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Library of Congress Cataloging-in-Publication Data

Turban, Efraim. [Decision support and expert system,) Business intelligence and analytics: systems for decision support/Ramesh Sharda , Oklahoma State University,

Dursun Delen , Oklahoma State University, Efraim Turban, University of Hawaii; With contributions by J. E. Aronson, The University of Georgia, Ting-Peng Liang, National Sun Yat-sen University, David King, JOA Software Group, Inc.-Tenth edition.

pages cm ISBN-13: 978-0-13-305090-5 ISBN-10: 0-13-305090-4 1. Management-Data processing. 2. Decision support systems. 3. Expert systems (Compute r science)

4. Business intelligence. I. Title . HD30.2.T87 2014 658.4'03801 l-dc23

10 9 8 7 6 5 4 3 2 1

PEARSON

2013028826

ISBN 10: 0-13-305090-4 ISBN 13: 978-0-13-305090-5

BRIEF CONTENTS

Preface xxi About the Authors xxix

PART I Decision Making and Analytics: An Overview 1 Chapter 1 An Overview of Business Intelligence, Analytics,

and Decision Support 2 Chapter 2 Foundations and Technologies for Decision Making 37

PART II Descriptive Analytics 77 Chapter 3 Data Warehousing 78 Chapter 4 Business Reporting, Visual Analytics, and Business

Performance Management 135

PART Ill Predictive Analytics 185 Chapter 5 Data Mining 186 Chapter 6 Techniques for Predictive Modeling 243 Chapter 7 Text Analytics, Text Mining, and Sentiment Analysis 288 Chapter 8 Web Analytics, Web Mining, and Social Analytics 338

PART IV Prescriptive Analytics 391 Chapter 9 Model-Based Decision Making: Optimization and Multi-

Criteria Systems 392 Chapter 10 Modeling and Analysis: Heuristic Search Methods and

Simulation 435 Chapter 11 Automated Decision Systems and Expert Systems 469 Chapter 12 Knowledge Management and Collaborative Systems 507

PART V Big Data and Future Directions for Business Analytics 541 Chapter 13 Big Data and Analytics 542 Chapter 14 Business Analytics: Emerging Trends and Future

Impacts 592 Glossary 634 Index 648

iii

CONTENTS

Preface xxi About the Authors xxix

Part I Decision Making and Analytics: An Overview 1 Chapter 1 An Overview of Business Intelligence, Analytics, and

Decision Support 2 1.1 Opening Vignette: Magpie Sensing Employs Analytics to

Manage a Vaccine Supply Chain Effectively and Safely 3 1.2 Changing Business Environments and Computerized

Decision Support 5 The Business Pressures-Responses-Support Model 5

1.3 Managerial Decision Making 7 The Nature of Managers' Work 7 The Decision-Making Process 8

1.4 Information Systems Support for Decision Making 9 1.5 An Early Framework for Computerized Decision

Support 11 The Gorry and Scott-Morton Classical Framework 11 Computer Support for Structured Decisions 12 Computer Support for Unstructured Decisions 13 Computer Support for Semistructured Problems 13

1.6 The Concept of Decision Support Systems (DSS) 13 DSS as an Umbrella Term 13 Evolution of DSS into Business Intelligence 14

1.7 A Framework for Business Intelligence (Bl) 14 Definitions of Bl 14 A Brief History of Bl 14 The Architecture of Bl 15 Styles of Bl 15 The Origins and Drivers of Bl 16 A Multimedia Exercise in Business Intelligence 16

APPLICATION CASE 1.1 Sabre Helps Its Clients Through Dashboards and Analytics 17

The DSS-BI Connection 18 1.8 Business Analytics Overview 19

Descriptive Analytics 20 APPLICATION CASE 1.2 Eliminating Inefficiencies at Seattle Children's Hospital 21 APPLICATION CASE 1.3 Analysis at the Speed of Thought 22

Predictive Analytics 22

APPLICATION CASE 1.4 Moneybal/: Analytics in Sports and Movies 23 APPLICATION CASE 1.5 Analyzing Athletic Injuries 24

Prescriptive Analytics 24 APPLICATION CASE 1.6 Industrial and Commercial Bank of China (ICBC) Employs Models to Reconfigure Its Branch Network 25

Analytics Applied to Different Domains 26 Analytics or Data Science? 26

1.9 Brief Introduction to Big Data Analytics 27 What Is Big Data? 27

APPLICATION CASE 1.7 Gilt Groupe's Flash Sales Streamlined by Big Data Analytics 29

