coca-Cola Manages at the Point That Makes a Difference
COCA-COLA’S DATA MANAGEMENT CHALLENGES
The Coca-Cola Company is a Fortune 100 company with over $48 billion in sales
revenue and $9 billion in profit (Figure 3.2). The market leader manages and
analyzes several petabytes (Pb) of data generated or collected from more than 500 brands and consumers in 206 countries. Its bottling partners provide sales and
shipment data, while retail customers transmit transaction and merchandising data.
Other data sources are listed in Table 3.1. From 2003 to spring 2013, data analysts
at Coca-Cola knew there were BI opportunities in the mountains of data its bottlers
were storing, but finding and accessing all of that data for analytics proved to be
nearly impossible. The disparate data sources caused long delays in getting analytics
reports from IT to sales teams. The company decided to replace the legacy software
at each bottling facility and standardize them on a new BI system, a combination of
MicroStrategy and Microsoft BI products.
Enterprise Data Management Like most global companies, Coca-Cola relies on sophisticated enterprise data management, BI, and analytic technologies to sus-
tain its performance in fi ercely competitive markets (Figure 3.3). Data are managed
Petabyte (Pb) 1,000 Terabytes (Tb) 1 million Gigabytes (Gb).
Human
expertise
Data
analytics
High-Quality
data
Trends or
relationships
Context to understand
what the numbers
represent and how
to interpret them
What action to take
+
+ Figure 3.1 Data analytics, and human expertise and high-quality data, are needed to obtain actionable information.
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72 Chapter 3 Data Management, Big Data Analytics, and Records Management
in a centralized database, as illustrated in Figure 3.4. Data warehousing, big data, analytics, data modeling, and social media are used to respond to competitors’ activ-
ity, market changes, and consumer preferences.
To support its business strategy and operations, Coca-Cola changed from a
decentralized database approach to a centralized database approach. Now its data
are combined centrally and accessible via shared platforms across the organization
(Figure 3.5). Key objectives of the data management strategy are to help its retail
customers such as Walmart, which sells $4 billion of Coca-Cola products annually, sell
Figure 3.3 Coca-Cola World Headquarters in Atlanta, GA, announced on January 25, 2010, that new packaging material for plastic bottles will be made partially from plants—as part of its sustainability efforts.
Centralized database stores data at a single location that is accessible from anywhere. Searches can be fast because the search engine does not need to check multiple dis- tributed locations to find responsive data.
Data warehouses that inte- grate data from databases across an entire enterprise are called enterprise data warehouses (EDW).
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World’s largest nonalcoholic
beverage company with more
than 500 brands of beverages,
ready-to-drink coffees, juices, and
juice drinks.
Has the world’s largest beverage
distribution system, with
consumers in more than 200
countries.
Products consumed at a rate of
1.9 billion servings a day
worldwide.
Brand
Business Ethics & Sustainability
Focused on initiatives that reduce their
environmental footprint; support
active, healthy living; create a safe
work environment; and enhance the
economic development of the
communities where they operate.
Digital Technology Centralized database
Enterprise data warehouse (EDW)
Big data analytics
Decision models
70 million Facebook followers
The Coca-Cola Company
Figure 3.2 The Coca-Cola Company overview.
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CASE 3.1 Opening Case 73
TABLE 3.1 Opening Case Overview
Company • The Coca-Cola Company, coca-cola.com • Sustainability: www.coca-colacompany.com/sustainability
• $48 billion in sales revenue and profi ts of $9 billion, 2013
Industry • The global company manufactures, sells, and distributes nonalcoholic beverages.
Product lines • More than 500 brands of still and sparkling beverages, ready-to-drink coffees, juices, and juice drinks.
Digital technology • Enterprise data warehouse (EDW) • Big data and analytics
• Business intelligence
• In 2014, moved from a decentralized approach to a central-
ized approach, where the data are combined centrally and
available via the shared platforms across the organization.
Business challenges • In 2010, Coca-Cola had 74 unique databases, many of them used different software to store and analyze data. Dealing
with incompatible databases and reporting systems re-
mained a problem from 2003 to 2013.
• Chief Big Data Insights Offi cer Esat Sezer has stated that
Coca-Cola took a strategic approach instead of a tactical
approach with big data.
Global data sources • Transaction and merchandising data • Data from nationwide network of 74 bottlers
• Multichannel retail data
• Customer profi le data from loyalty programs • Social media data
• Supply chain data
• Competitor data
• Sales and shipment data from bottling partners
Figure 3.4 Centralized data architecture.
more Coca-Cola products and to improve the consumer experience. The company
has implemented a data governance program to ensure that cultural data sensitivi-
ties are respected.
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74 Chapter 3 Data Management, Big Data Analytics, and Records Management
SUSTAINING BUSINESS PERFORMANCE
All data are standardized through a series of master data management (MDM)
processes, as discussed in Chapter 2. An enterprise data warehouse (EDW) gener-
ates a single view of all multichannel retail data. The EDW creates a trusted view
of customers, sales, and transactions, enabling Coca-Cola to respond quickly and
accurately to changes in market conditions.
Throughout Coca-Cola’s divisions and departments, huge volumes of data
are analyzed to make more and better time-sensitive, critical decisions about
products, shopper marketing, the supply chain, and production. Point-of-sale
(POS) data are captured from retail channels and used to create customer pro-
files. Those profiles are communicated via a centralized iPad reporting system.
POS data are analyzed to support collaborative planning, forecasting, and
replenishment processes within its supply chain. (Supply chain management,
collaborative planning, forecasting, and replenishment are covered in greater
detail in Chapter 10.)
Coca-Cola’s Approach to Big Data and Decision Models Big data are treated as a strategic asset. Chief Big Data Insights Offi cer Esat Sezer has stated
that Coca-Cola takes a strategic approach instead of a tactical approach with big
data. The company is far advanced in the use of big data to manage its products,
sales revenue, and customer experiences in near real time and to reduce costs. For
example, it cut overtime costs almost in half by analyzing the service center data.
Big data help Coca-Cola relate to its 70 million Facebook followers—many of them
bolster the Coke brand.
Figure 3.5 Data from online and offl ine transactions are stored in databases. Data about entities such as customers, products, orders, and employees are stored in an organized way.
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3.1 Database Management Systems 75
Big data play a key role in ensuring that its orange juice tastes the same year-
round and is readily available anywhere in the world. Oranges used by Coca-Cola
have a peak growing season of only three months. Producing orange juice with a
consistent taste year-round despite the inconsistent quality of the orange supply is
complex. To deal with the complexity, an orange juice decision model was devel-
oped, the Black Book model. A decision model quantifies the relationship between variables, which reduces uncertainty. Black Book combines detailed data on the
600 flavors that make up an orange, weather, customer preferences, expected
crop yields, cost pressures, regional consumer preferences, and acidity or sweet-
ness rate. The model specifies how to blend the orange juice to create a consistent
taste. Coke’s Black Book juice model is considered to be one of the most complex
business analytics apps. It requires analyzing up to 1 quintillion (10E18) decision
variables to consistently deliver the optimal blend.
With the power of big data and decision models, Coca-Cola is prepared for
disruptions in supply far in advance. According to Doug Bippert, Coca-Cola’s vice
president of business acceleration, “If we have a hurricane or a freeze, we can
quickly re-plan the business in five or 10 minutes just because we’ve mathematically
modeled it” (BusinessIntelligence.com, 2013b).
Sources: Compiled from Burns (2013), Fernandez (2012), BusinessIntelligence.com (2013a, 2013b), CNNMoney (2014), Big Data Startups (2013), and Teradata (2012).
Questions 1. Why does the Coca-Cola Company have petabytes of data? 2. Why is it important for Coca-Cola to be able to process POS data in
near real time? 3. How does Coca-Cola attempt to create favorable customer
experiences? 4. What is the importance of having a trusted view of the data? 5. What is the benefi t of a decision model? 6. What is the Black Book model? 7. Explain the strategic benefi t of the Black Book model.
Data are the driving force behind any successful business. Operations, plan-
ning, control, and all other management functions rely largely on processed
information, not raw data. And, no one wants to wait for business-critical
reports or specific answers to their questions. Data management technologies
that keep users informed and support the various business demands are the
following:
• Databases store data generated by business apps, sensors, operations, and transaction-processing systems (TPS). Data in databases are extremely vola- tile. Medium and large enterprises typically have many databases of various types.
• Data warehouses integrate data from multiple databases and data silos, and organize them for complex analysis, knowledge discovery, and to support
3.1 Database Management Systems
Databases are collections of data sets or records stored in a systematic way.
Volatile refers to data that change frequently.
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76 Chapter 3 Data Management, Big Data Analytics, and Records Management
DATABASE MANAGEMENT SYSTEMS AND SQL
Database management systems (DBMSs) integrate with data collection systems such as TPS and business applications; store the data in an organized way; and
provide facilities for accessing and managing that data. Over the past 25 years, the
relational database has been the standard database model adopted by most enter- prises. Relational databases store data in tables consisting of columns and rows,
similar to the format of a spreadsheet, as shown in Figure 3.6.
Relational management systems (RDBMSs) provide access to data using a declarative language—structured query language (SQL). Declarative languages simplify data access by requiring that users only specify what data they want to
access without defining how access will be achieved. The format of a basic SQL
statement is
SELECT column_name(s)
FROM table_name
WHERE condition
An instance of SQL is shown in Figure 3.7.
Database management systems (DBMSs) are software used to manage the additions, updates, and dele- tions of data as transactions occur, and to support data queries and reporting. They are OLTP systems.
SQL is a standardized query language for accessing databases.
decision making. For example, data are extracted from a database, processed
to standardize their format, and then loaded into data warehouses at specific
times, such as weekly. As such, data in data warehouses are nonvolatile—and
ready for analysis.
• Data marts are small-scale data warehouses that support a single function or one department. Enterprises that cannot afford to invest in data warehous-
ing may start with one or more data marts.
• Business intelligence (BI) tools and techniques process data and do statisti- cal analysis for insight and discovery—that is, to discover meaningful rela-
tionships in the data, keep informed in real time, detect trends, and identify
opportunities and risks.
Data-processing techniques, processing power, and enterprise performance
management capabilities have undergone revolutionary advances in recent years
for reasons you are already familiar with—big data, mobility, and cloud comput-
ing. The last decade, however, has seen the emergence of new approaches, first in
data warehousing and, more recently, for transaction processing, as you read in this
chapter.
Figure 3.6 Illustration of structured data format. Numeric and alphanumeric data are arranged into rows and predefi ned columns similar to those in an Excel spreadsheet. ©
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3.1 Database Management Systems 77
DBMS Functions
An accurate and consistent view of data throughout the enterprise is needed so
one can make informed, actionable decisions that support the business strategy.
Functions performed by a DBMS to help create such a view are:
• Data filtering and profiling: Process and store data efficiently. Inspect the data for errors, inconsistencies, redundancies, and incomplete
information.
• Data integrity and maintenance: Correct, standardize, and verify the consis- tency and integrity of the data.
• Data synchronization: Integrate, match, or link data from disparate sources.
• Data security: Check and control data integrity over time.
• Data access: Provide authorized access to data in both planned and ad hoc ways within acceptable time.
Today’s computing hardware is capable of crunching through huge datasets
that were impossible to manage a few years back and making them available on-
demand via wired or wireless networks (Figure 3.8).
Figure 3.7 An instance of SQL to access employee information based on date of hire.
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TECH NOTE 3.1 Factors That Determine the Performance of a DBMS
Factors to consider when evaluating the performance of a database are the following.
Data latency. Latency is the elapsed time (or delay) between when data are created and when they are available for a query or report. Applications have different toler- ances for latency. Database systems tend to have shorter latency than data ware-
houses. Short latency imposes more restrictions on a system.
Queries are ad hoc (unplanned) user requests for specific data.
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78 Chapter 3 Data Management, Big Data Analytics, and Records Management
Figure 3.8 Database queries are processed in real time (a), and results are transmitted via wired or wireless networks to computer screens or handhelds (b).
(a) (b)
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Ability to handle the volatility of the data. The database has the processing power to handle the volatility of the data. The rates at which data are added, updated, or
deleted determine the workload that the database must be able to control to prevent
problems with the response rate to queries.
Query response time. The volume of data impacts response times to queries and data explorations. Many databases pre-stage data—that is, summarize or precalcu- late results—so queries have faster response rates.
Data consistency. Immediate consistency means that as soon as data are updated, responses to any new query will return the updated value. With eventual consistency, not all query responses will refl ect data changes uniformly. Inconsistent query results
could cause serious problems for analyses that depend on accurate data.
Query predictability. The greater the number of ad hoc or unpredictable queries, the more fl exible the database needs to be. Database or query performance manage-
ment is more diffi cult when the workloads are so unpredictable that they cannot be
prepared for in advance. The ability to handle the workload is the most important
criterion when choosing a database.
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3.1 Database Management Systems 79
Online Transaction Processing and Online Analytics Processing
When most business transactions occur—for instance, an item is sold or returned,
an order is sent or cancelled, a payment or deposit is made—changes are made
immediately to the database. These online changes are additions, updates, or
deletions. DBMSs record and process transactions in the database, and support
queries and reporting. Given their functions, DBMSs are referred to as online transaction-processing (OLTP) systems. OLTP is a database design that breaks down complex information into simpler data tables to strike a balance between
transaction-processing efficiency and query efficiency. OLTP databases process
millions of transactions per second. However, databases cannot be optimized for
data mining, complex online analytics-processing (OLAP) systems, and decision support. These limitations led to the introduction of data warehouse technology.
Data warehouses and data marts are optimized for OLAP, data mining, BI, and
decision support. OLAP is a term used to describe the analysis of complex data
from the data warehouse. In summary, databases are optimized for extremely fast
transaction processing and query processing. Data warehouses are optimized for
analysis.
DBMS AND DATA WAREHOUSING VENDORS RESPOND TO LATEST DATA DEMANDS
One of the major drivers of change in the data management market is the
increased amount of data to be managed. Enterprises need powerful DBMSs and
data warehousing solutions, analytics, and reporting. The four vendors that domi-
nate this market—Oracle, IBM, Microsoft, and Teradata—continue to respond
to evolving data management needs with more intelligent and advanced software
and hardware. Advanced hardware technology enables scaling to much higher
data volumes and workloads than previously possible, or it can handle specific
workloads. Older general-purpose relational databases DBMSs lack the scal-
ability or flexibility for specialized or very large workloads, but are very good at
what they do.
DBMS Vendor Rankings
The highest-ranking enterprise DBMSs in mid-2014 were Oracle’s MySQL,
Microsoft’s SQL Server, PostgreSQL, IBM’s DB2, and Teradata Database. Most
run on multiple operating systems (OSs).
• MySQL, which was acquired by Oracle in January 2010, powers hundreds of
thousands of commercial websites and a huge number of internal enterprise
applications.
• SQL Server’s ease of use, availability, and Windows operating system inte-
gration make it an easy choice for firms that choose Microsoft products for
their enterprises.
• PostgreSQL is the most advanced open source database, often used by
online gaming applications and Skype, Yahoo!, and MySpace.
• DB2 is widely used in data centers and runs on Linux, UNIX, Windows, and
mainframes.
Trend Toward NoSQL Systems
RDBMSs are still the dominant database engines, but the trend toward NoSQL (short for “not only SQL”) systems is clear. NoSQL systems increased in popu-
larity by 50 percent from 2013 to 2014. Although NoSQL have existed for as
Online transaction processing (OLTP) systems are designed to manage transaction data, which are volatile.
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long as relational DBMS, the term itself was not introduced until 2009. That
was when many new systems were developed in order to cope with the unfold-
ing requirements for DBMS—namely, handling big data, scalability, and fault
tolerance for large Web applications. Scalability means the system can increase in size to handle data growth or the load of an increasing number of concurrent
users. To put it differently, scalable systems efficiently meet the demands of high-
performance computing. Fault tolerance means that no single failure results in any loss of service.
NoSQL systems are such a heterogeneous group of database systems that
attempts to classify them are not very helpful. However, their general advantages
are these:
• Higher performance
• Easy distribution of data on different nodes, which enables scalability and
fault tolerance
• Greater flexibility
• Simpler administration
Starting in 2010 and continuing through 2014, Microsoft has been working on
the first rewrite of SQL Server’s query execution since Version 7 was released in
1998. The goal is to offer NoSQL-like speeds without sacrificing the capabilities of
a relational database.
With most NoSQL offerings, the bulk of the cost does not lie in acquiring the
database, but rather in implementing it. Data need to be selected and migrated
(moved) to the new database. Microsoft hopes to reduce these costs by offering
migration solutions.
CENTRALIZED AND DISTRIBUTED DATABASE ARCHITECTURE
Databases are centralized or distributed, as shown in Figure 3.9. Both types of data-
bases need one or more backups and should be archived onsite and offsite in case
of a crash or security incident.
Centralized Database Architecture
A centralized database stores all related files in a central location—as you read in
the opening Coca-Cola case. For decades the main database platform consisted of
centralized database files on massive mainframe computers. Benefits of centralized
database configurations include:
1. Better control of data quality. Data consistency is easier when data are kept in one physical location because data additions, updates, and deletions can be
made in a supervised and orderly fashion.
2. Better IT security. Data are accessed via the centralized host computer, where they can be protected more easily from unauthorized access or modifi cation.
A major disadvantage of centralized databases, like all centralized systems,
is transmission delay when users are geodispersed. More powerful hardware and
networks compensate for this disadvantage.
Distributed Database Architecture
A distributed database system allows apps on computers and mobiles to access data from both local and remote databases, as diagrammed in Figure 3.10. Distributed
databases use client/server architecture to process information requests. Computers
and mobile devices accessing the servers are called clients. The databases are stored
on servers that reside in the company’s data centers, a private cloud, or a public cloud.
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3.1 Database Management Systems 81
Users
Los Angeles
Users
New York
Users
Kansas City
Users
Chicago
New York
Users
Los Angeles
Los Angeles
Users
Kansas City
Kansas City
Central Location
Central
Location
New York
Users
New York
(a)
(b)
New York
Users
Chicago
Chicago
Figure 3.9 Comparison of centralized and distributed databases.
GARBAGE IN, GARBAGE OUT
Data collection is a highly complex process that can create problems concerning
the quality of the data being collected. Therefore, regardless of how the data are
collected, they need to be validated so users know they can trust them. Classic
expressions that sum up the situation are “garbage in, garbage out” (GIGO) and
the potentially riskier “garbage in, gospel out.” In the latter case, poor-quality
data are trusted and used as the basis for planning. You have encountered data
safeguards, such as integrity checks, to help improve data quality when you fill in
an online form. For example, the form will not accept an e-mail address that is not
formatted correctly.
Dirty Data Costs and Consequences
Dirty data—that is, poor-quality data—lack integrity and cannot be trusted.
Too often managers and information workers are actually constrained by data
that cannot be trusted because they are incomplete, out of context, outdated,
inaccurate, inaccessible, or so overwhelming that they require weeks to analyze.
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82 Chapter 3 Data Management, Big Data Analytics, and Records Management
In such situations, the decision maker is facing too much uncertainty to make
intelligent business decisions. The cost of poor-quality data may be expressed
as a formula:
Cost to
Correct
Errors
Cost to
Prevent
Errors
Lost
Business
Cost of Poor-
Quality Data
Examples of these costs include:
• Lost business. Business is lost when sales opportunities are missed, orders are returned because wrong items were delivered, or errors frustrate and
drive away customers.
• Time spent preventing errors. If data cannot be trusted, then employees need to spend more time and effort trying to verify information in order to
avoid mistakes.
• Time spent correcting errors. Database staff need to process corrections to the database. For example, the costs of correcting errors at Urent
Corporation are estimated as follows:
a) Two database staff members spend 25 percent of their workday process- ing and verifying data corrections each day:
2 people * 25% of 8 hours/day 4 hours/day correcting errors
b) Hourly salaries are $50 per hour based on pay rate and benefits:
$50/hour * 4 hours/day $200/day correcting errors
c) 250 workdays per year:
$200/day * 250 days $50,000/year to correct errors
The costs of poor-quality data spread throughout a company, affecting
systems from shipping and receiving to accounting and customer service. Data
Distributed databases on servers
Manufacturing
Manufacturing clients
Headquarter clients
Headquarters
Sales & Marketing
Sales & marketing clients
Figure 3.10 Distributed database architecture for headquarters, manufacturing, and sales and marketing.
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3.1 Database Management Systems 83
errors typically arise from the functions or departments that generate or create
the data—and not within the IT department. When all costs are considered, the
value of finding and fixing the causes of data errors becomes clear. In a time of
decreased budgets, some organizations may not have the resources for such proj-
ects and may not even be aware of the problem. Others may be spending most of
their time fixing problems, thus leaving them with no time to work on preventing
them.
Bad data are costing U.S. businesses hundreds of billions of dollars a year
and affecting their ability to ride out the tough economic climate. Incorrect
and outdated values, missing data, and inconsistent data formats can cause lost
customers, sales, and revenue; misallocation of resources; and flawed pricing
strategies.
For a particular company, it is difficult to calculate the full cost of poor data
quality and its long-term effects. Part of the difficulty is the time delay between the
mistake and when it is detected. Errors can be very difficult to correct, especially
when systems extend across the enterprise. Another concern is that the impacts of
errors can be unpredictable or serious. For example, the cost of errors due to unre-
liable and incorrect data alone is estimated to be as high as $40 billion annually in
the retail sector (Zynapse, 2010). And, one health-care company whose agents were
working with multiple ISs, but were not updating client details in every IS, saw its
annual expenses increase by $9 million.
Data Ownership and Organizational Politics
Despite the need for high-quality data, organizational politics and technical issues
make that difficult to achieve. The source of the problem is data ownership—that
is, who owns or is responsible for the data. Data ownership problems exist when
there are no policies defining responsibility and accountability for managing data.
Inconsistent data formats of various departments create an additional set of prob-
lems as organizations try to combine individual applications into integrated enter-
prise systems.
The tendency to delegate data-quality responsibilities to the technical teams
who have no control over data quality, as opposed to business users who do have
such control, is another common pitfall that stands in the way of accumulating high-
quality data.
Those who manage a business or part of a business are tasked with trying
to improve business performance and retain customers. Compensation is tied to
improving profitability, driving revenue growth, and improving the quality of cus-
tomer service. These key performance indicators (KPIs) are monitored closely by
senior managers who want to find and eliminate defects that harm performance. It
is strange then that so few managers take the time to understand how performance
is impacted by poor-quality data. Two examples make a strong case for investment
in high-quality data.
Retail banks: For retail bank executives, risk management is the number- one issue. Disregard for risk contributed to the 2008 financial services meltdown.
Despite risk management strategies, many banks still incur huge losses. Part of the
problem in many banks is that their ISs enable them to monitor risk only at the
product level—mortgages, loans, or credit cards. Product-level risk management
ISs monitor a customer’s risk exposure for mortgages, or for loans, or for credit
cards, and so forth—but not for a customer for all products. With product-level ISs,
a bank cannot see the full risk exposure of a customer. The limitations of these siloed
product-level risks have serious implications for business performance because
bad-risk customers cannot be identified easily, and customer data in the various ISs
may differ. For example, consider what happens when each product-level risk man-
agement IS feeds data to marketing ISs. Marketing may offer bad-risk customers
incentives to take out another credit card or loan that they cannot repay. And since
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the bank cannot identify its best customers either, they may be ignored and enticed
away by better deals offered by competitors. This scenario illustrates how data own-
ership and data-quality management are critical to risk management. Data defects
and incomplete data can quickly trigger inaccurate marketing and mounting losses.
One retail bank facing these problems lost 16 percent of its mortgage business
within 18 months while losses in its credit card business increased (Ferguson, 2012).
Manufacturing. Many manufacturers are at the mercy of a powerful customer base—large retailers. Manufacturers want to align their processes with those of
large retail customers to keep them happy. This alignment makes it possible for
a retailer to order centrally for all stores or to order locally from a specific manu-
facturer. Supporting both central and local ordering makes it difficult to plan pro-
duction runs. For example, each manufacturing site has to collect order data from
central ordering and local ordering systems to get a complete picture of what to
manufacture at each site. Without accurate, up-to-date data, orders may go unfilled,
or manufacturers may have excess inventory. One manufacturer who tried to keep
its key retailer happy by implementing central and local ordering could not process
orders correctly at each manufacturing site. No data ownership and lack of control
over how order data flowed throughout business operations had negative impacts.
Conflicting and duplicate business processes at each manufacturing site caused data
errors, leading to mistakes in manufacturing, packing, and shipments. Customers
were very dissatisfied.
These two examples represent the consequences of a lack of data ownership
and data quality. Understanding the impact of mismanaged data makes data owner-
ship and accurate data a higher priority.
Compliance with numerous federal and state regulations relies on rock-solid
data and trusted metrics used for regulatory reporting. Data ownership, data quality,
and formally managed data are high on the agenda of CFOs and CEOs who are held
personally accountable if their company is found to be in violation of regulations.
DATA LIFE CYCLE AND DATA PRINCIPLES
The data life cycle is a model that illustrates the way data travel through an organi-
zation, as shown in Figure 3.11. The data life cycle begins with storage in a database,
to being loaded into a data warehouse for analysis, then reported to knowledge
workers or used in business apps. Supply chain management (SCM), customer
relationship management (CRM), and e-commerce are enterprise applications that
require up-to-date, readily accessible data to function properly.
Three general data principles relate to the data life cycle perspective and help
to guide IT investment decisions:
1. Principle of diminishing data value. The value of data diminishes as they age. This is a simple, yet powerful principle. Most organizations cannot operate at
peak performance with blind spots (lack of data availability) of 30 days or
longer. Global fi nancial services institutions rely on near real time data for peak
performance.
Figure 3.11 Data life cycle.
Data Sources and Databases
Personal expertise & judgment
Data Visualization
SCM
E-commerce
Strategy
Others
CRM
Data AnalysisData Storage Results
Business Analytics
Business Applications
Internal Data
External Data
Data Warehouse
Data Marts
Data Marts
OLAP, Queries, EIS, DSS
Data Mining
Decision Support
Knowledge and its
Management
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3.1 Database Management Systems 85
2. Principle of 90/90 data use. According to the 90/90 data-use principle, a majority of stored data, as high as 90 percent, is seldom accessed after 90 days (except for
auditing purposes). That is, roughly 90 percent of data lose most of their value
after 3 months.
3. Principle of data in context. The capability to capture, process, format, and distribute data in near real time or faster requires a huge investment in data
architecture (Chapter 2) and infrastructure to link remote POS systems to data
storage, data analysis systems, and reporting apps. The investment can be justi-
fi ed on the principle that data must be integrated, processed, analyzed, and for-
matted into “actionable information.”
MASTER DATA AND MASTER DATA MANAGEMENT
As data become more complex and their volumes explode, database performance
degrades. One solution is the use of master data and master data management (MDM), as introduced in Chapter 2. MDM processes integrate data from various sources or enterprise applications to create a more complete (unified) view of a cus-
tomer, product, or other entity. Figure 3.12 shows how master data serve as a layer
between transactional data in a database and analytical data in a data warehouse.
Although vendors may claim that their MDM solution creates “a single version of
the truth,” this claim is probably not true. In reality, MDM cannot create a single
unified version of the data because constructing a completely unified view of all
master data is simply not possible.
Master Reference File and Data Entities
Realistically, MDM consolidates data from various data sources into a master refer-
ence file, which then feeds data back to the applications, thereby creating accurate
and consistent data across the enterprise. In IT at Work 3.1, participants in the
health-care supply chain essentially developed a master reference file of its key data
entities. A data entity is anything real or abstract about which a company wants to collect and store data. Master data entities are the main entities of a company, such
as customers, products, suppliers, employees, and assets.
Each department has distinct master data needs. Marketing, for example, is
concerned with product pricing, brand, and product packaging, whereas production
is concerned with product costs and schedules. A customer master reference file can
feed data to all enterprise systems that have a customer relationship component,
thereby providing a more unified picture of customers. Similarly, a product master
reference file can feed data to all the production systems within the enterprise.
An MDM includes tools for cleaning and auditing the master data elements as
well as tools for integrating and synchronizing data to make them more accessible.
MDM offers a solution for managers who are frustrated with how fragmented and
dispersed their data sources are.
Figure 3.12 An enterprise has transactional, master, and analytical data.
Transactional data supports
the applications.
Transactional
Data
Master
Data
Analytical
Data
Enterprise
Data
Master data describes the enterprise’s
business entities upon which
transactions are done and the
dimensions (Customer, Product,
Supplier, Account, and Site), around
which analyses are done.
Analytical data supports
decision making and planning.
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86 Chapter 3 Data Management, Big Data Analytics, and Records Management
At an insurance company, the cost of processing each claim is $1, but the average downstream cost due to errors in a claim is $300. The $300 average downstream costs included manual handling of exceptions, customer support calls initi- ated due to errors in claims, and reissuing corrected docu- ments for any claims processed incorrectly the first time. In addition, the company faced significant soft costs from regulatory risk, lost revenues due to customer dissatisfaction, and overpayment on claims due to claims-processing errors. These soft costs are not included in the hard cost of $300.
Every day health-care administrators and others through- out the health-care supply chain waste 24 to 30 percent of their time correcting data errors. Each transaction error costs $60 to $80 to correct. In addition, about 60 percent of all
invoices among supply chain partners contain errors, and each invoice error costs $40 to $400 to reconcile. Altogether, errors and conflicting data increase supply costs by 3 to 5 percent. In other words, each year billions of dollars are wasted in the health-care supply chain because of supply chain data disconnects, which refer to one organization’s IS not understanding data from another’s IS.
Questions
1. Why are the downstream costs of data errors so high? 2. What are soft costs? 3. Explain how soft costs might exceed hard costs. Give an
example.
IT at Work 3 . 1 Data Errors Increase Costs Downstream
Questions 1. Describe a database and a database management system (DBMS). 2. Explain what an online transaction-processing (OLAP) system does. 3. Why are data in databases volatile? 4. Explain what processes DBMSs are optimized to perform. 5. What are the business costs or risks of poor data quality? 6. Describe the data life cycle. 7. What is the function of master data management (MDM)?
The senior marketing manager of a major U.S. retailer learned that her company
was steadily losing market share to a competitor in many of their profitable seg- ments. Losses continued even after a sales campaign that combined online promo-
tions with improved merchandizing (Brown, Chui, & Manyika, 2011). To under-
stand the causes, a team of senior managers studied their competitor’s practices.
They discovered that the problems were not simply due to basic marketing tactics,
but ran much deeper. The competitor:
• Had invested heavily in IT to collect, integrate, and analyze data from each
store and sales unit.
• Had linked these data to suppliers’ databases, making it possible to adjust
prices in real time, to reorder hot-selling items automatically, and to shift
items from store to store easily.
• Was constantly testing, integrating, and reporting information instantly
available across the organization—from the store floor to the CFO’s office.
The senior management team realized that their competitor was stealing away
their customers because big data analytics enabled them to pinpoint improvement
3.2 Data Warehouse and Big Data Analytics
Market share is the percent- age of total sales in a market captured by a brand, product, or company.
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3.2 Data Warehouse and Big Data Analytics 87
opportunities across the supply chain—from purchasing to in-store availabil-
ity management. Specifically, the competitor was able to predict how customers
would behave and used that knowledge to be prepared to respond quickly. This case is an example of what researchers have learned. According to the McKinsey
Global Institute (MGI), big data analytics have helped companies outperform their
competitors. MGI estimates that retailers using big data analytics increase their
operating margin by more than 60 percent. Leading retailers, insurance, and financial services use big data to capture market share away from local competi-
tors (Breuer, Forina, & Moulton, 2013). An IBM study shows that companies with
advanced business analytics and optimization can experience 20 times more profit
growth and 30 percent higher return on invested capital (ibm.com, 2011).
In this section, you will learn about the value, challenges, and technologies
involved in putting data and analytics to use to support decisions and action. The four
V’s of analytics—variety, volume, velocity, and veracity—are described in Table 3.2.
Big data can have a dramatic impact on the success of any enterprise, or they
can be a low-contributing major expense. However, success is not achieved with
technology alone. Many companies are collecting and capturing huge amounts of
data, but spending very little effort to ensure the veracity and value of data captured
at the transactional stage or point of origin. Emphasis in this direction will not only
increase confidence in the datasets, but also significantly reduce the efforts for ana-
lytics and enhance the quality of decision making. Success depends also on ensuring
that you avoid invalid assumptions, which can be done by testing the assumptions
during analysis.
Operating margin is a measure of the percent of a company’s revenue left over after paying for its variable costs, such as wages and raw materials. An increasing margin means the company is earning more per dollar of sales. The higher the operating margin, the better.
TABLE 3.2 Four V’s of Data Analytics
1. Variety: The analytic environment has expanded from pulling data from enter- prise systems to include big data and unstructured sources.
2. Volume: Large volumes of structured and unstructured data are analyzed.
3. Velocity: Speed of access to reports that are drawn from data defi nes the differ- ence between effective and ineffective analytics.
4. Veracity: Validating data and extracting insights that managers and workers can trust are key factors of successful analytics. Trust in analytics has grown
more diffi cult with the explosion of data sources.
Managing and Interpreting Big Data Are in Highest Demand
The IT job market is on the rise, and top jobs include
anything in big data, mobile, cloud, or IT security.
TechRepublic held a roundtable of IT executives and
tech recruiters to learn about the latest hiring trends.
Here are three forecasts (Hammond, 2014):
• Pete Kazanjy, co-founder of TalentBin, stated
there “will be the continued uptick in demand
for technical talent, but more broadly across the
entire economy, and not just siloed in its own tech
sector. Technology is ceasing to be a sector on its
own, and is instead becoming more critical in every
industry.”
• Tendu Yogurtcu, vice president of engineering at
Syncsort, explained: “With the rising popularity of
Hadoop, positions are geared towards filling these
roles, with lots of interest placed on big data and data
mining and analysis. Most of the new hires are recent
graduates, since they embody a lot of creativity and
forward thinking, both qualities needed in the indus-
try of big data.”
C A R E E R I N S I G H T 3 . 1 J O B S
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TORTURE DATA LONG ENOUGH AND IT WILL CONFESS . . . BUT MAY NOT TELL THE TRUTH
As someone posted in a Harvard Business Review (HBR) blog, “If you torture the data long enough, it will confess” (Neill, 2013). That is, analytics will produce
results, but those results may be meaningless or misleading. For example, some
believe that Super Bowl results in February predict whether the stock market will
go up or down that year. If the National Football Conference (NFC) wins, the mar-
ket goes up; otherwise, stocks take a dive. Looking at results over the past 30 years,
most often the NFC has won the Super Bowl and the market has gone up. Does this
mean anything? No.
HUMAN EXPERTISE AND JUDGMENT ARE NEEDED
Human expertise and judgment are needed to interpret the output of analytics
(refer to Figure 3.1). Data are worthless if you cannot analyze, interpret, under-
stand, and apply the results in context. This brings up several challenges:
• Data need to be prepared for analysis. For example, data that are incom- plete or duplicated need to be fixed.
• Dirty data degrade the value of analytics. The “cleanliness” of data is very important to data mining and analysis projects. Analysts have complained
that data analytics is like janitorial work because they spend so much time
on manual, error-prone processes to clean the data. Large data volumes and
variety mean more data that are dirty and harder to handle.
• Data must be put into meaningful context. If the wrong analysis or datasets are used, the output would be nonsense, as in the example of the Super Bowl
winners and stock market performance. Stated in reverse, managers need
context in order to understand how to interpret traditional and big data.
IT at Work 3.2 describes how big data analytics, collaboration, and human
expertise have transformed the new drug development process.
• Robert Noble, director of software of engineering
at WhitePages, gave an overview of the recruiting
issues: “The demand for tech and software talent
is exploding. A lot of companies have been aggres-
sive and creative to compete for candidates in these
fields. For instance, besides compensation and the
technical work of the job role, companies are using
culture as a key differentiator. They are not only
talking about the company, they are also talking
about the perks outside of work, and benefits, like
cool team events, providing free haircuts, massages,
food and more.”
Drug development is a high-risk business. Almost 90 percent of new drugs ultimately fail. One of the challenges has been the amount, variety, and complexity of the data that need to be systematically analyzed. Big data technologies and private– public partnerships have made biomedical analytics feasible.
New Drug Development Had Been Slow and Expensive
Biotechnology advances have produced massive data on the biological causes of disease. However, analyzing these data
and converting discoveries into treatments are much more difficult. Not all biomedical insights lead to effective drug targets, and choosing the wrong target leads to failures late in the drug development process, costing time, money, and lives. Developing a new drug—from early discovery through Food and Drug Administration (FDA) approval—takes over a decade and has a failure rate of more than 95 percent (Figure 3.13). As a consequence, each success ends up costing more than $1 billion.
IT at Work 3 . 2 Researchers Use Genomics and Big Data in Drug Discovery
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3.2 Data Warehouse and Big Data Analytics 89
For example, by the time Pfizer Inc., Johnson & Johnson, and Eli Lilly & Co. announced their new drugs had only lim- ited benefit for Alzheimer’s patients in late-stage testing, the industry had spent more than $30 billion researching amyloid plaque in the brain.
Cutting Risk of Failure
Drug makers, governments, and academic researchers have partnered to improve the odds of drug success. Partnerships bring together the expertise of scientists from biology, chemistry, bioinformatics, genomics, and big data. They are using big data to identify biological targets for drugs and eliminate failures before they reach the human testing stage.
GlaxoSmithKline, the European Bioinformatics Institute (EBI), and the Wellcome Trust Sanger Institute established the Centre for Therapeutic Target Validation (CTTV) near Cambridge, England. CTTV partners combine cutting-edge genomics with the ability to collect and analyze massive amounts of biological data. By not developing drugs that target the wrong biological pathways, they avoid wasting billions of research dollars.
Janet Thornton, director of the EBI, explained that maximizing “our use of ‘big data’ in the life sciences is critical for solving some of society’s most pressing problems” (Kitamura, 2014). With biology now a data-driven discipline, collaborations such as CTTV are needed to improve efficien- cies, cut costs, and provide the best opportunities for suc- cess. Other private–public partnerships that had formed to harness drug research and big data include:
• Accelerating Medicines Partnership and U.S. National Institutes of Health (NIH). In February 2014 the NIH announced that the agency, 10 pharmaceutical companies, and nonprofit organizations were investing $230 million in the Accelerating Medicines Partnership.
• Target Discovery Institute and Oxford University. Oxford University opened the Target Discovery Institute in 2013. Target Discovery helps to identify drug targets and molecular interactions at a critical point in a disease- causing pathway—that is, when those diseases will respond to drug therapy. Researchers try to understand complex biological processes by analyzing image data that have been acquired at the microscopic scale.
“By changing our business model, taking a more open- minded approach to sharing information and forging collab- orations like the CTTV, we believe there is an opportunity to accelerate the development of innovative new medicines,” said Patrick Vallance, president of Glaxo’s pharmaceuticals research and development (Kitamura, 2014).
Sources: Compiled from Kitamura (2014), NIH (2014), and HealthCanal
(2014).
Questions
1. What are the consequences of new drug development failures?
2. What factors have made biomedical analytics feasible? Why?
3. Large-scale big data analytics are expensive. How can the drug makers justify investments in big data?
4. Why would drug makers such as Glaxo and Pfizer be willing to share data given the fierce competition in their industry?
Figure 3.13 An estimated 90 to 95 percent of new drugs that undergo clinical trials ultimately fail. These costs drive up the prices of drugs that are a success— to an average of $1 billion.
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ENTERPRISE DATA WAREHOUSE AND DATA MART
Data warehouses store data from various source systems and databases across
an enterprise in order to run analytical queries against huge datasets collected
over long time periods. Warehouses are the primary source of cleansed data
for analysis, reporting, and BI. Often the data are summarized in ways that enable
quick responses to queries. For instance, query results can reveal changes in cus-
tomer behavior and drive the decision to redevelop the advertising strategy.
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Data warehouses that pull together data from disparate sources and databases
across an entire enterprise are called enterprise data warehouses (EDW). Tech Note 3.2 summarizes key characteristics of the two types of data stores.
The high cost of data warehouses can make them too expensive for a company
to implement. Data marts are lower-cost, scaled-down versions that can be imple- mented in a much shorter time, for example, in less than 90 days. Data marts serve
a specific department or function, such as finance, marketing, or operations. Since
they store smaller amounts of data, they are faster, easier to use, and navigate.
TECH NOTE 3.2 Summary of Differences Between Databases and Data Warehouses
Databases are: • Designed and optimized to ensure that every transaction gets recorded and
stored immediately.
• Volatile because data are constantly being updated, added, or edited.
• OLTP systems.
Data warehouses are: • Designed and optimized for analysis and quick response to queries.
• Nonvolatile. This stability is important to being able to analyze the data and
make comparisons. When data are stored, they might never be changed or
deleted in order to do trend analysis or make comparisons with newer data.
• OLAP systems.
• Subject-oriented, which means that the data captured are organized to have
similar data linked together.
Procedures to Prepare EDW Data for Analytics
Consider a bank’s database. Every deposit, withdrawal, loan payment, or other
transaction adds or changes data. The volatility caused by constant transaction
processing makes data analysis difficult—and the demands to process millions of
transactions per second consume the database’s processing power. In contrast, data
in warehouses are relatively stable, as needed for analysis. Therefore, select data
are moved from databases to a warehouse. Specifically, data are:
1. Extracted from designated databases.
2. Transformed by standardizing formats, cleaning the data, integrating them.
3. Loaded into a data warehouse.
These three procedures—extract, transform, and load—are referred to by their initials ETL (Figure 3.14). In a warehouse, data are read-only; that is, they do not change until the next ETL.
Three technologies involved in preparing raw data for analytics include ETL, change data capture (CDC), and data deduplication (“deduping the data”). CDC processes capture the changes made at data sources and then apply those changes
throughout enterprise data stores to keep data synchronized. CDC minimizes the
resources required for ETL processes by only dealing with data changes. Deduping
processes remove duplicates and standardize data formats, which helps to minimize
storage and data synch.
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3.2 Data Warehouse and Big Data Analytics 91
BUILDING A DATA WAREHOUSE
Figure 3.15 diagrams the process of building and using a data warehouse. The orga-
nization’s data are stored in operational systems (left side of the figure). Not all data
are transferred to the data warehouse. Frequently, only summary data are trans-
ferred. The warehouse organizes the data in multiple ways—by subject, functional
area, vendor, and product. As shown, the data warehouse architecture defines the
flow of data that starts when data are captured by transaction systems; the source
data are stored in transactional (operational) databases; ETL processes move data
from databases into data warehouses or data marts, where the data are available for
access, reports, and analysis.
REAL TIME SUPPORT FROM ACTIVE DATA WAREHOUSE
Early data warehouse technology primarily supported strategic applications that
did not require instant response time, direct customer interaction, or integration
with operational systems. ETL might have been done once per week or once per
month. But, demand for information to support real time customer interaction and
operations leads to real time data warehousing and analytics—known as an active data warehouse (ADW). Massive increases in computing power, processing speeds, and memory made ADW possible. ADW are not designed to support executives’
Business Intelligence Management
Analytics
Reporting
Queries
Data Mining
InformationData Marts
Data Mart
Data
Warehouse
Data
Warehouse
Business
Intelligence
Environment
ETL
processes
Transaction
Systems
Operational
Databases
Figure 3.15 Database, data warehouse and marts, and BI architecture.
Figure 3.14 Data enter databases from transaction systems. Data of interest are extracted from databases, transformed to clean and standardize them, and then loaded into a data warehouse. These three processes are called ETL. ©
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92 Chapter 3 Data Management, Big Data Analytics, and Records Management
strategic decision making, but rather to support operations. For example, shipping
companies like DHL use huge fleets of trucks to move millions of packages. Every
day and all day, operational managers make thousands of decisions that affect the
bottom line, such as: “Do we need four trucks for this run?” “With two drivers
delayed by bad weather, do we need to bring in extra help?” Traditional data ware-
housing is not suited for immediate operational support, but active data warehous-
ing is. For example, companies with an ADW are able to:
• Interact with a customer to provide superior customer service.
• Respond to business events in near real time.
• Share up-to-date status data among merchants, vendors, customers, and associates.
Here are some examples of how two companies use ADW:
Capital One. Capital One uses its ADW to track each customer’s “profitability score” to determine the level of customer service to provide that person. Higher-
cost personalized service is only given to those with high scores. For instance, when
a customer calls Capital One, he or she is asked to enter a credit card number, which
is linked to a profitability score. Low-profit customers get a voice response unit
only; high-profit customers are connected to a live customer service representative
(CSR) because the company wants to minimize the risk of losing those customers.
Travelocity. If you use Travelocity, an ADW is finding the best travel deals especially for you. The goal is to use “today’s data today” instead of “yesterday’s
data today.” The online travel agency’s ADW analyzes your search history and des-
tinations of interest; then predicts travel offers that you would most likely purchase.
Offers are both relevant and timely to enhance your experience, which helps close
the sale in a very competitive market. For example, when a customer is searching
flights and hotels in Las Vegas, Travelocity recognizes the interest—the customer
wants to go to Vegas. The ADW searches for the best-priced flights from all car-
riers, builds a few package deals, and presents them in real time to the customer.
When customers see a personalized offer they are already interested in, the ADW
helps generate a better customer experience. The real time data-driven experience
increases the conversion rate and sales.
Data warehouse content can be delivered to decision makers throughout the
enterprise via the cloud or company-owned intranets. Users can view, query, and
analyze the data and produce reports using Web browsers. These are extremely
economical and effective data delivery methods.
Data Warehousing Supports Action as Well as Decisions
Many organizations built data warehouses because they were frustrated with incon-
sistent data that could not support decisions or actions. Viewed from this perspec-
tive, data warehouses are infrastructure investments that companies make to support
ongoing and future operations, such as:
• Marketing and sales. Keeps people informed of the status of products, mar- keting program effectiveness, and product line profitability; and allows them
to take intelligent action to maximize per-customer profitability.
• Pricing and contracts. Calculates costs accurately in order to optimize pricing of a contract. Without accurate cost data, prices may be below or
too near to cost; or prices may be uncompetitive because they are too high.
• Forecasting. Estimates customer demand for products and services.
• Sales. Calculates sales profitability and productivity for all territories and regions; analyzes results by geography, product, sales group, or
individual.
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3.2 Data Warehouse and Big Data Analytics 93
• Financial. Provides real time data for optimal credit terms, portfolio analysis, and actions that reduce risk or bad debt expense.
Table 3.3 summarizes several successful applications of data warehouses.
BIG DATA ANALYTICS AND DATA DISCOVERY
Data analytics help users discover insights. These insights combined with human
expertise enable people to recognize meaningful relationships more quickly or easily;
and furthermore, realize the strategic implications of these situations. Imagine try-
ing to make sense of the fast and vast data generated by social media campaigns on
Facebook or by sensors attached to machines or objects. Low-cost sensors make it
possible to monitor all types of physical things—while analytics make it possible to
understand those data in order to take action in real time. For example, sensors data
can be analyzed in real time:
• To monitor and regulate the temperature and climate conditions of perish- able foods as they are transported from farm to supermarket.
• To sniff for signs of spoilage of fruits and raw vegetables and detect the risk of E. coli contamination.
• To track the condition of operating machinery and predict the probability of failure.
• To track the wear of engines and determine when preventive maintenance is needed.
TABLE 3.3 Data Warehouse Applications by Industry
Industry Applications
Airline Crew assignment, aircraft deployment, analysis of
route profi tability, customer loyalty promotions
Banking and fi nancial Customer service, trend analysis, product and
service services promotions, reduction of IS
expenses
Credit card Customer service, new information service for a
fee, fraud detection
Defense contracts Technology transfer, production of military
applications
E-business Data warehouses with personalization capabilities,
marketing/shopping preferences allowing for
up-selling and cross-selling
Government Reporting on crime areas, homeland security
Health care Reduction of operational expenses
Investment and insurance Risk management, market movements analysis,
customer tendencies analysis, portfolio
management
Retail chain Trend analysis, buying pattern analysis, pricing
policy, inventory control, sales promotions, optimal
distribution channel decision
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94 Chapter 3 Data Management, Big Data Analytics, and Records Management
Machine-generated sensor data are becoming a larger proportion of big data
(Figure 3.16), according to a research report by IDC (Lohr, 2012b). It is predicted
that these data will increase to 42 percent of all data by 2020, representing a signifi-
cant increase from the 11 percent level of 2005.
The value of analyzing machine data was recognized by General Electric (GE)
Company, the United States’ largest industrial company. Since 2011 GE has been put-
ting sensors on everything from gas turbines to hospital beds. GE’s mission is to design
the software for gathering data, and the algorithms for analyzing them to optimize cost
savings and productivity gains. Across the industries that it covers, GE estimates effi-
ciency opportunities will slash costs by as much as $150 billion (Lohr, 2012a).
Federal health reform efforts have pushed health-care organizations toward
big data and analytics. These organizations are planning to use big data analytics to
support revenue cycle management, resource utilization, fraud prevention, health
management, and quality improvement.
Hadoop and MapReduce
Big data volumes exceed the processing capacity of conventional database
infrastructures. A widely used processing platform is Apache Hadoop (hadoop. apache.org/). It places no conditions on the structure of the data it can process.
Hadoop distributes computing problems across a number of servers. Hadoop
implements MapReduce in two stages:
1. Map stage: MapReduce breaks up the huge dataset into smaller subsets; then dis- tributes the subsets among multiple servers where they are partially processed.
2. Reduce stage: The partial results from the map stage are then recombined and made available for analytic tools.
Figure 3.16 Machine- generated data from physical objects are becoming a much larger portion of big data and analytics. ©
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3.2 Data Warehouse and Big Data Analytics 95
To store data, Hadoop has its own distributed file system, HaDoop File Systems (HDFS), which functions in three stages:
• Loads data into HDFS.
• Performs the MapReduce operations.
• Retrieves results from HDFS.
Figure 3.17 diagrams how Facebook uses database technology and Hadoop. IT at Work 3.3 describes how First Wind has applied big data analytics to improve the operations of its wind farms and to support sustainability of the planet by reducing
environmentally damaging carbon emissions.
Figure 3.17 Facebook’s MySQL database and Hadoop technology provide customized pages for its members.
Wind power can play a major role in meeting America’s ris- ing demand for electricity—as much as 20 percent by 2030. Using more domestic wind power would reduce the nation’s dependence on foreign sources of natural gas and also decrease carbon dioxide (CO2) emissions that contribute to adverse climate change.
First Wind is an independent North American renew- able energy company focused on the development, financ- ing, construction, ownership, and operation of utility-scale power projects in the United States. Based in Boston, First Wind has developed and operates 980 megawatts (MW) of generating capacity at 16 wind energy projects in Maine, New York, Vermont, Utah, Washington, and Hawaii. First Wind has a large network of sensors embedded in the wind turbines, which generate huge volumes of data continu- ously. The data are transmitted in real time and analyzed on a 24/7 real time basis to understand the performance of each wind turbine.
Sensors collect massive amounts of data on the tem- perature, wind speeds, location, and pitch of the blades. The data are analyzed to study the operation of each turbine in order to adjust them to maximum efficiency. By analyzing sensor data, highly refined measurements of wind speeds
are possible. In wintry conditions, turbines can detect when they are icing up, and speed up or change pitch to knock off the ice. In the past, when it was extremely windy, turbines in the entire farm had been turned off to prevent damage from rotating too fast. Now First Wind can identify the specific portion of turbines that need to be shut down. Based on certain alerts, decisions often need to be taken within a few seconds.
Upgrades on 123 turbines on two wind farms have improved energy output by 3 percent, or about 120 megawatt hours per turbine per year. That improvement translates to $1.2 million in additional revenue a year from these two farms.
Sources: Compiled from Lohr (2012a), FirstWind.com (2014), and U.S.
Department of Energy (2008).
Questions
1. What are the benefits of big data analytics to First Wind? 2. What are the benefits of big data analytics to the environ-
ment and the nation? 3. How do big data analytics impact the performance of
wind farms?
IT at Work 3 . 3 Industrial Project Relies on Big Data Analytics
MySQL databases capture and
store Facebook’s data.
Results are transferred back into
MySQL for use in pages that are
loaded for members.
Members see customized Facebook
pages.
Data are loaded into Hadoop where
processing occurs, such as identifying
recommendations for you based on
your friends’ interests.
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96 Chapter 3 Data Management, Big Data Analytics, and Records Management
Questions 1. Why are human expertise and judgment important to data analytics?
Give an example. 2. What is the relationship between data quality and the value of analytics? 3. Why do data need to be put into a meaningful context? 4. What are the differences between databases and data warehouses? 5. Explain ETL and CDC. 6. What is an advantage of an active data warehouse (ADW)? 7. Why might a company invest in a data mart? 8. How can manufacturers and health care benefi t from data analytics? 9. Explain how Hadoop implements MapReduce in two stages.
As you read, DBMSs support queries to extract data or get answers from huge
databases. But in order to perform queries, you must first know what to ask for or
what you want answered. In data mining and text mining, it is the opposite. Data
and text mining are used to discover knowledge that you did not know existed in
the databases.
Business analytics describes the entire function of applying technologies, algo- rithms, human expertise, and judgment. Data and text mining are specific analytic
techniques.
3.3 Data and Text Mining
CREATING BUSINESS VALUE
Enterprises invest in data mining tools to add business value. Business value falls
into three categories, as shown in Figure 3.18.
Here are brief cases illustrating the types of business value created by data and
text mining.
1. Using pattern analysis, Argo Corporation, an agricultural equipment manufacturer based in Georgia, was able to optimize product confi guration options for farm
machinery and real time customer demand to determine the optimal base confi gu-
rations for its machines. As a result, Argo reduced product variety by 61 percent
and cut days of inventory by 81 percent while still maintaining its service levels.
2. The mega-retailer Walmart wanted its online shoppers to fi nd what they were looking for faster. Walmart analyzed clickstream data from its 45 million monthly
online shoppers; then combined that data with product and category-related
popularity scores. The popularity scores had been generated by text mining the
retailer’s social media streams. Lessons learned from the analysis were integrated
into the Polaris search engine used by customers on the company’s website.
Polaris has yielded a 10 to 15 percent increase in online shoppers completing a
purchase, which equals roughly $1 billion in incremental online sales.
3. McDonald’s bakery operation replaced manual equipment with high-speed photo analyses to inspect thousands of buns per minute for color, size, and sesame
seed distribution. Automatically, ovens and baking processes adjust instantly
to create uniform buns and reduce thousands of pounds of waste each year.
Another food products company also uses photo analyses to sort every french
fry produced in order to optimize quality.
4. Infi nity Insurance discovered new insights that it applied to improve the per- formance of its fraud operation. The insurance company text mined years of
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3.3 Data and Text Mining 97
adjuster reports to look for key drivers of fraudulent claims. As a result, the
company reduced fraud by 75 percent, and eliminated marketing to customers
with a high likelihood of fraudulent claims.
DATA AND TEXT MINING
Data mining software enables users to analyze data from various dimensions or angles, categorize them, and find correlations or patterns among fields in the data
warehouse. Up to 75 percent of an organization’s data are nonstructured word-
processing documents, social media, text messages, audio, video, images and diagrams,
faxes and memos, call center or claims notes, and so on. Text mining is a broad category that involves interpreting words and concepts in context. Any customer
becomes a brand advocate or adversary by freely expressing opinions and attitudes
that reach millions of other current or prospective customers on social media. Text
mining helps companies tap into the explosion of customer opinions expressed
online. Social commentary and social media are being mined for sentiment analysis or to understand consumer intent. Innovative companies know they could be more
successful in meeting their customers’ needs, if they just understood them better.
Tools and techniques for analyzing text, documents, and other nonstructured con-
tent are available from several vendors.
Combing Data and Text Mining
Combining data and text mining can create even greater value. Palomäki and
Oksanen (2012) pointed out that mining text or nonstructural data enables organi-
zations to forecast the future instead of merely reporting the past. They also noted
that forecasting methods using existing structured data and nonstructured text from
both internal and external sources provide the best view of what lies ahead.
Figure 3.18 Business value falls into three buckets.
Making more informed decisions at the time they need to be made
Discovering unknown insights, patterns, or relationships
Automating and streamlining or digitizing business processes
The Defense Advanced Research Projects Agency (DARPA) was established in 1958 to prevent strategic surprise from negatively impacting U.S. national security and to create stra- tegic surprise for U.S. adversaries by maintaining the techno- logical superiority of the U.S. military. One DARPA office is the Information Innovation Office (I2O). I2O aims to ensure U.S. technological superiority in all areas where information can provide a decisive military advantage. This includes intel- ligence, surveillance, reconnaissance, and operations support.
Figure 3.19 is an example. Nexus 7 is one of DARPA’s intel- ligence systems.
Nexus 7, Data Mining System
Nexus 7 is a massive data mining system put into use by the U.S. military in Afghanistan to understand Afghan society, and to look for signs of weakness or instability. The classified program ties together “everything from spy radars to fruit prices” in order to read the Afghan social situation and help
IT at Work 3 . 4 U.S. Military Uses Data Mining Spy Machine for Cultural Intelligence
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98 Chapter 3 Data Management, Big Data Analytics, and Records Management
the U.S. military plot its strategy. DARPA describes Nexus 7 as both a breakthrough data analysis tool and an oppor- tunity to move beyond its traditional, long-range research role into a more active wartime mission. Nexus 7 gathers information that can reveal exactly where a town is working and where it is broken; and where the traffic piles up and where it flows free.
Cultural Intelligence
On the military’s classified network, DARPA technologists describe Nexus 7 as far-reaching and revolutionary, taking data from many agencies to produce population-centric, cultural intelligence. For example, Nexus 7 searches the vast U.S. spy apparatus to figure out which communities in Afghanistan are falling apart and which are stabilizing, which are loyal to the government in Kabul, and which are falling under the influence of militants.
A small Nexus 7 team is currently working in Afghanistan with military-intelligence officers, while a much larger group
in Virginia with a “large-scale processing capacity” handles the bulk of the data crunching, according to DARPA. “Data in the hands of some of the best computer scientists working side by side with operators provides useful insights in ways that might not have otherwise been realized” (Shachtman, 2011).
Sources: Compiled from DARPA.mil (2012), Shachtman (2011), and
Defense Systems (2011).
Questions
1. What is Nexus 7? 2. How does data mining help I2O achieve its mission? 3. What are Nexus 7’s data sources? 4. According to DARPA, what benefit does Nexus 7 provide
that could not be realized without it?
Text Analytics Procedure
With text analytics, information is extracted from large quantities of various types
of textual information. The basic steps involved in text analytics include:
1. Exploration. First, documents are explored. This might occur in the form of sim- ple word counts in a document collection, or by manually creating topic areas to
categorize documents after reading a sample of them. For example, what are the
major types of issues (brake or engine failure) that have been identifi ed in recent
automobile warranty claims? A challenge of the exploration effort is misspelled
or abbreviated words, acronyms, or slang.
2. Preprocessing. Before analysis or the automated categorization of content, the text may need to be preprocessed to standardize it to the extent possible. As in
traditional analysis, up to 80 percent of preprocessing time can be spent preparing
and standardizing the data. Misspelled words, abbreviations, and slang may need
to be transformed into consistent terms. For instance, BTW would be standard-
ized to “by the way” and “left voice message” could be tagged as “lvm.”
3. Categorizing and Modeling. Content is then ready to be categorized. Catego- rizing messages or documents from information contained within them can be
achieved using statistical models and business rules. As with traditional model
development, sample documents are examined to train the models. Additional
documents are then processed to validate the accuracy and precision of the
model, and fi nally new documents are evaluated using the fi nal model (scored).
Models can then be put into production for the automated processing of new
documents as they arrive.
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3.4 Business Intelligence 99
Quicken Loans, Inc. is the largest online mortgage lender and second largest overall
retail lender in the United States. The Detroit-based company closed more than
$70 billion in home loans in 2012, which was more than double the $30 billion figure
in 2011. In 2013 Quicken Loans continued its explosive growth, closing a company
record $80 billion in home loan volume. The company also grew its loan servicing
capabilities to become the 11th largest mortgage servicer in the nation, with more
than $138 billion in home loans in its portfolio.
In 2014 FORTUNE Magazine ranked Quicken Loans one of the top 5 places to work nationwide, which marked the 11th consecutive year it ranked in the top 30 of
Fortune’s benchmark workplace culture study. For the fourth consecutive year, the
company was named by J.D. Power as the highest in customer satisfaction among
all home loan lenders in America.
One key success factor is BI. At the 2013 Data Warehousing Institute’s (TDWI)
Best Practices Awards that recognized companies for their world-class BI and data
warehousing solutions, Quicken managers explained:
This growth can be attributed to the success of our online lending plat-
form. Our scalable, technology-driven loan platform has allowed us to
handle a large surge in loan applications while keeping closing times for
the majority of our loans at 30 days or less. (TDWI, 2013)
Using BI, the company has increased the speed from loan application to close,
which allows it to meet client needs as thoroughly and quickly as possible. Over
almost a decade, performance management has evolved from a manual process of
report generation to BI-driven dashboards and user-defined alerts that allow busi-
ness leaders to proactively deal with obstacles and identify opportunities for growth
and improvement.
The field of BI started in the late 1980s and has been a key to competitive
advantage across industries and in enterprises of all sizes. What started as a tool to
support sales, marketing, and customer service departments has widely evolved into
an enterprisewide strategic platform. While BI systems are used in the operational
management of divisions and business processes, they are also used to support
strategic corporate decision making. The dramatic change that has taken effect
over the last few years is the growth in demand for operational intelligence across
multiple systems and businesses—increasing the number of people who need access
to increasing amounts of data. Complex and competitive business conditions do not
leave much slack for mistakes.
3.4 Business Intelligence
Text analytics can help identify the ratio of positive/negative posts relating to
the promotion. It can be a powerful validation tool to complement other primary
and secondary customer research and feedback management initiatives. Companies
that improve their ability to navigate and text mine the boards and blogs relevant
to their industry are likely to gain a considerable information advantage over their
competitors.
Questions 1. Describe data mining. 2. How does data mining generate or provide value? Give an example. 3. What is text mining? 4. Explain the text mining procedure.
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100 Chapter 3 Data Management, Big Data Analytics, and Records Management
BUSINESS BENEFITS OF BI
BI provides data at the moment of value to a decision maker—enabling it to extract crucial facts from enterprise data in real time or near real time. A BI solu-
tion with a well-designed dashboard, for example, provides retailers with better
visibility into inventory to make better decisions about what to order, how much,
and when in order to prevent stock-outs or minimize inventory that sits on ware-
house shelves.
Companies use BI solutions to determine what questions to ask and find
answers to them. BI tools integrate and consolidate data from various internal and
external sources and then process them into information to make smart decisions.
BI answers questions such as these: Which products have the highest repeat sales
rate in the last six months? Do customer likes on Facebook relate to product pur-
chase? How does the sales trend break down by product group over the last five
years? What do daily sales look like in each of my sales regions?
According to TDWI, BI “unites data, technology, analytics, and human knowl-
edge to optimize business decisions and ultimately drive an enterprise’s success.
BI programs usually combine an enterprise data warehouse and a BI platform or
tool set to transform data into usable, actionable business information” (TDWI,
2014). For many years, managers have relied on business analytics to make better-
informed decisions. Multiple surveys and studies agree on BI’s growing importance
in analyzing past performance and identifying opportunities to improve future
performance.
COMMON CHALLENGES: DATA SELECTION AND QUALITY
Companies cannot analyze all of their data—and much of them would not add
value. Therefore, an unending challenge is how to determine which data to use for
BI from what seems like unlimited options (Schroeder, 2013). One purpose of a
BI strategy is to provide a framework for selecting the most relevant data without
limiting options to integrate new data sources. Information overload is a major problem for executives and for employees. Another common challenge is data quality,
particularly with regard to online information, because the source and accuracy
might not be verifiable.
ALIGNING BUSINESS STRATEGY WITH BI STRATEGY
Reports and dashboards are delivery tools, but they may not be delivering business
intelligence. To get the greatest value out of BI, the CIO needs to work with the
CFO and other business leaders to create a BI governance program whose mission
is to achieve the following (Acebo et al., 2013):
1. Clearly articulate business strategies.
2. Deconstruct the business strategies into a set of specifi c goals and objectives— the targets.
3. Identify the key performance indicators (KPIs) that will be used to measure progress toward each target.
4. Prioritize the list of KPIs.
5. Create a plan to achieve goals and objectives based on the priorities.
6. Estimate the costs needed to implement the BI plan.
7. Assess and update the priorities based on business results and changes in busi- ness strategy.
After completing these activities, BI analysts can identify the data to use in BI
and the source systems. This is a business-driven development approach that starts with a business strategy and work backward to identify data sources and the data
that need to be acquired and analyzed.
Businesses want KPIs that can be utilized by both departmental users and
management. In addition, users want real time access to these data so that they
can monitor processes with the smallest possible latency and take corrective action
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3.4 Business Intelligence 101
whenever KPIs deviate from their target values. To link strategic and operational
perspectives, users must be able to drill down from highly consolidated or summa-
rized figures into the detailed numbers from which they were derived to perform
in-depth analyses.
BI ARCHITECTURE AND ANALYTICS
BI architecture is undergoing technological advances in response to big data and
the performance demands of end-users (Watson, 2012). BI vendors are facing the
challenges of social, sensor, and other newer data types that must be managed
and analyzed. One technology advance that can help handle big data is BI in the
cloud. Figure 3.19 lists the key factors contributing to the increased use of BI. It
can be hosted on a public or private cloud. With a public cloud, a service provider
hosts the data and/or software that are accessed via an Internet connection. For
private clouds, the company hosts its own data and software, but uses cloud-based
technologies.
For cloud-based BI, a popular option offered by a growing number of BI tool
vendors is software as a service (SaaS). MicroStrategy offers MicroStrategy Cloud,
which provides fast deployment with reduced project risks and costs. This cloud
approach appeals to small and midsize companies that have limited IT staff and
want to carefully control costs. The potential downsides include slower response
times, security risks, and backup risks.
Competitive Analytics in Practice: CarMax
CarMax, Inc. is the nation’s largest retailer of used cars and for a decade has
remained one of FORTUNE Magazine’s 100 Best Companies to Work For. CarMax was the fastest retailer in U.S. history to reach $1 billion in revenues. In 2013 the
company had $11 billion in revenues, representing a 9.6 percent increase above the
Figure 3.19 Four factors contributing to increased use of BI.
Figure 3.20 CarMax is the United States’ largest used- car retailer and a Fortune 500 company.
have created demand for
effortless 24/7 access to
insights.
Smart Devices Everywhere
when they provide insight
that supports decisions
and action.
Data are Big Business
help to ask questions that
were previously unknown
and unanswerable.
Advanced Bl and Analytics
are providing low-cost and
flexible solutions.
Cloud Enabled Bl and Analytics
B lo
o m
b e rg
/G e tt
y Im
ag e s
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102 Chapter 3 Data Management, Big Data Analytics, and Records Management
prior year’s results. The company grew rapidly because of its compelling customer
offer—no-haggle prices and quality guarantees backed by a 125-point inspection
that became an industry benchmark—and auto financing. In 2014 CarMax recruited
for more than 1,200 employee positions in locations across the country in response
to continued growth. CarMax currently operates 131 used car superstores in
64 markets.
CarMax continues to enhance and refine its information systems, which it
believes to be a core competitive advantage. CarMax’s IT includes:
• A proprietary IS that captures, analyzes, interprets, and distributes data about the cars CarMax sells and buys.
• Data analytics applications that track every purchase; number of test drives and credit applications per car; color preferences in every demographic and
region.
• Proprietary store technology that provides management with real time data about every aspect of store operations, such as inventory management,
pricing, vehicle transfers, wholesale auctions, and sales consultant produc-
tivity.
• An advanced inventory management system that helps management antici- pate future inventory needs and manage pricing.
Throughout CarMax, analytics are used as a strategic asset and insights gained
from analytics are available to everyone who needs them.
Questions 1. How has BI improved performance management at Quicken Loans? 2. What are the business benefi ts of BI? 3. What are two data-related challenges that must be resolved for BI to
produce meaningful insight? 4. What are the steps in a BI governance program? 5. What is a business-driven development approach? 6. What does it mean to drill down, and why is it important? 7. What four factors are contributing to increased use of BI? 8. How did BI help CarMax achieve record-setting revenue growth?
All organizations create and retain business records. A record is documentation of a business event, action, decision, or transaction. Examples are contracts, research
and development, accounting source documents, memos, customer/client com-
munications, hiring and promotion decisions, meeting minutes, social posts, texts,
e-mails, website content, database records, and paper and electronic files. Business
documents such as spreadsheets, e-mail messages, and word-processing documents
are a type of record. Most records are kept in electronic format and maintained
throughout their life cycle—from creation to final archiving or destruction by an
electronic records management (ERM) system. ERM systems consist of hardware and software that manage and archive
electronic documents and image paper documents; then index and store them
according to company policy. For example, companies may be required by law
to retain financial documents for at least seven years, product designs for many
3.5 Electronic Records Management
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3.5 Electronic Records Management 103
LEGAL DUTY TO RETAIN BUSINESS RECORDS
Companies need to be prepared to respond to an audit, federal investigation, law-
suit, or any other legal action against them. Types of lawsuits against companies
include patent violations, product safety negligence, theft of intellectual property,
breach of contract, wrongful termination, harassment, discrimination, and many
more.
Because senior management must ensure that their companies comply with
legal and regulatory duties, managing electronic records (e-records) is a strategic
issue for organizations in both the public and private sectors. The success of ERM
depends greatly on a partnership of many key players, namely, senior manage-
ment, users, records managers, archivists, administrators, and most importantly, IT
personnel. Properly managed, records are strategic assets. Improperly managed or
destroyed, they become liabilities.
ERM BEST PRACTICES Effective ERM systems capture all business data and documents at their first touchpoint—data centers, laptops, the mailroom, at customer sites, or remote offices.
Records enter the enterprise in multiple ways—from online forms, bar codes, sensors,
websites, social sites, copiers, e-mails, and more. In addition to capturing the entire
document as a whole, important data from within a document can be captured and
stored in a central, searchable repository. In this way, the data are accessible to support
informed and timely business decisions.
In recent years, organizations such as the Association for Information
and Image Management (AIIM; ww.aiim.org), National Archives and Records
Administration (NARA), and ARMA International (formerly the Association of
Records Managers and Administrators; www.arma.org) have created and published
industry standards for document and records management. Numerous best practices
articles, and links to valuable sources of information about document and records
management, are available on their websites. IT at Work 3.5 describes ARMA’s
generally accepted recordkeeping principles.
ERM BENEFITS Departments or companies whose employees spend most of their day filing or retrieving documents or warehousing paper records can reduce costs significantly
with ERM. These systems minimize the inefficiencies and frustration associated
with managing paper documents and workflows. However, they do not create a
paperless office as had been predicted.
An ERM can help a business to become more efficient and productive by:
• Enabling the company to access and use the content contained in
documents.
• Cutting labor costs by automating business processes.
• Reducing the time and effort required to locate information the business
needs to support decision making.
• Improving the security of content, thereby reducing the risk of intellectual
property theft.
• Minimizing the costs associated with printing, storing, and searching for
content.
decades, and e-mail messages about marketing promotions for a year. The major
ERM tools are workflow software, authoring tools, scanners, and databases.
ERM systems have query and search capabilities so documents can be identified
and accessed like data in a database. These systems range from those designed
to support a small workgroup to full-featured, Web-enabled enterprisewide
systems.
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104 Chapter 3 Data Management, Big Data Analytics, and Records Management
When workflows are digital, productivity increases, costs decrease, compli-
ance obligations are easier to verify, and green computing becomes possible.
Green computing is an initiative to conserve our valuable natural resources
by reducing the effects of our computer usage on the environment. You can
read about green computing and the related topics of reducing an organiza-
tion’s carbon footprint, sustainability, and ethical and social responsibility in
Chapter 14.
ERM FOR DISASTER RECOVERY, BUSINESS CONTINUITY, AND COMPLIANCE
Businesses also rely on their ERM system for disaster recovery and business con-
tinuity, security, knowledge sharing and collaboration, and remote and controlled
access to documents. Because ERM systems have multilayered access capabili-
ties, employees can access and change only the documents they are authorized to
handle.
When companies select an ERM to meet compliance requirements, they should
ask the following questions:
1. Does the software meet the organization’s needs? For example, can the DMS be installed on the existing network? Can it be purchased as a service?
2. Is the software easy to use and accessible from Web browsers, offi ce applications, and e-mail applications? If not, people will not use it.
3. Does the software have lightweight, modern Web and graphical user interfaces that effectively support remote users?
Generally accepted recordkeeping principles are a frame- work for managing business records to ensure that they sup- port an enterprise’s current and future regulatory, legal, risk mitigation, environmental, and operational requirements.
The framework consists of eight principles or best prac- tices, which also support information governance. These principles were created by ARMA International and legal and IT professionals.
• Principle of Accountability. An organization will assign a senior executive to oversee a recordkeeping program; adopt policies and procedures to guide personnel; and ensure program audit ability.
• Principle of Transparency. The processes and activi- ties of an organization’s recordkeeping program will be documented in an understandable manner and available to all personnel and appropriate parties.
• Principle of Integrity. A recordkeeping program will be able to reasonably guarantee the authenticity and reli- ability of records and data.
• Principle of Protection. The recordkeeping program will be constructed to ensure a reasonable level of protection to records and information that are private, confidential, privileged, secret, or essential to business continuity.
• Principle of Compliance. The recordkeeping program will comply with applicable laws, authorities, and the organization’s policies.
• Principle of Availability. Records will be maintained in a manner that ensures timely, efficient, and accurate retrieval of needed information.
• Principle of Retention. Records and data will be main- tained for an appropriate time based on legal, regula- tory, fiscal, operational, and historical requirements.
• Principle of Disposition. Records will be securely dis- posed of when they are no longer required to be main- tained by laws or organizational policies.
IT at Work 3 . 5 Generally Accepted Recordkeeping Principles
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3.5 Electronic Records Management 105
4. Before selecting a vendor, it is important to examine workfl ows and how data, documents, and communications fl ow throughout the company. For example,
know which information on documents is used in business decisions. Once those
needs and requirements are identifi ed, they guide the selection of technology
that can support the input types—that is, capture and index them so they can be
archived consistently and retrieved on-demand.
IT at Work 3.6 describes how several companies currently use ERM. Simply
creating backups of records is not sufficient because the content would not be
organized and indexed to retrieve them accurately and easily. The requirement
to manage records—regardless of whether they are physical or digital—is not
new.
Questions 1. What are business records? 2. Why is ERM a strategic issue rather than simply an IT issue? 3. Why might a company have a legal duty to retain records? Give an
example. 4. Why is creating backups an insuffi cient way to manage an organization’s
documents? 5. What are the benefi ts of ERM?
Here are a few examples of how companies use ERM:
• The Surgery Center of Baltimore stores all medical records electronically, providing instant patient informa- tion to doctors and nurses anywhere and at any time. The system also routes charts to the billing department, which can then scan and e-mail any relevant information to insurance providers and patients. The ERM system helps maintain the required audit trail, including the provision of records when they are needed for legal purposes. How valuable has ERM been to the center? Since it was implemented, business processes have been expedited by more than 50 percent, the costs of these processes have been significantly reduced, and the morale of office employees in the center has improved noticeably.
• American Express (AMEX) uses TELEform, developed by Alchemy and Cardiff Software, to collect and process more than 1 million customer satisfaction surveys every year. The data are collected in templates that consist of
more than 600 different survey forms in 12 languages and 11 countries. AMEX integrated TELEform with AMEX’s legacy system, which enables it to distribute processed results to many managers. Because the survey forms are now readily accessible, AMEX has reduced the number of staff who process these forms from 17 to 1, thereby saving the company more than $500,000 a year.
• The University of Cincinnati provides authorized access to the personnel files of 12,000 active employees and tens of thousands of retirees. The university receives more than 75,000 queries about personnel records every year and then must search more than 3 million records to answer these queries. Using a microfilm system to find answers took days. The solution was an ERM that digi- tized all paper and microfilm documents, without help from the IT department, making them available via the Internet and the university’s intranet. Authorized employees access files using a browser.
IT at Work 3 . 6 ERM Applications
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106 Chapter 3 Data Management, Big Data Analytics, and Records Management
Key Terms
active data warehouse
(ADW)
business analytics
business intelligence (BI)
business record
business-driven
development approach
centralized database
change data capture (CDC)
data entity
data mart
data mining
data warehouse
database
database management
system (DBMS)
decision model
declarative language
distributed database
system
electronic records
management (ERM)
extract, transform, load
(ETL)
enterprise data warehouse
(EDW)
eventual consistency
fault tolerance
HaDoop
information overload
immediate consistency
latency
MapReduce
market share
master data management
(MDM)
NoSQL
online transaction-
processing (OLTP)
systems
online analytical-processing
(OLAP) systems
operating margin
petabyte
relational database
relational management
system (RDBMS)
sentiment analysis
scalability
structured query language
(SQL)
text mining
volatile
1. Visit YouTube.com and search for SAS Enterprise Miner Software Demo in order to assess the features
and benefi ts of SAS Enterprise Miner. The URL is
http://www.youtube.com/watch?v=Nj4L5RFvkMg.
a. View the SAS Enterprise Miner Software demo, which is about 7 minutes long.
b. Based on what you learn in the demo, what skills or expertise are needed to build a predictive model?
c. At the end of the demo, you hear the presenter say that “SAS Enterprise Miner allows end-users
to easily develop predictive models and to
generate scoring to make better decisions about
future business events.” Do you agree that SAS
Enterprise Miner makes it easy to develop such
models? Explain.
d. Do you agree that if an expert develops predictive models, it will help managers make
better decisions about future business events?
Explain.
e. Based on your answers to (c), (d), and (e), under what conditions would you recommend SAS
Enterprise Miner?
2. Research two electronic records management vendors, such as Iron Mountain.
EXPLORE: Online and Interactive Exercises
Assuring Your Learning
1. What are the functions of databases and data ware- houses?
2. How does data quality impact business performance?
3. List three types of waste or damages that data errors can cause.
4. What is the role of a master reference fi le?
5. Give three examples of business processes or opera- tions that would benefi t signifi cantly from having
detailed real time or near real time data and identify
the benefi ts.
6. What are the tactical and strategic benefi ts of big data analytics?
7. Explain the four V’s of data analytics.
8. Select an industry. Explain how an organization in that industry could improve consumer satisfaction
through the use of data warehousing.
9. Explain the principle of 90/90 data use.
10. Why is master data management (MDM) important in companies with multiple data sources?
11. Why would a company invest in a data mart instead of a data warehouse?
12. Why is data mining important?
13. What are the operational benefi ts and competitive advantages of business intelligence?
14. How can ERM decrease operating costs?
DISCUSS: Critical Thinking Questions
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Assuring Your Learning 107
1. Visit Oracle.com. Click the Solutions tab to open the menu; then click Data Warehousing under Technology
Solutions.
a. Select one of the Customer Highlights.
b. Describe the customer’s challenges, why it selected a particular Oracle solution, and how
that solution met their challenge.
2. Visit the Microsoft SQL Server website at Microsoft. com/SQLserver.
a. Click the CloudOS tab and select Customer Stories.
b. Filter the customer stories by selecting Business Intelligence and Data Discovery.
c. Summarize each company’s business problems or challenges and why it selected a particular
solution.
d. What were the benefi ts of the BI solution?
3. Visit Teradata.com. Click Resources and review Video News, and select one of the videos related
to analytics. Explain the benefi ts of the solution
chosen.
4. Spring Street Company (SSC) wanted to reduce the “hidden costs” associated with its paper-intensive
processes. Employees jokingly predicted that if
the windows were open on a very windy day, total
chaos would ensue as thousands of papers started
to fl y. If a fl ood, fi re, or windy day occurred, the
business would literally grind to a halt. The com-
pany’s accountant, Sam Spring, decided to calculate
the costs of its paper-driven processes to identify
their impact on the bottom line. He recognized that
several employees spent most of their day fi ling or
retrieving documents. In addition, there were the
monthly costs to warehouse old paper records. Sam
measured the activities related to the handling of
printed reports and paper fi les. His average esti-
mates were as follows:
a. Dealing with a fi le: It takes an employee 12 minutes to walk to the records room, locate
a fi le, act on it, refi le it, and return to his or
her desk. Employees do this 4 times per day
(5 days per week).
b. Number of employees: 10 full-time employees perform the functions.
c. Lost document replacement: Once per day, a document gets “lost” (destroyed, misplaced, or
covered with massive coffee stains) and must be
recreated. The total cost of replacing each lost
document is $200.
d. Warehousing costs: Currently, document storage costs are $75 per month.
Sam would prefer a system that lets employees
fi nd and work with business documents without leav-
ing their desks. He’s most concerned about the human
resources and accounting departments. These person-
nel are traditional heavy users of paper fi les and would
greatly benefi t from a modern document management
system. At the same time, however, Sam is also risk
averse. He would rather invest in solutions that would
reduce the risk of higher costs in the future. He recog-
nizes that the U.S. PATRIOT Act’s requirements that
organizations provide immediate government access to
records apply to SSC. He has read that manufacturing
and government organizations rely on effi cient docu-
ment management to meet these broader regulatory
imperatives. Finally, Sam wants to implement a disaster
recovery system.
Prepare a report that provides Sam with the
data he needs to evaluate the company’s costly paper-
intensive approach to managing documents. You will
need to conduct research to provide data to prepare
this report. Your report should include the following
information:
1. How should SSC prepare for an ERM if it decides to implement one?
2. Using the data collected by Sam, create a spread- sheet that calculates the costs of handling paper at
SSC based on average hourly rates per employee of
$28. Add the cost of lost documents to this. Then, add
the costs of warehousing the paper, which increases
by 10 percent every month due to increases in vol-
ume. Present the results showing both monthly totals
and a yearly total. Prepare graphs so that Sam can
easily identify the projected growth in warehousing
costs over the next three years.
3. How can ERM also serve as a disaster recovery system in case of fi re, fl ood, or break-in?
4. Submit your recommendation for an ERM solution. Identify two vendors in your recommendation.
ANALYZE & DECIDE: Apply IT Concepts to Business Decisions
a. What are the retention recommendations made by the vendors? Why?
b. What services or solutions does each vendor offer?
3. View the “Edgenet Gain Real time Access to Retail Product Data with In-Memory Technology” video
on YouTube. Explain the benefi t of in-memory
technology.
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108 Chapter 3 Data Management, Big Data Analytics, and Records Management
C A S E 3 . 2 Business Case: Financial Intelligence Fights Fraud
The Financial Crimes Enforcement Network (FinCEN; fi ncen.gov) is a bureau of the Treasury Department and the fi nancial intelligence unit of the United States. The bureau reports to the undersecretary for terrorism and fi nancial intelligence. FinCEN’s mission is to safeguard the fi nancial system from abuses of fi nancial crimes, to institute anti- money laundering (AML) programs, and to promote national security through the collection, analysis, and dissemination of fi nancial intelligence.
Constrained by Defi cient Data Analytics Prior to 2008, FinCEN was not able to effectively gather data, analyze them, and deliver them to users. Data that fi nancial institutions had to report to FinCEN suffered from incon- sistent quality and lack of validation and standardization. When trying to analyze its data, FinCEN was limited to small datasets and simple routines. The bureau could not conduct analysis across massive datasets and lacked capabilities for proactive analysis and trend prediction. Reporting data to agencies was done using numerous offl ine systems. Data had to be cleaned and transformed, thus delaying user access. Analytics and reporting defi cien- cies made it diffi cult for FinCEN to quickly detect new and emerging threats and aid in disrupting criminal enterprises.
FinCEN Upgrades Data Analytics and Query Capabilities In 2008 FinCEN launched a major effort to upgrade its ana- lytics capabilities, IT infrastructure, and databases. Upgraded analytics were needed to better collect and analyze data from multiple sources and provide them to federal, state, and local law enforcement and regulatory authorities. Then in May 2010 FinCEN launched the Bank Secrecy Act (BSA) IT Modernization (IT Mod) program and further improved its IT infrastructure. The IT Mod program has improved data quality and the ability of 9,000 authorized users to access, search, and analyze data. The bureau provides federal, state, and local law enforcement and regulators with
direct access to BSA data. These users make approximately 18,000 queries of the extensive database each day. Additional milestones achieved by FinCEN were:
• Converted 11 years of data from its legacy system to FinCEN’s new System of Record. FinCEN is able to elec- tronically receive, process, and store all FinCEN reports.
• Deployed a new advanced analytics tool that provides FinCEN analysts with improved analytic and examination capabilities.
• Released the FinCEN Query Web-based app, a new search tool accessed by FinCEN analysts, law enforce- ment, intelligence, and regulatory users as of September 2012. FinCEN Query provides real time access to over 11 years of BSA data.
Predictive Capabilities Attack Crimes Consulting fi rm Deloitte helped FinCEN with the massive critical tasks of deploying systems and populating data, providing user access, and ensuring system security. Effective data analytics identify patterns and relationships that reveal potential illicit activity. This intelligence has increased the speed and ability to detect money launderers and terrorist fi nanciers and disrupt their criminal activity.
Sources: Compiled from FinCEN.gov (2014), Fact Sheet Bank Secrecy Act (BSA) IT Modernization (IT Mod) Program (2013), and Deloitte (2014).
Questions 1. Explain FinCEN’s mission and responsibilities. 2. What data and IT problems were limiting FinCEN’s ability
to fi ght fi nancial crime? 3. Describe the IT upgrades and capabilities needed by
FinCEN in order to achieve its mission. 4. On what does fi nancial intelligence depend? 5. Why is the ability to identify patterns and relationships
critical to national security? 6. Research recent fi nancial crimes that FinCEN has detected
and disrupted. Explain the role of data analytics in crime detection.
C A S E 3 . 3 Video Case: Hertz Finds Gold in Integrated Data
Finding CRM gold after integrating customer data, Hertz is dominating the global rental car market by giving customers unique, real time offers through multiple channels, with upwards of 80,000 during peak times. Visit Teradata.com and search for the video “Hertz: Finding Gold in Integrated Data.”
1. Describe Hertz’s new strategy and data solution.
2. How did Hertz strengthen customer loyalty?
3. What did Hertz need to do to its data prior to implementing its new solution?
4. Describe the potential short-term and longer-term business benefi ts of integrated data.
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Brown, B., M. Chui, & J. Manyika. “Are You Ready for the Era
of ‘Big Data?’” McKinsey Quarterly, October 2011.
Burns, E. “Coca-Cola Overcomes Challenges to Seize BI
Opportunities.” TechTarget.com. August 2013.
BusinessIntelligence.com. “How Coca-Cola Takes a Refreshing
Approach on Big Data.” July 18, 2013a.
BusinessIntelligence.com. “Coca-Cola’s Juicy Approach to Big
Data.”July 29, 2013b. http://businessintelligence.com/
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ment: Is Your Data Under Control?” DataFlux.com, 2012.
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Neill, J. “Big Data Demands Big Context.” HBR Blog Network, December 3, 2013.
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References
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110
Chapter Snapshot
The basic technology that makes global communication
possible is the Internet Protocol, or IP. Each device attached to a network has a unique IP address that enables it to send and receive files. Files are broken down into blocks known
as packets in order to be transmitted over a network to their destination, which also has a unique IP address. Most
networks use IP Version 4 (IPv4). In April 2014 ARIN, the group that oversees Internet addresses, reported that IPv4
Networks for Effi cient Operations and Sustainability4
Chapter
1. Describe data networks, their quality-of-service (QOS) issues, and how IP addresses and APIs function. Identify opportunities to apply networked devices to improve operational efficiency and business models.
2. Describe wireless 3G and 4G networks, mobile network infrastructure, and how they support worker productivity, business operations, and strategy.
3. Evaluate performance improvements from virtual collaboration and communication technologies, and explain how they support group work.
4. Describe how companies can contribute to sustainability, and green, social, and ethical challenges related to the use and operations of IT networks.
Chapter Snapshot Case 4.1 Opening Case: Sony Builds an IPv6 Network to Fortify Competitive Edge
4.1 Data Networks, IP Addresses, and APIs 4.2 Wireless Networks and Mobile Infrastructure 4.3 Collaboration and Communication
Technologies 4.4 Sustainability and Ethical Issues
Key Terms
Assuring Your Learning
• Discuss: Critical Thinking Questions • Explore: Online and Interactive Exercises • Analyze & Decide: Apply IT Concepts
to Business Decisions
Case 4.2 Business Case: Google Maps API for Business
Case 4.3 Video Case: Fresh Direct Connects for Success
References
Learning Outcomes
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111
C A S E 4 . 1 O P E N I N G C A S E Sony Builds an IPv6 Network to Fortify Competitive Edge
IPv4
IPv6
32 bit address
0000.0000.0000.0000
128 bit address
0000.0000.0000.0000.0000.0000.0000.0000
Figure 4.1 IPv4 addresses have 4 groups of four alphanumeric characters, which allows for 232 or roughly 4.3 billion unique IP address. IPv6 addresses have 8 groups of alphanumerics, which allows for 2128, or 340 trillion, trillion, trillion addresses. IPv6 offers also enhanced quality of service that is needed by the latest in video, interactive games, and e-commerce.
addresses were running out—making it urgent that enterprises move to the newer IPv6
(Figure 4.1). Enterprises need to prepare for the 128-bit protocol because switching
from IPv4 to IPv6 is not trivial.
Networking is undergoing tremendous change. The convergence of access tech-
nologies, cloud, advanced 4G networks, multitasking mobile operating systems, and
collaboration platforms continues to change the nature of work, the way we do busi-
ness, how machines interact, and other things not yet imagined. The downsides of
such massive use of energy-dependent wired and wireless networks are their carbon
footprints and damage to the environment as well as personal privacy. Intelligently
planned sustainability efforts can reduce the depletion of the earth’s natural resources
significantly. Efforts to protect what is left of personal privacy are less successful.
Internet Protocol (IP) is the method by which data are sent from one device to another via a network.
IP address. Every device that communicates with a network must have a unique identify- ing IP address. An IP address is comparable to a telephone number or home address.
IP Version 4 (IPv4) has been Internet protocol for over three decades, but has reached the limits of its design. It is difficult to configure, it is running out of addressing space, and it provides no features for site renumbering to allow for an easy change of Internet Service Provider (ISP), among other limitations.
Figure 4.2 Sony Corporation overview.
SONY’S RAPID BUSINESS GROWTH
In the early 2000s, Sony Corporation had been engaged in strategic mergers and
acquisitions to strengthen itself against intensifying competition. By 2007 Sony’s
enterprise network (internal network) had become too complex and incapable
Sony Corporation
Global Reach
Network Solution
Sony aims to accelerate global
collaboration and business across
business units to achieve goal of
“One Sony.”
Cisco Enterprise IPv6 network
integrated with IPv4 network.
Consumer electronics equipment
and services; music, pictures,
computer entertainment.
More versatile network
Network without communications
constraints, supporting “One Sony”
through information systems.
Brand
Business Results
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112 Chapter 4 Networks for Efficient Operations and Sustainability
TABLE 4.1 Opening Case Overview
Company Sony Corporation, Sony.com
Location Headquartered in Tokyo, Japan. Over 700 total network sites worldwide.
Industries One of the largest consumer electronics and enter- tainment companies in the world, including audio/
video equipment, semiconductors, computers,
and video games. Also engaged in production and
distribution of recorded music, motion picture,
and video.
Business challenges • Network expansion required too much time due to complexity of enterprise network.
• Networking TCO (total cost of ownership) was
continually increasing.
• Numerous constraints on networks obstructing
communication between companies in Sony
Group.
Network technology Integrated its IPv4 networks with new IPv6
solution solutions from Cisco. The integrated IPv4/IPv6 network has been used by Sony as infrastructure
for the development of new products and enterprise-
wide collaboration.
Sony also upgraded its Cisco switches at the
corporate data center, campuses, and remote offi ces
to handle concurrent IPv4 and IPv6 traffi c.
NETWORK LIMITATIONS
of supporting communication, operations, and further business growth. The
enterprise network was based on IPv4. A serious limitation was that the IPv4
network could not provide real time collaboration among business units and
group companies.
Expansion efforts were taking too long because of the complicated structure
of the network, and total cost of ownership (TCO) was increasing. Also, a number
of technical limitations were blocking internal communications.
Many of the Sony Group companies had developed independently—and had inde-
pendent networks. Devices connected to the independent networks were using the
same IP addresses. That situation is comparable to users having duplicate telephone
numbers—making it impossible to know which phone was being called. Also,
phones with the same number could not call each other.
Once these networks were integrated, the duplicate IP address caused traffic-
routing conflicts. Routing conflicts, in turn, led to the following problems:
1. Sony’s employee communication options were severely limited, which harmed productivity.
2. File sharing and real time communication were not possible.
3. Introducing cloud services was diffi cult and time-consuming.
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4.1 Data Networks, IP Addresses, and APIs 113
MIGRATION TO IPV6 NETWORKS: AN INVESTMENT IN THE FUTURE
To eliminate these limitations, Sony decided to invest in IPv6-based networks in
2006; it then launched a full-scale effort in 2008. With its virtually unlimited number
of IP addresses, IPv6 would support Sony’s long-term, next-generation information and communications technology (ICT) infrastructure strategy and improve collabo- ration and productivity.
Migrating from IPv4 to IPv6 involved 700 sites, hundreds of thousands of net-
working devices, and hundreds of thousands of network users spread around the
globe. During the transition, Sony realized that it was necessary to support both IP
protocols. That is, the IPv6 would supplement and coexist with the existing enter-
prise IPv4 network, rather than replace it. Running both protocols on the same
network at the same time was necessary because Sony’s legacy devices and apps
only worked on IPv4.
Sony selected Cisco as a key partner in the migration and integration of IPv4
and IPv6 traffic because of the maturity of its IPv6 technology. The integrated net-
work has been used by Sony as infrastructure for product development. Sony also
upgraded its Cisco network switches at the corporate data center, campuses, and
remote offices to handle concurrent IPv4 and IPv6 traffic.
BUSINESS RESULTS The use of IPv6 eliminated the issue of conflicting IP addresses, enabling Sony employees in all divisions to take advantage of the productivity benefits of real time
collaboration applications. Other business improvements are:
• Flexibility to launch new businesses quickly
• Reduced TCO of enterprise network
• Network without communications constraints, supporting “One Sony” through
information systems
Sources: Compiled from Cisco (2014a; 2014b), Khedekar (2012), and AT&T (2012).
Questions 1. Why might IPv6 be a business continuity issue for organizations? 2. Explain how Sony’s IPv4 enterprise network was restricting the
productivity of its workers. 3. What problems did duplicate IP addresses cause at Sony? Give an
analogy. 4. Why did Sony need to run both protocols on its network instead of
replacing IPv4 with IPv6? 5. Describe the strategic benefi t of Sony’s IPv6 implementation. 6. Do research to determine the accuracy of this prediction: “Today,
almost everything on the Internet is reachable over IPv4. In a few years, both IPv4 and IPv6 will be required for universal access.”
Managers now need to understand the technical side of networks, IP addressing,
and APIs in order to make intelligent investment decisions that impact operations
and competitive position. Enterprises run on networks—wired and mobile—and
depend upon their ability to interface with other networks and applications.
4.1 Data Networks, IP Addresses, and APIs
IP Version 6 (IPv6) is replacing IPv4 because it has run out of IP addresses.
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114 Chapter 4 Networks for Efficient Operations and Sustainability
Networks are changing significantly with the shift to IPv6 and the build-out of 5G networks. 5G will offer huge gains in both speed and capacity over existing 4G
networks—along with opportunities at the operations and strategic levels. At the
2014 Mobile World Congress in Barcelona, Neelie Kroes, the vice president of the European Commission, discussed how deploying 5G networks could reduce high-
level youth unemployment across Europe. In the short term, the 5G infrastructure
build-out would create new jobs. In the longer term, 5G would create entirely
new markets and economic opportunities driven by superior mobile capabilities in
industries ranging from health care to automotive (Basulto, 2014).
5G (fifth generation), the next-generation mobile communications network.
FUNDAMENTALS OF DATA NETWORKS
The capacity and capabilities of data networks provide opportunities for more
automated operations and new business strategies. M2M communications over
wireless and wired networks automate operations, for instance, by triggering
action such as sending a message or closing a valve. The speed at which data can
be sent depends on the network’s bandwidth. Bandwidth characteristics are shown in Figure 4.3.
Bandwidth is the capacity or throughput per second of a network.
TECH NOTE 4.1 4G and 5G Networks in 2018
More Mobile Network Traffi c and Users
Cisco predicts that by 2018, global mobile data traffi c will have increased 11 times
from current levels. Much of that traffi c will be driven by billions of devices talking
to other devices wirelessly. This includes a major increase in M2M communications
and the number of wearable technology devices. The number of mobile data con-
nections will total more than 10 billion by 2018—8 billion of which will be personal
mobile devices and 2 billion M2M connections.
Faster Mobile Network Speeds
Cisco expects that the average global mobile network speed will almost double from
1.4 Mbps in 2013 to 2.5 Mbps by 2018. And 5G networks are promising speeds that
will be 100 times faster than mid-2014 speeds.
Figure 4.3 Network bandwidth.
Bandwidth is the communication
capacity of a network.
Bandwidth is the amount of data that
passes through a network connection
over time as measured in bits per
second (bps).
Bandwidth is used in both
directions—for uploads and
downloads.
Very large data transfers reduce
availability for everyone on the
network.
Network speed depends on
amount of traffic. Data flows
quickly and smoothly when
traffic volume on the network
is small relative to its capacity.
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4.1 Data Networks, IP Addresses, and APIs 115
Figure 4.4 Four drivers of global mobile traffi c through 2018.
Figure 4.5 Basic functions of business networks.
FUNCTIONS SUPPORTED BY BUSINESS NETWORKS
Figure 4.5 describes the basic functions of business networks: communication,
mobility, collaboration, relationships, and search. These functions depend on net-
work switches and routers—devices that transmit data packets from their source to their destination based on IP addresses. A switch acts as a controller, enabling
networked devices to talk to each other efficiently. For example, switches connect
computers, printers, and servers within an office building. Switches create a net-
work. Routers connect networks. A router links computers to the Internet, so users
HIGH DEMAND FOR HIGH-CAPACITY NETWORKS
As described in Tech Note 4.1, global mobile data traffic is increasing. The four
drivers of that demand are shown in Figure 4.4. Demand for high-capacity networks
is growing at unprecedented rates. Examples of high-capacity networks are wireless
mobile, satellite, wireless sensor, and VoIP (voice over Internet Protocol) such as
Skype. Voice over IP (VoIP) networks carry voice calls by converting voice (analog signals) to digital signals that are sent as packets. With VoIP, voice and data trans-
missions travel in packets over telephone wires. VoIP has grown to become one of
the most used and least costly ways to communicate. Improved productivity, flex-
ibility, and advanced features make VoIP an appealing technology.
Over 10 billion
2.5 megabits per second (Mbps)
Almost 5 billion
70% of mobile traffic
By 2018
More Mobile Connections
Faster Mobile Speeds
More Mobile Users
More Mobile Video
Communication
Mobility Provides secure, trusted,
and reliable access from any
mobile device anywhere at
satisfactory download and
upload speeds.
Relationships Manages interaction with
customers, supply chain
partners, shareholders,
employees, regulatory
agencies, and so on.
Search Able to locate data, contracts,
documents, spreadsheets, and other
knowledge within an organization
easily and efficiently.
Collaboration Supports teamwork
that may be synchronous
or asynchronous;
brainstorming; and
knowledge and
document sharing.
Provides sufficient capacity for human
and machine-generated transmissions.
Delays are frustrating, such as when
large video files pause during download
waiting for the packets to arrive.
Buffering means the network cannot handle the speed at which the video is
being delivered and therefore stops to
collect packets.
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116 Chapter 4 Networks for Efficient Operations and Sustainability
can share the connection. Routers act like a dispatcher, choosing the best paths for
packets to travel.
Investments in data networks, IP addresses, routers, and switches are business
decisions because of their impact on productivity, security, user experiences, and
customer service.
QUALITY OF SERVICE An important management decision is the network’s quality of service (QoS), especially for delay-sensitive data such as real time voice and high-quality video.
The higher the required QoS, the more expensive the technologies needed to man-
age organizational networks. Bandwidth-intensive apps are important to business
processes, but they also strain network capabilities and resources. Regardless of the
TECH NOTE 4.2 Circuit and Packet Switching
All generations of networks are based on switching. Prior to 4G, networks included
circuit switching, which is slower than packet switching. 4G was fi rst to be fully
packet switched, which signifi cantly improved performance. The two basic types of
switching are:
Circuit switching: A circuit is a dedicated connection between a source and destina- tion. In the past, when a call was placed between two landline phones, a circuit or
connection was created that remained until one party hung up. Circuit switching
is older technology that originated with telephone calls; it is ineffi cient for digital
transmission.
Packet switching: Packet switching transfers data or voice in packets. Files are bro- ken into packets, numbered sequentially, and routed individually to their destina-
tion. When received at the destination, the packets are reassembled into their proper
sequence.
Wireless networks use packet switching and wireless routers whose antennae trans-
mit and receive packets. At some point, wireless routers are connected by cables to
wired networks, as shown in Figure 4.6.
Figure 4.6 Network cables plug into a wireless router. The antennae create wireless access points (WAP). ©
M e tt
a d
ig it
al /A
la m
y
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4.1 Data Networks, IP Addresses, and APIs 117
type of traffic, networks must provide secure, predictable, measurable, and some-
times guaranteed services for certain types of traffic. For example, QoS technolo-
gies can be applied to create two tiers of traffic:
• Prioritize traffic: Data and apps that are time-delay-sensitive or latency- sensitive apps, such as voice and video, are given priority on the network.
• Throttle traffic: In order to give latency-sensitive apps priority, other types of traffic need to be held back (throttled).
The ability to prioritize and throttle network traffic is referred to as traffic shaping and forms the core of the hotly debated Net neutrality issue, which is discussed in
IT at Work 4.1.
In 2014 the battle over the complicated issue of net neu- trality heated up. Net neutrality is a principle that Internet Service Providers (ISPs) and their regulators treat all Internet traffic the same way. On the opposing side of that issue is traffic shaping. Traffic shaping creates a two-tier system for specific purposes such as:
1. Time-sensitive data are given priority over traffic that can be delayed briefly with little-to-no adverse effect. Companies like Comcast argue that Net neutrality rules hurt consumers. Certain applications are more sensi- tive to delays than others, such as streaming video and Internet phone services. Managing data transfer makes it possible to assure a certain level of performance or QoS.
2. In a corporate environment, business-related traffic may be given priority over other traffic, in effect, by paying a premium price for that service. Proponents of traffic shaping argue that ISPs should be able to charge more to customers who want to pay a premium for priority service.
Specifically, traffic is shaped by delaying the flow of less important network traffic, such as bulk data transfers, P2P file-sharing programs, and BitTorrent traffic.
Traffic shaping is hotly debated by those in favor of Net neutrality. They want a one-tier system in which all Internet data packets are treated the same, regardless of their con- tent, destination, or source. In contrast, those who favor the two-tiered system argue that there have always been differ- ent levels of Internet service and that a two-tiered system would enable more freedom of choice and promote Internet- based commerce.
Federal Communications Commission’s 2010 Decision
On December 21, 2010, the Federal Communications Commission (FCC) approved a compromise that created two classes of Internet access: one for fixed-line providers and the other for the wireless Net. In effect, the new rules are Net semi-neutrality. The FCC banned any outright blocking of and “unreasonable discrimination” against websites or applications by fixed-line broadband providers. But the rules do not explicitly forbid “paid prioritization,” which would allow a company to pay an ISP for faster data transmission.
Net Semi-Neutrality Overturned in 2014
In January 2014 an appeals court struck down the FCC’s 2010 decision. The court allowed ISPs to create a two-tiered Internet, but promised close supervision to avoid anticom- petitive practices, and banned “unreasonable” discrimina- tion against providers.
On April 24 FCC Chairman Tom Wheeler reported that his agency would propose new rules to comply with the court’s decision that would be finalized by December 2014. Wheeler stated that these rules “would establish that behavior harmful to consumers or competition by limiting the openness of the Internet will not be permitted” (Wheeler, 2014). But Wheeler’s proposal would allow network owners to charge extra fees to content providers. This decision has angered consumer advocates and Net neutrality advocates who view Wheeler with suspicion because of his past work as a lobbyist for the cable industry and wireless phone companies.
Sources: Compiled from Federal Communications Commission (fcc.
gov, 2014), Wheeler (2014), and various blog posts.
IT at Work 4 . 1 Net Neutrality Debate Intensifies
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118 Chapter 4 Networks for Efficient Operations and Sustainability
Questions
1. What is Net neutrality? 2. What tiers are created by traffic shaping? 3. Why did the battle over Net neutrality intensify in 2014?
4. Did the FCC’s 2010 ruling favor either side of the debate? Explain.
5. What has been a reaction to the 2014 appeals court deci- sion? Explain.
NETWORK TERMINOLOGY
To be able to evaluate networks and the factors that determine their functionality,
you need to be familiar with the following network basics:
• Bandwidth: Bandwidth depends on the network protocol. Common wire- less protocols are 802.11b. 802.11g, 802.11n, and 802.16. For an analogy to
bandwidth, consider a pipe used to transport water. The larger the diameter
of the pipe, the greater the throughput (volume) of water that flows through
it and the faster water is transferred through it.
• Protocol: Protocols are rules and standards that govern how devices on a network exchange data and “talk to each other.” An analogy is a country’s
driving rules—whether to drive on the right or left side of the road.
• TCP/IP: Transmission control protocol/Internet protocol (TCP/IP) is the basic communication protocol of the Internet. This protocol is supported
by every major network operating system (OS) to ensure that all devices on
the Internet can communicate. It is used as a communications protocol in a
company’s private network for internal uses.
• Fixed-line broadband: Describes either cable or DSL Internet connections.
• Mobile broadband: Describes various types of wireless high-speed Internet access through a portable modem, telephone, or other device.
• 3G: 3G networks support multimedia and broadband services, do so over a wider distance, and at faster speeds than prior 1G and 2G generations. 3G
networks have far greater ranges because they use large satellite connec-
tions to telecommunication towers.
• 4G: 4G mobile network standards enable faster data transfer rates. 4G net- works are digital, or IP, networks.
Overall, 2G networks were for voice, 3G networks for voice and data, and 4G net-
works for broadband Internet connectivity.
TECH NOTE 4.3 Origin of the Internet, e-mail, and TCP/IP
The Advanced Research Projects Agency network (ARPAnet) was the fi rst real net-
work to run on packet-switching technology. In October 1969 computers at Stanford
University, UCLA, and two other U.S universities connected for the fi rst time—
making them the fi rst hosts on what would become the Internet. ARPAnet was
designed for research, education, and government agencies. ARPAnet provided a
communications network linking the country in the event that a military attack or
nuclear war destroyed conventional communications systems.
In 1971 e-mail was developed by Ray Tomlinson, who used the @ symbol to
separate the username from the network’s name, which became the domain name.
Broadband (short for broad bandwidth) means high- capacity or high-speed network.
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4.1 Data Networks, IP Addresses, and APIs 119
On January 1, 1983, ARPAnet computers switched over to the TCP/IP protocols
developed by Vinton Cerf. A few hundred computers were affected by the switch.
The original ARPAnet protocol had been limited to 1,000 hosts, but the adoption of
the TCP/IP standard made larger numbers of hosts possible. The number of Internet
hosts reached nearly 1 billion by 2013.
3G AND 4G 4G delivers average realistic download rates of 3 Mbps or higher (as opposed to theoretical rates, which are much higher). In contrast, today’s 3G networks typically deliver average download speeds about one-tenth of that rate. Even though individ-
ual networks, ranging from 2G to 3G, started separately with their own purposes,
soon they will be converted to the 4G network.
4G is based purely on the packet-based IP—unlike 2G and 3G that have a
circuit-switched subsystem. Users can obtain 4G wireless connectivity through one
of the following standards:
1. WiMAX is based on the IEEE 802.16 standard. IEEE 802.16 specifi cations are:
• Range: 30 miles (50 km) from base station
• Speed: 70 megabits per second (Mbps)
• Line-of-sight not needed between user and base station
WiMAX operates on the same basic principles as Wi-Fi in that it transmits data
from one device to another via radio signals.
2. Long-Term Evolution (LTE) is a GSM-based technology that is deployed by Verizon, AT&T, and T-Mobile. LTE capabilities include:
• Speed: Downlink data rates of 100 Mbps and uplink data rates of 50 Mbps
Improved network performance, which is measured by its data transfer capacity, provides fantastic opportunities for mobility, mobile commerce, collaboration,
supply chain management, remote work, and other productivity gains.
BUSINESS USES OF NEAR-FIELD COMMUNICATION
Near-field communication (NFC) enables two devices within close proximity to establish a communication channel and transfer data through radio waves. NFC are
location-aware technologies that are more secure than other wireless technologies
like Bluetooth and Wi-Fi. Unlike RFID, NFC is a two-way communication tool.
Location-aware NFC technology can be used to make purchases in restaurants,
resorts, hotels, theme parks and theaters, at gas stations, and on buses and trains.
Here are some examples of NFC applications and their potential business value.
• The Apple iWatch wearable device with NFC communication capabilities
could be ideal for mobile payments. Instead of a wallet, users utilize their
iWatch as a credit card or wave their wrists to pay for their Starbucks coffee.
With GPS and location-based e-commerce services, retailers could send a
coupon alert to the iWatch when a user passes their store. Consumers would
then see the coupon and pay for the product with the iWatch.
• Ticketmaster Spain teamed up with Samsung to offer NFC tickets to a Dum
Dum Girls concert in Madrid. Consumers needed to download the NFC
Ticketmaster app from the Samsung app store to purchase a ticket, which
was then stored in their phone for secure entry at the door.
• International fresh produce distributor Total Produce plans to give consum-
ers access to videos, recipes, and interactive games about the benefits of a
healthy diet via NFC tag–equipped SmartStands located in supermarkets
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120 Chapter 4 Networks for Efficient Operations and Sustainability
and convenience stores. “We can upload a new video to these units instantly
to respond to opportunities; a barbecue-themed video on a sunny afternoon,
a pumpkin carving video for Halloween or a recipe video to complement an
in-store price promotion,” says Vince Dolan, European marketing manager
at Total Produce. “Similarly, we can update grower videos to reflect changes
in product range at any time” (Boden, 2014).
• Passengers on public transportation systems can pay fares by waving an
NFC smartphone as they board.
Mashup of GPS and Bluetooth
The mashup of GPS positioning and short-range wireless technologies, such as Bluetooth and Wi-Fi, can provide unprecedented intelligence. These technologies create opportunities for companies to develop solutions that make a consumer’s life
better. They could, for example, revolutionize traffic and road safety. Intelligent
transport systems being developed by car manufacturers allow cars to communicate
with each other and send alerts about sudden braking. In the event of a collision,
the car’s system could automatically call emergency services. The technology could
also apply the brakes automatically if it was determined that two cars were getting
too close to each other.
Advancements in networks, devices, and RFID sensor networks are changing
enterprise information infrastructures and business environments dramatically. The
preceding examples and network standards illustrate the declining need for a physi-
cal computer, as other devices provide access to data, people, or services at any
time, anywhere in the world, on high-capacity networks.
Fans attending gigs by The Wild Feathers were given gui- tar picks embedded with an NFC tag. Warner Music had distributed the guitar picks for fans to enter a competition, share content via social media, and vote at the gig simply by tapping with an NFC phone. NFC-embedded picks were inserted into the band’s promotional flyers at six European venues. Each pick was encoded with a unique URL and also printed with a unique code for iPhone users to enable track- ing and monitoring.
Marketing Campaign Success Shows an Exciting Future for NFC
The tags generated a high response rate. Over 65 percent of the NFC guitar picks had registered in the competition. And 35 percent of the fans had shared content on social media— spending an average of five minutes on the site.
NFC is being used in marketing campaigns because the technology offers slick one-tap interaction. NFC allows brands to engage with their customers in unique ways and
create exciting user experiences. With millions of NFC- equipped smartphones set to reach users over the next few years and the technology’s advantages for shoppers and businesses, NFC is emerging as a major technology.
Questions
1. Assume you attended a concert and were given a bro- chure similar to the one distributed to fans at The Wild Feathers concert. Would you use the guitar pick or com- parable NFC-embedded item to participate in a contest? To post on Facebook or tweet about the concert? Explain why or why not.
2. How can NFC be applied to create an interesting user experience at a sporting event? At a retail store or coffee shop?
3. Refer to your answers in Question 2. What valuable information could be collected by the NFC tag in these businesses?
IT at Work 4 . 2 NFC-Embedded Guitar Picks
Mashup is a general term referring to the integration of two or more technologies.
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4.1 Data Networks, IP Addresses, and APIs 121
APPLICATION PROGRAM INTERFACES AND OPERATING SYSTEMS
When software developers create applications, they must write and compile the
code for a specific operating system (OS). Figure 4.7 lists the common OSs. Each
OS communicates with hardware in its own unique way; each OS has a specific API
that programmers must use. Video game consoles and other hardware devices also
have APIs that run software programs.
What Is an API?
An API consists of a set of functions, commands, and protocols used by program-
mers to build software for an OS. The API allows programmers to use predefined
functions or reusable codes to interact with an OS without having to write a soft-
ware program from scratch. APIs simplify the programmer’s job.
APIs are the common method for accessing information, websites, and data-
bases. They were created as gateways to popular apps such as Twitter, Facebook,
and Amazon and enterprise apps provided by SAP, Oracle, NetSuite, and many
other vendors.
Automated API
The current trend is toward automatically created APIs that are making innovative
IT developments possible. Here are two examples of the benefits of automated APIs:
• Websites such as the European Union Patent office have mappings of every
one of their pages to both URLs for browser access and URLs for REST
APIs. Whenever a new page is published, both access methods are sup-
ported.
• The startup SlashDB offers the capability to automatically create an API
to access data in a SQL database. This API simplifies many of the details
of SQL usage and makes it much easier for developers to get at the data
(Woods, 2013a and 2013b).
Application program inter- face (API). An interface is the boundary where two sepa- rate systems meet. An API provides a standard way for different things, such as software, content, or web- sites, to talk to each other in a way that they both under- stand without extensive programming.
TECH NOTE 4.4 Spotify Released Its API to Developers
In early 2012 the digital music service Spotify released its API to developers. The
developers quickly created hundreds of apps that fans are using to discover and share
new music. The most popular new app is Tunigo, which uses its music experts to curate
playlists targeted to various moods. The Tunigo service was so successful that Spotify
bought it and made it part of Browse. Browse is the in-house curation department that searches Spotify’s catalog and continuously delivers new playlists (Dean, 2013).
Browse lets you search for specifi c playlists based on your mood. These playlists
are created by other users and selected by Spotify staffers. Users have created over
1 billion playlists, which grow and morph every day.
Android
iOS
Windows Phone
Common Mobile OS
Windows
Mac OS X
Linux
Common Desktop OS Figure 4.7 Common mobile and desktop operating systems. Each computer OS provides an API for programmers. Mobile OSs are designed around touchscreen input.
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122 Chapter 4 Networks for Efficient Operations and Sustainability
API Value Chain in Business
APIs deliver more than half of all the traffic to major companies like Twitter and
eBay. APIs are used to access business assets, such as customer information or a
product or service, as shown in Figure 4.8. IT developers use APIs to quickly and
easily connect diverse data and services to each other. APIs from Google, Twitter,
Amazon, Facebook, Accuweather, Sears, and E*Trade are used to create many
thousands of applications. For example, Google Maps API is a collection of APIs
used by developers to create customized Google Maps that can be accessed on a
Web browser or mobile devices.
The API value chain takes many forms because the organization that owns the
business asset may or may not be the same as the organization that builds the APIs.
Different people or organizations may build, distribute, and market the applica-
tions. At the end of the chain are end-users who benefit from the business asset.
Often, many APIs are used to create a new user experience.
The business benefits of APIs are listed in Table 4.2.
The power of Spotify was demonstrated in 2013 when it won a challenge with
Pink Floyd to gain access to the group’s entire music collection: every track from
every album the band had ever released. The Pink Floyd collection became part of
Spotify’s streaming music service when the tracks were offi cially “unlocked” in June
2013. The challenge was for the song “Wish You Were Here” to be streamed by fans
over 1 million times in just a few days. Pink Floyd members fulfi lled the deal.
Figure 4.8 API value chain in business.
TABLE 4.2 Business Benefi ts of APIs
APIs are channels to new customers and markets: APIs enable partners to use business assets to extend the reach of a company’s products or services to
customers and markets they might not reach easily.
APIs promote innovation: Through an API, people who are committed to a challenge or problem can solve it themselves.
APIs are a better way to organize IT: APIs promote innovation by allowing everyone in a company to use each other’s assets without delay.
APIs create a path to lots of Apps: Apps are going to be a crucial channel in the next 10 years. Apps are powered by APIs. Developers use APIs and
combinations of APIs to create new user experiences.
API Developers
Provides quick,
easy access to
business assets.
Applications
created using
APIs
Use APIs to
create new
business.
Business Assets
Data
information
products
services
Customers, employees,
and end-users use the
business apps that give
them access to assets.
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4.2 Wireless Networks and Mobile Infrastructure 123
In the 21st-century global economy, advanced wireless networks are a foundation
on which global economic activity takes place. Current 3G and 4G networks and
technologies provide that foundation, moving entire economies. For any nation to
stay competitive and prosperous, it is imperative that investment and upgrades in
these technologies continue to advance to satisfy demand.
Global mobile data traffic is forecasted to increase nearly 11-fold between
2013 and 2018. Mobile data traffic will grow at a compound annual growth rate
(CAGR) of 61 percent from 2013 to 2018, reaching 15.9 exabytes (EB) per month
by 2018, according to the Cisco Visual Networking Index (VNI): Global Mobile Data Traffic Forecast Update, 2013–2018. Mobile data traffic will reach the following milestones:
1. The average mobile connection speed will surpass 2 Mbps by 2016.
2. Smartphones will reach 66 percent of mobile data traffi c by 2018.
3. Monthly mobile tablet traffi c will surpass 2.5 EB per month by 2018.
4. Tablets will exceed 15 percent of global mobile data traffi c by 2016.
5. 4G traffi c will be more than half of total mobile traffi c by 2018.
6. There will be more traffi c offl oaded from cellular networks and onto Wi-Fi net- works than remains on cellular networks by 2018.
7. In addition to supporting mobile users, increased bandwidth is needed to sup- port the numerous industrial applications that leverage wireless technologies—
primarily the smart grid, or smart energy, and health-care segments.
With a combination of smart meters, wireless technology, sensors, and soft-
ware, the smart grid allows utilities to accurately track power grids and cut back
on energy use when the availability of electricity is stressed. And consumers
gain insight into their power consumption to make more intelligent decisions
about how to use energy. A fully deployed smart grid has the potential of saving
between $39.69 and $101.57, and up to 592 pounds of carbon dioxide emissions,
per consumer per year in the United States, according to the Smart Grid Consumer
Collaborative (SGCC).
Wireless hospitals and remote patient monitoring, for example, are grow-
ing trends. Tracking medical equipment and hospital inventory, such as gurneys,
is done with RFID tagging at a number of hospitals. Remote monitoring apps
are making health care easier and more comfortable for patients while reaching
patients in remote areas.
4.2 Wireless Networks and Mobile Infrastructure
Questions 1. Why has IPv6 become increasingly important? 2. What is an IP address? 3. What are bandwidth and broadband? 4. Briefl y described the basic network functions. 5. What is the difference between circuit switching and packet switching? 6. What is the difference between 3G and 4G? 7. What are the mobile network standards? 8. Explain the Net neutrality debate. 9. What are two applications of NFC? 10. What are the benefi ts of APIs?
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124 Chapter 4 Networks for Efficient Operations and Sustainability
In the small city of Santander on Spain’s Atlantic coast, Mayor Iñigo de la Serna raised $12 million, mostly from the European Commission, to launch SmartSantander. SmartSantander is a smart city experiment that is improving the quality of life, reducing energy consumption, and engag- ing its citizens in civic duties.
10,000 Sensors Embedded
The city implemented wireless sensor networks and embed- ded 10,000 sensors in its streets and municipal vehicles to monitor garbage collection, crime, and air quality and manage street lighting for better energy efficiency. Sensors communi- cate with smartphone apps to inform drivers and commuters on parking availability, bus delays, road closures, and the current pollen count in real time. Parking apps direct drivers to available spaces via cell phone alerts. Drivers benefit from a reduction in the time and annoyance of finding parking spots. Anyone can feed his or her own data into the system by, for example, snapping a smartphone photo of a pothole or broken streetlight to notify the local government that a problem needs to be fixed.
Build-Out of Smart City Applications
This mobile technology can help cities contribute to a greener planet. Municipal landscape sprinklers can send facts to city agencies for analysis to conserve water usage. Sensors can monitor weather and pollen counts as well as water and power leaks.
Police State
The data streams and mobile apps that keep citizens informed also keep the government informed. What is the difference between a smart city and a police state? Consider
how data collected from sensors mounted outside a bar to track noise levels might be used.
• Scenario #1. Instances of loud noises and squealing tires are transmitted to local police. The city uses the informa- tion to enforce public nuisance laws and make arrests.
• Scenario #2: People who live in the neighborhood show civic leaders what is keeping them up at night and receive help in resolving the problem.
• Scenario #3: Landlords could use data showing less noise and cleaner air to promote their apartments or office buildings.
The Dark Side of Smart
The wireless networks and sensors need to be maintained. Thousands of batteries embedded in roadways could have expensive and disruptive maintenance requirements.
Parking space alerts might create other annoyances. If everyone becomes aware of a parking spot up the street, the rush of cars converging on a few open locations could lead to rage and defeat the purpose of such an alert.
Sources: Compiled from O’Connor (2013) and Edwards (2014).
Questions
1. What are the benefits of a smart city? 2. What are the potential abuses of data collected in this way? 3. Consider the dark side of smart. Are you skeptical of the
benefits of a smart city? 4. Would you want to live in a smart city? Explain. 5. How would you prevent Santander from becoming a
police state?
IT at Work 4 . 3 Smart City or Police State?
STRATEGIC BUILD- OUT OF MOBILE CAPABILITIES
Enterprises are moving away from the ad hoc adoption of mobile devices and
network infrastructure to a more strategic planning build-out of their mobile capa-
bilities. As technologies that make up the mobile infrastructure evolve, identifying
strategic technologies and avoiding wasted investments require more extensive
planning and forecasting. Factors to consider are the network demands of multi-
tasking mobile devices, more robust mobile OSs, and their applications.
Mobile Infrastructure
Mobile infrastructure consists of the integration of technology, software, support,
security measures, and devices for the management and delivery of wireless com-
munications.
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4.2 Wireless Networks and Mobile Infrastructure 125
TECH NOTE 4.5 Wi-Fi Networking Standards
• 802.11b. This standard shares spectrum with 2.4-GHz cordless phones, microwave ovens, and many Bluetooth products. Data are transferred at
distances up to 100 meters or 328 feet.
Wi-Fi and Bluetooth
Bluetooth is a short-range—up to 100 meters or 328 feet—wireless communications technology found in billions of devices, such as smartphones, computers, medical
devices, and home entertainment products. When two Bluetooth-enabled devices
connect to each other, this is called pairing.
Wi-Fi is the standard way computers connect to wireless networks. Nearly all computers have built-in Wi-Fi chips that allow users to find and connect to wireless
routers. The router must be connected to the Internet in order to provide Internet
access to connected devices.
Wi-Fi technology allows devices to share a network or Internet connection
without the need to connect to a commercial network. Wi-Fi networks beam packets
over short distances using part of the radio spectrum, or they can extend over larger
areas, such as municipal Wi-Fi networks. However, municipal networks are not com-
mon because of their huge costs. See Figure 4.9 for an overview of how Wi-Fi works.
Bluetooth is a short-range wireless communications technology.
Wi-Fi is the standard way computers connect to wireless networks.
Figure 4.9 Overview of Wi-Fi.
Wireless Network
Acess Point
Cable/DSL
Modem
Antenna
Radio
Waves
Directional Antenna
and PC Card
Laptop(s) or Desktop(s)
1
2
3
Radio-equipped access point connected to the Internet
(or via a router). It generates and receives radio waves
(up to 400 feet).
Several client devices, equipped with PC cards, generate
and receive radio waves.
Router is connected to the Internet via a cable or
DSL modem, or is connected via a satellite.
Wireless Network
PC Card
2
1 3
Satellite
Internet
PC
Router
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126 Chapter 4 Networks for Efficient Operations and Sustainability
Figure 4.10 WiMAX/Wi-Fi network.
Notebook with built-in
WiMAX adapter
Wi-Fi hotspots
Base station
WiMAX hub
InternetWiMAX network
WIRELESS WIDE AREA NETWORKS
There are three general types of mobile networks: wide area networks (WANs), WiMAX, and local area networks (LANs). WANs for mobile computing are known as wireless wide area networks (WWANs). The range of a WWAN depends on the trans- mission media and the wireless generation, which determines which services are avail-
able. Two components of wireless infrastructures are wireless LANs and WiMAX.
LANs
Wireless LANs use high-frequency radio waves to communicate between comput-
ers, devices, or other nodes on the network. A wireless LAN typically extends an
existing wired LAN by attaching a wireless AP to a wired network.
WiMAX
Wireless broadband WiMAX transmits voice, data, and video over high-frequency
radio signals to businesses, homes, and mobile devices. It was designed to bypass
traditional telephone lines and is an alternative to cable and DSL. WiMAX is
based on the IEEE 802.16 set of standards and the metropolitan area network
(MAN) access standard. Its range is 20 to 30 miles and it does not require a clear
line of sight to function. Figure 4.10 shows the components of a WiMAX/Wi-Fi
network.
• 802.11a. This standard runs on 12 channels in the 5-GHz spectrum in North America, which reduces interference issues. Data are transferred about 5 times
faster than 802.11b, improving the quality of streaming media. It has extra
bandwidth for large files. Since the 802.11a and b standards are not interopera-
ble, data sent from an 802.11b network cannot be accessed by 802.11a networks.
• 802.11g. This standard runs on three channels in the 2.4-GHz spectrum, but at the speed of 802.11a. It is compatible with the 802.11b standard.
• 802.11n. This standard improves upon prior 802.11 standards by adding multiple-input multiple-output (MIMO) and newer features. Frequency
ranges from 2.4 GHz to 5 GHz with a data rate of about 22 Mbps, but perhaps
as high as 100 Mbps.
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4.3 Collaboration and Communication Technologies 127
TECH NOTE 4.6 Mobile Network Evaluation Factors
When evaluating mobile network solutions, there are four factors to consider. They
are:
1. Simple: Easy to deploy, manage, and use. 2. Connected: Always makes the best connection possible. 3. Intelligent: Works behind the scenes, easily integrating with other systems. 4. Trusted: Enables secure and reliable communications.
Questions 1. What factors are contributing to mobility? 2. Why is strategic planning of mobile networks important? 3. How does Wi-Fi work? 4. What is a WLAN? 5. Why is WiMAX important? 6. What factors should be considered when selecting a mobile network?
Now more than ever, business gets done through information sharing and col-
laborative planning. Business performance depends on broadband data networks
for communication, mobility, and collaboration. For example, after Ford Motor
Company began relying on UPS Logistics Group’s data networks to track millions
of cars and trucks and to analyze any potential problems before they occur, Ford
realized a $1 billion reduction in vehicle inventory and $125 million reduction in
inventory carrying costs annually.
People need to work together and share documents. Teams make most of the
complex decisions in organizations. And organizational decision making is difficult
when team members are geographically spread out and working in different time
zones.
Messaging and collaboration tools include older communications media such
as e-mail, videoconferencing, fax, and texts—and blogs, Skype, Web meetings,
and social media. Yammer is an enterprise social network that helps employees
collaborate across departments, locations, and business apps. These private social
sites are used by more than 400,000 enterprises worldwide. Yammer functions as a
communication and problem-solving tool and is rapidly replacing e-mail. You will
read about Yammer is detail in Chapter 7.
4.3 Collaboration and Communication Technologies
VIRTUAL COLLABORATION
Leading businesses are moving quickly to realize the benefits of virtual collabora-
tion. Several examples appear below.
Information Sharing Between Retailers and Their Suppliers
One of the most publicized examples of information sharing exists between Procter &
Gamble (P&G) and Walmart. Walmart provides P&G with access to sales informa-
tion on every item Walmart buys from P&G. The information is collected by P&G
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128 Chapter 4 Networks for Efficient Operations and Sustainability
on a daily basis from every Walmart store, and P&G uses that information to man-
age the inventory replenishment for Walmart.
Retailer–Supplier Collaboration: Asda Corporation
Supermarket chain Asda (asda.com) has rolled out Web-based electronic data
interchange (EDI) technology to 650 suppliers. Web EDI technology is based on
the AS2 standard, an internationally accepted HTTP-based protocol used to send
real time data in multiple formats securely over the Internet. It promises to improve
the efficiency and speed of traditional EDI communications, which route data over
third-party, value-added networks (VANs).
Lower Transportation and Inventory Costs and Reduced Stockouts: Unilever
Unilever’s 30 contract carriers deliver 250,000 truckloads of shipments annually.
Unilever’s Web-based database, the Transportation Business Center (TBC), pro-
vides these carriers with site specification requirements when they pick up a shipment
at a manufacturing or distribution center or when they deliver goods to retailers.
TBC gives carriers all of the vital information they need: contact names and phone
numbers, operating hours, the number of dock doors at a location, the height of the
dock doors, how to make an appointment to deliver or pick up shipments, pallet
configuration, and other special requirements. All mission-critical information that
Unilever’s carriers need to make pickups, shipments, and deliveries is now available
electronically 24/7.
Reduction of Product Development Time
Caterpillar, Inc. is a multinational heavy-machinery manufacturer. In the traditional
mode of operation, cycle time along the supply chain was long because the process
involved paper–document transfers among managers, salespeople, and technical
staff. To solve the problem, Caterpillar connected its engineering and manufactur-
ing divisions with its active suppliers, distributors, overseas factories, and customers
through an extranet-based global collaboration system. By means of the collabora-
tion system, a request for a customized tractor component, for example, can be
transmitted from a customer to a Caterpillar dealer and on to designers and sup-
pliers, all in a very short time. Customers also can use the extranet to retrieve and
modify detailed order information while the vehicle is still on the assembly line.
GROUP WORK AND DECISION PROCESSES
Managers and staff continuously make decisions as they develop and manufacture
products, plan social media marketing strategies, make financial and IT invest-
ments, determine how to meet compliance mandates, design software, and so on.
By design or default, group processes emerge, referred to as group dynamics, and those processes can be productive or dysfunctional.
Group Work and Dynamics
Group work can be quite complex depending on the following factors:
• Group members may be located in different places or work at different times.
• Group members may work for the same or different organizations.
• Needed data, information, or knowledge may be located in many sources, several of which are external to the organization.
Despite the long history and benefits of collaborative work, groups are not
always successful.
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4.3 Collaboration and Communication Technologies 129
Online Brainstorming in the Cloud
Brainstorming ideas is no longer limited to a room full of people offering their
ideas that are written on a whiteboard or posters. Companies are choosing an
alternative—online brainstorming applications, many of them cloud-based. An
advantage is the avoidance of travel expenses if members are geographically
dispersed, which often restricts how many sessions a company can afford to hold.
The following are two examples of online brainstorming apps:
• Evernote (evernote.com) is a cloud-based tool that helps users gather and share information, and brainstorm ideas. One function is Synch, which keeps
Evernote notes up-to-date across a user’s computers, phones, devices and the
Web. See Figure 4.11. A free version of Evernote is available for download.
• iMindmap Online, from UK-based ThinkBuzan (thinkbuzan.com), relies on mind mapping and other well-known structured approaches to brainstorm-
ing. iMindmap Online helps streamline work processes, minimize informa-
tion overload, generate new ideas, and boost innovation.
INTRANETS, EXTRANETS, AND VIRTUAL PRIVATE NETWORKS
Intranets are used within a company for data access, sharing, and collaboration. They are portals or gateways that provide easy and inexpensive browsing and
search capabilities. Colleges and universities rely on intranets to provide services
to students and faculty. Using screen sharing and other groupware tools, intranets
can support team work.
An extranet is a private, company-owned network that can be logged into remotely via the Internet. Typical users are suppliers, vendors, partners, or custom-
ers (Figure 4.12). Basically, an extranet is a network that connects two or more
companies so they can securely share information. Since authorized users remotely
access content from a central server, extranets can drastically reduce storage space
on individual hard drives.
A major concern is the security of the transmissions that could be intercepted
or compromised. One solution is to use virtual private networks (VPNs), which encrypt the packets before they are transferred over the network. VPNs consist of
encryption software and hardware that encrypt, send, and decrypt transmissions, as
shown in Figure 4.13. In effect, instead of using a leased line to create a dedicated,
physical connection, a company can invest in VPN technology to create virtual
Figure 4.11 Evernote brainstorming, note taking, and archiving software website. ©
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130 Chapter 4 Networks for Efficient Operations and Sustainability
Figure 4.12 Example of an AT&T extranet used by a customer to access account information. ©
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Figure 4.13 Virtual private networks (VPNs) create encrypted connections to company networks. ©
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Being profit-motivated without concern for damage to the environment is unaccept-
able. Society expects companies to generate a profit and to conduct themselves in
an ethical, socially responsible, and environmentally sustainable manner. Four fac-
tors essential to preserving the environment are shown in Figure 4.14. Sustainability grows more urgent every year as carbon emissions contribute to climate changes that are threatening quality of life—and possibly life itself.
4.4 Sustainability and Ethical Issues
connections routed through the Internet from the company’s private network to the
remote site or employee. Extranets can be expensive to implement and maintain
because of hardware, software, and employee training costs if hosted internally
rather than by an application service provider (ASP).
Questions 1. Why is group work challenging? 2. What might limit the use of in-person brainstorming? 3. How can online brainstorming tools overcome those limits? 4. What is the difference between an intranet and an extranet? 5. How does a virtual private network (VPN) provide security?
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4.4 Sustainability and Ethical Issues 131
Figure 4.14 The 4 Rs of environmental sustainability.
GLOBAL TEMPERATURE RISING TOO MUCH TOO FAST
At the United Nations’ 2009 climate conference in Copenhagen, climatologists
estimated that countries must keep the global mean temperature (GMT) from ris-
ing by more than 2 C (3.6 F) above the preindustrial GMT in order to avoid pro-
found damage to life on the earth. Damage includes water and food scarcity, rising
sea levels, and greater incidence and severity of disease. Only three years later,
GMT had already increased by 0.7 C, or 1.3 F. In 2012 IEA chief economist Faith
Birol warned that this trend is perfectly in line with a temperature increase of 6 C
by 2050, which would have devastating impacts on the planet. Since 2005 the Prince
of Wales’ Corporate Leaders Group on Climate Change has lobbied for more
aggressive climate legislation within the United Kingdom, the European Union,
and internationally. It holds that carbon emission reductions between 50 percent
and 85 percent are necessary by 2050 to prevent the global temperature from rising
too much too fast because of the greenhouse effect, as shown in Figure 4.15.
TECH NOTE 4.7 NASA’s Greenhouse Gas Emission Warnings
According to NASA (climate.nasa.gov), CO2 and other greenhouse gases (GHGs) trap the sun’s heat within the earth’s atmosphere, warming it and keeping it at habit-
able temperatures. Scientists have concluded that increases in CO2 resulting from
human activities have thrown the earth’s natural carbon cycle off balance, increasing
global temperatures and changing the planet’s climate.
GHG emissions worldwide hit record highs in 2011, according to the Interna-
tional Energy Agency (IEA). The IEA’s preliminary estimates indicate that global
emissions of carbon dioxide (CO2) from fossil-fuel combustion spiked to 31.6 giga-
tonnes (Gt) in 2011, an increase of 1 Gt or 3.2 percent from the 2010 level (Lemonick,
2012). One Gt equals 1 billion metric tons. GHG is now a serious a global concern.
The main international treaty on climate change is the United Nations Framework
Convention on Climate Change (UNFCCC).
In 2010 parties to the UNFCCC agreed that future global warming should be
limited to below 2 C (3.6 F) relative to the preindustrial level. Analysis suggests
that meeting the 2 C target would require annual global emissions of GHG to peak
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132 Chapter 4 Networks for Efficient Operations and Sustainability
Figure 4.16 Carbon cycle. The Orbiting Carbon Observatory-2 was launched in July 2014. The observatory is NASA’s fi rst satellite mission dedicated to studying CO2, which is a critical component of the earth’s carbon cycle driving changes in the earth’s climate. CO2 is also the largest human- produced GHG. Courtesy of genomicscience. energy.gov.
Figure 4.15 Illustration of the earth’s greenhouse effect.
GLOBAL WARMING Global warming refers to the upward trend in GMT. It is one of the most compli- cated issues facing world leaders. Figure 4.16 shows the relationship of fossil fuel,
soil, water, atmosphere, and so on in the carbon cycle. Even though the global
carbon cycle plays a central role in regulating CO2 in the atmosphere and thus the
earth’s climate, scientists’ understanding of the interlinked biological processes
that drive this cycle is limited. They know that whether an ecosystem will capture,
store, or release carbon depends on climate changes and organisms in the earth’s
biosphere. The biosphere refers to any place that life of any kind can exist on the
earth and contains several ecosystems. An ecosystem is a self-sustaining functional
unit of the biosphere; it exchanges material and energy between adjoining ecosys-
tems. Global warming occurs because of the greenhouse effect, which is the holding
of heat within the earth’s atmosphere. GHGs such as CO2, methane (CH4), and
nitrous oxide (N2O) absorb infrared radiation (IR), as diagrammed in Figure 4.17.
ICT’s Role in Global Warming
The IT industry sector is called the information and communications technology, or
ICT, in emission reports. ICT has certainly supported economic growth in developed
and developing countries and transformed societies, businesses, and people’s lives.
But what impacts do our expanding IT and social media dependence have on global
warming? How can business processes change or reduce GHGs? And what alternative
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before the year 2020 and decline signifi cantly thereafter, with emissions in 2050 reduced
by 30 to 50 percent compared to 1990 levels.
Analyses by the United Nations Environment Programme and International
Energy Agency warn that current policies are too weak to achieve the 2 C target.
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4.4 Sustainability and Ethical Issues 133
energy sources can be used to power the increasing demands for connectivity? Listed
below are several reports and initiatives to help answer these questions.
Global e-Sustainability Initiative and the SMART 2020 Report
The Climate Group’s SMART 2020 Report is the world’s first comprehensive global
study of the IT sector’s growing significance for the world’s climate. On behalf of
the Global e-Sustainability Initiative (GeSI, gesi.org), Climate Group found that
ICT plays a key role in reducing global warming. Transforming the way people and
businesses use IT could reduce annual human-generated global emissions by 15
percent by 2020 and deliver energy efficiency savings to global businesses of over
500 billion euros, or $800 billion U.S. And using social media, for example, to
inform consumers of the grams (g) of carbon emissions associated with the products
they buy could change buyer behavior and ultimately have a positive eco-effect. Like
food items that display calories and grams of fat to help consumers make healthier
food choices, product labels display the CO2 emissions generated in the production
of an item, as shown in Figure 4.18. By 2020 not only will people become more con-
nected, but things will, too—an estimated 50 billion machine-to-machine connec-
tions in 2020. A benefit of machine-to-machine connections is that they can relay
data about climate changes that make it possible to monitor our emissions.
Figure 4.17 Greenhouse gases absorb infrared radiation (IR) emitted from the earth and reradiate it back, thus contributing to the greenhouse effect. ©
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134 Chapter 4 Networks for Efficient Operations and Sustainability
Recommended Actions for the IT Sector
Analysis conducted by management consultants McKinsey & Company concludes
the following:
• The IT sector’s own footprint of 2 percent of global emissions could double
by 2020 because of increased use of tablets, smartphones, apps, and services.
To help, rather than worsen, the fight against climate change, the IT sector
must manage its own growing impact and continue to reduce emissions from
data centers, telecom networks, and the manufacture and use of its products.
• IT has the unique ability to monitor and maximize energy efficiency both within
and outside of its own industry sector to cut CO2 emissions by up to 5 times this
amount. This represents a savings of 7.8 Gt of CO2 per year by 2020, which is
greater than the 2010 annual emissions of either the United States or China.
The SMART 2020 Report gives a picture of the IT industry’s role in addressing
global climate change and facilitating efficient and low-carbon development. The
role of IT includes emission reduction and energy savings not only in the sector itself,
but also by transforming how and where people work. The most obvious ways are
by substituting digital formats—telework, video-conferencing, e-paper, and mobile
and e-commerce—for physical formats. Researchers estimate that replacing physical
products/services with their digital equivalents would provide 6 percent of the total
benefits the IT sector can deliver. Greater benefits are achieved when IT is applied
to other industries. Examples of those industries are smart building design and use,
smart logistics, smart electricity grids, and smart industrial motor systems.
SUSTAINABILITY YouTube reported that 100 hours of video are uploaded every minute in 2014— more than double the 48 hours per minute in 2012. Over 6 billion hours of video are
watched each month on YouTube—almost an hour for every person on the earth.
Within 5 years, from 2008 through 2013, most U.S. households tripled the number
of computing, gaming, consumer electronics, and mobile devices. Statistics about
Twitter and other social services also show phenomenal growth. Almost all of these
network activities are powered by the burning of fossil fuels. Today’s connected
lifestyles will further harm the environment unless corrective actions are taken such
as those listed in Figure 4.19.
Global Warming: A Hot Debate
Does our society have the capacity to endure in such a way that the 9 billion people
expected on the earth by 2050 will all be able to achieve a basic quality of life? The
Reducing barriers to the use
of public transport and
improving people’s experience
of the journey. For example,
smart ticketing and free Wi-Fi.
Facilitating car sharing and
eco-driving.
Encouraging and enforcing
speed limits by using average
speed cameras and intelligent
speed adaptation, which help
drivers to avoid fines and
stay safe.
Enabling home working and
using video conferencing and
e-commerce to reduce travel.
Figure 4.19 Recommendations for ICT from the Sustainable Development Commission (2010).
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4.4 Sustainability and Ethical Issues 135
answer is uncertain—and hotly debated. As you read, many scientists and experts
are extremely alarmed by global warming and climate change, but other experts
outright deny that they are occurring.
This debate may be resolved to some degree by NASA. A NASA spacecraft
was designed to make precise measurements of carbon dioxide (CO2) in the earth’s
atmosphere. The Orbiting Carbon Observatory-2 (OCO-2) was launched in July
2014. The observatory is NASA’s first satellite mission dedicated to studying CO2,
a critical component of the earth’s carbon cycle that is the most prevalent human-
produced GHG driving changes in the earth’s climate (Figure 4.16).
OCO-2 is a new tool for understanding both the sources of CO2 emissions and
the natural processes that remove CO2 from the atmosphere, and how they are
changing over time. The mission’s data will help scientists reduce uncertainties in
forecasts of how much carbon dioxide is in the atmosphere and improve the accu-
racy of global climate change predictions.
According to NASA, since the start of the Industrial Revolution more than
200 years ago, the burning of fossil fuels, as well as other human activities, have led
to an unprecedented buildup in this GHG, which in 2014 was at its highest level in
at least 800,000 years. Human activities have increased the level of CO2 by more
than 25 percent in just the past half century.
It is possible that we are living far beyond the earth’s capacity to support human
life. While sustainability is about the future of our society, for businesses, it is also
about return on investment (ROI). Businesses need to respect environmental lim-
its, but also need to show an ROI.
Sustainability Through Climate Change Mitigation
There are no easy or convenient solutions to carbon emissions from the fossil fuels
burned to power today’s tech dependencies. But there are pathways to solutions,
and every IT user, enterprise, and nation plays a role in climate change mitigation. Climate change mitigation is any action to limit the magnitude of long-term climate
change. Examples of mitigation include switching to low-carbon renewable energy
sources and reducing the amount of energy consumed by power stations by increasing
their efficiency. There have been encouraging successes. For example, investments
in research and development (R&D) to reduce the amount of carbon emitted by
power stations for mobile networks are paying off. Announced in April 2014, a break-
through in the design of signal amplifiers for mobile technology will cut 200 mega-
watts (MW) from the load of power stations, which will reduce CO2 emissions by
0.5 million tonnes a year (Engineering and Physical Sciences Research Council, 2014).
Mobile, Cloud, and Social Carbon Footprint
No one sees CO2 being emitted from their Androids or iPhones. But wired and
mobile networks enable limitless data creation and consumption—and these activi-
ties increase energy consumption. Quite simply, the surge in energy used to power
data centers, cell towers, base stations, and recharge devices is damaging the envi-
ronment and depleting natural resources. It is critical to develop energy systems
that power our economy without increasing global temperatures beyond 2 C. To
do their part to reduce damaging carbon emissions, some companies have imple-
mented effective sustainability initiatives.
Sustainability Initiatives
Communications technology accounts for approximately 2 percent of global
carbon emissions; it is predicted that this figure will double by 2020 as end-
user demand for high-bandwidth services with enhanced quality of experience
explodes worldwide. Innovative solutions hold the key to curbing these emissions
and reducing environmental impact.
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136 Chapter 4 Networks for Efficient Operations and Sustainability
Network service providers as well as organizations face the challenges of
energy efficiency, a smaller carbon footprint, and eco-sustainability. To deal with these challenges, wired and wireless service providers and companies need to
upgrade their networks to next-generation, all-IP infrastructures that are opti-
mized and scalable. The network must provide eco-sustainability in traffic trans-
port and deliver services more intelligently, reliably, securely, efficiently and at the
lowest cost.
For example, Alcatel-Lucent’s High Leverage Network (HLN) can reduce total
cost of ownership (TCO) by using fewer devices, creating an eco-sustainable choice
for service providers. Fewer devices mean less power and cooling, which reduces
the carbon footprint. HLN can also handle large amounts of traffic more efficiently
because the networks are intelligent, sending packets at the highest speed and most
efficiently.
ETHICAL CONSIDERATIONS OF HYPER-CONNECTED HUMANS
The complexity of a connected life will increase as we move to the new era of nano-
sensors and devices, virtual spaces, and 3D social networks exchanging zillions of
bytes of data. Managers and workers need to consider ethical and social issues, such
as quality of life and working conditions. Individuals will experience both positive
and negative impacts from being linked to a 24/7 workplace, working in virtual
teams, and being connected to handhelds whose impact on health can be damag-
ing. A 2008 study by Solutions Research Group found that always being connected
is a borderline obsession for many people. According to the study, 68 percent of
Americans may suffer from disconnect anxiety—feelings of disorientation and ner- vousness when deprived of Internet or wireless access for a period of time. Consider
this development and its implications.
Driving while distracted is a crime. Texting while driving is comparable to
driving under the influence (DUI), according to safety experts. Several studies indi-
cate that the use of mobile devices is a leading cause of car crashes. At any given
moment, more than 10 million U.S. drivers are talking on handheld cell phones,
according to the National Highway Traffic Safety Administration. Why is this a
problem? Mobiles are a known distraction, and the NHTSA has determined that
driver inattention is a primary or contributing factor in as many as 25 percent of all
police-reported traffic accidents. This does not include the thousands of accidents
not reported to the authorities.
In most or all states, distracted driving is a crime that carries mandatory fines
(Figure 4.20). For example, in California and New York State, drivers charged with
this crime face fines and have their driving license suspended. If driving while dis-
tracted causes injury or death to others, violators face jail time.
The importance of understanding ethical issues has been recognized by the
Association to Advance Collegiate Schools of Business (AACSB International,
aacsb.edu). For business majors, the AACSB International has defined Assurance
Figure 4.20 Texting while driving is a crime and potentially fatal. ©
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4.4 Sustainability and Ethical Issues 137
of Learning Requirements for ethics at both the undergraduate and graduate levels.
In Standard 15: Management of Curricula (AACSB, 2006), the association identi- fies general knowledge and skill learning experiences that include “ethical under-
standing and reasoning abilities” at the undergraduate level. At the graduate level,
Standard 15 requires learning experiences in management-specific knowledge and skill areas to include “ethical and legal responsibilities in organizations and society”
(AACSB, 2006).
Additions and Life Out of Control
The technologies covered in this chapter blur work, social, and personal time. IT
keeps people connected with no real off switch. Tools that are meant to improve
the productivity and quality of life in general can also intrude on personal time.
Managers need to be aware of the huge potential for abuse by expecting a 24/7
response from workers.
The report Looking Further with Ford—2014 Trends identifies trends in how the technology explosion will affect consumer choices and behaviors. Sheryl
Connelly, Ford global trend and futuring manager, summarized what was learned:
“There is no escaping the impact—both positive and negative—of the rapid pace
of technology. . . . We are seeing a consumer culture that is increasingly mindful of
the need to nurture society’s valuable and irreplaceable resources” (Ford Motors,
2013). Four trends were discussed in this report:
1. Micro Moments: With so much information at our fi ngertips, downtime has given way to fi lling every moment with bite-sized chunks of information, education,
and entertainment—seemingly packing our lives with productivity.
2. Myth of Multitasking: In an increasingly screen-saturated, multitasking mod- ern world, more and more evidence is emerging to suggest that when we do
everything at once, we sacrifi ce the quality—and often safety—of each thing
we do.
3. Vying for Validation: In a world of hyper-self-expression, chronic public journal- ing, and other forms of digital expression, consumers are creating a public self
that may need validation even more than their authentic self.
4. Sustainability: The world has been fi xated on going green, and now attention is shifting beyond recycling and eco-chic living to a growing concern for the power
and preciousness of the planet’s water.
In our hyper-connected world, people are always on, collaborating, communi-
cating, and creating—and not always aware of how technology impacts them. We
need to learn how to harness that energy and connection to develop the next gen-
eration of critical, thoughtful thinkers.
Questions 1. Why do some experts warn that carbon emission reductions between
50 percent and 85 percent are necessary by 2050? 2. What contributes to the rise of global mean temperature? 3. What is the greenhouse effect? 4. How does the use of mobile devices contribute to the level of green-
house gases? 5. What is ICT’s role in global warming? 6. Why is global warming hotly debated? 7. Explain the goal of sustainability. 8. Explain the characteristics of a life out of control.
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138 Chapter 4 Networks for Efficient Operations and Sustainability
Key Terms
3G
4G
5G
application program
interface (API)
bandwidth
Bluetooth
carbon footprint
circuit switching
climate change
climate change mitigation
extranet
fi xed-line broadband
greenhouse gases
(GHGs)
group dynamics
information and commu-
nications technology
(ICT)
Internet Protocol (IP)
intranet
IP address
IP Version 4 (IPv4)
IP Version 6 (IPv6)
latency-sensitive apps
local area network (LAN)
Long-Term Evolution
(LTE)
mashup
mobile broadband
near-fi eld communication
(NFC)
Net neutrality
Net semi-neutrality
packet
packet switching
protocol
quality of service (QoS)
router
sustainability
switch
traffi c shaping
transmission control
protocol/Internet
protocol (TCP/IP)
virtual private network
(VPN)
voice over IP (VoIP)
wide area network (WAN)
Wi-Fi
WiMAX
Assuring Your Learning
1. Explain how network capacity is measured.
2. How are devices identifi ed to a network?
3. Explain how digital signals are transmitted.
4. Explain the functions of switches and routers.
5. QoS technologies can be applied to create two tiers of traffi c. What are those tiers? Give an example of
each type of traffi c.
6. Typically, networks are confi gured so that down- loading is faster than uploading. Explain why.
7. What are signifi cant issues about 4G wireless networks?
8. What are two 4G wireless standards?
9. How is network performance measured?
10. Discuss two applications of near-fi eld communica- tion (NFC).
11. What are the benefi ts of APIs?
12. Describe the components of a mobile communica- tion infrastructure.
13. What is the range of WiMAX? Why does it not need a clear line of sight?
14. Why are VPNs used to secure extranets?
15. How can group dynamics improve group work? How can it disrupt what groups might accomplish?
16. What are the benefi ts of using software to conduct brainstorming in the cloud (remotely)?
17. How do mobile devices contribute to carbon emissions?
18. Discuss the ethical issues of anytime-anywhere accessibility.
19. What health and quality-of-life issues are associ- ated with social networks and a 24/7 connected
lifestyle?
20. Is distracted driving an unsolvable problem? Explain.
DISCUSS: Critical Thinking Questions
21. Visit the Alcatel-Lucent website (www.alcatel-lucent. com) and search for “eco-sustainability strategy.”
a. Read about Alcatel’s eco-sustainability strategy.
b. Describe how the company is developing eco- sustainable networks. In your opinion, is this an
effective strategy? Explain.
c. Explain how the company is enabling a low- carbon economy. What is its most signifi cant
contribution to sustainability?
22. Visit the Google apps website. Identify three types of collaboration support and their value in the workplace.
23. Compare the various features of broadband wireless networks (e.g., 3G, Wi-Fi, and WiMAX).
Visit at least three broadband wireless network
vendors.
a. Prepare a list of capabilities for each network.
b. Prepare a list of actual applications that each network can support.
c. Comment on the value of such applications to users. How can the benefi ts be assessed?
EXPLORE: Online and Interactive Exercises
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CASE 4.2 Business Case 139
24. Visit Youtube.com and search for tutorials on the latest version of iMindMap. Watch a few of the
tutorials. As an alternative, watch the video at http://
www.youtube.com/watch?v=UVt3Qu6Xcko&list=PL
A42C25431E4EA4FF. Describe the potential value
of sharing maps online and synching maps with other
computers or devices. What is your opinion of the
ease or complexity of the iMindMap interface?
25. Visit Google Green at www.google.com/green/ bigpicture. Describe Google’s efforts to minimize
the environmental impact of its services. Do you
believe that Google can reduce its carbon footprint
beyond zero, as the company claims? Explain your
answer.
ANALYZE & DECIDE: Apply IT Concepts to Business Decisions
C A S E 4 . 2