1.10 Plan of the Book 29 Part I: Business Analytics: An Overview 29 Part II: Descriptive Analytics 30 Part Ill: Predictive Analytics 30 Part IV: Prescriptive Analytics 31 Part V: Big Data and Future Directions for Business Analytics 31

1.11 Resources, Links, and the Teradata University Network Connection 31 Resources and Links 31 Vendors, Products, and Demos 31 Periodicals 31 The Teradata University Network Connection 32 The Book's Web Site 32 Chapter Highlights 32 • Key Terms 33 Questions for Discussion 33 • Exercises 33

END-OF-CHAPTER APPLICATION CASE Nationwide Insurance Used Bl to Enhance Customer Service 34

References 35

Chapter 2 Foundations and Technologies for Decision Making 37 2.1 Opening Vignette: Decision Modeling at HP Using

Spreadsheets 38 2.2 Decision Making: Introduction and Definitions 40

Characteristics of Decision Making 40 A Working Definition of Decision Making 41 Decision-Making Disciplines 41 Decision Style and Decision Makers 41

2.3 Phases of the Decision-Making Process 42 2.4 Decision Making: The Intelligence Phase 44

Problem (or Opportunity) Identification 45 APPLICATION CASE 2.1 Making Elevators Go Faster! 45

Problem Classification 46 Problem Decomposition 46 Problem Ownership 46

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2.5 Decision Making: The Design Phase 47 Models 47 Mathematical (Quantitative) Models 47 The Benefits of Models 4 7 Selection of a Principle of Choice 48 Normative Models 49 Suboptimization 49 Descriptive Models 50 Good Enough, or Satisficing 51 Developing (Generating) Alternatives 52 Measuring Outcomes 53 Risk 53 Scenarios 54 Possible Scenarios 54 Errors in Decision Making 54

2.6 Decision Making: The Choice Phase 55 2.7 Decision Making: The Implementation Phase 55 2.8 How Decisions Are Supported 56

Support for the Intelligence Phase 56 Support for the Design Phase 5 7 Support for the Choice Phase 58 Support for the Implementation Phase 58

2.9 Decision Support Systems: Capabilities 59 A DSS Application 59

2.10 DSS Classifications 61 The AIS SIGDSS Classification for DSS 61 Other DSS Categories 63 Custom-Made Systems Versus Ready-Made Systems 63

2.11 Components of Decision Support Systems 64 The Data Management Subsystem 65 The Model Management Subsystem 65

APPLICATION CASE 2.2 Station Casinos Wins by Building Customer Relationships Using Its Data 66 APPLICATION CASE 2.3 SNAP DSS Helps OneNet Make Telecommunications Rate Decisions 68

The User Interface Subsystem 68 The Knowledge-Based Management Subsystem 69

APPLICATION CASE 2.4 From a Game Winner to a Doctor! 70 Chapter Highlights 72 • Key Terms 73 Questions for Discussion 73 • Exercises 74

END-OF-CHAPTER APPLICATION CASE Logistics Optimization in a Major Shipping Company (CSAV) 74

References 75

Part II Descriptive Analytics 77 Chapter 3 Data Warehousing 78

3.1 Opening Vignette: Isle of Capri Casinos Is Winning with Enterprise Data Warehouse 79

3.2 Data Warehousing Definitions and Concepts 81 What Is a Data Warehouse? 81 A Historical Perspective to Data Warehousing 81 Characteristics of Data Warehousing 83 Data Marts 84 Operational Data Stores 84 Enterprise Data Warehouses (EDW) 85 Metadata 85

APPLICATION CASE 3.1 A Better Data Plan: Well-Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry 85

3.3 Data Warehousing Process Overview 87 APPLICATION CASE 3.2 Data Warehousing Helps MultiCare Save More Lives 88

3.4 Data Warehousing Architectures 90 Alternative Data Warehousing Architectures 93 Which Architecture Is the Best? 96

3.5 Data Integration and the Extraction, Transformation, and Load (ETL) Processes 97 Data Integration 98

APPLICATION CASE 3.3 BP Lubricants Achieves BIGS Success 98 Extraction, Transfonnation, and Load 100

3.6 Data Warehouse Development 102 APPLICATION CASE 3.4 Things Go Better with Coke's Data Warehouse 103

Data Warehouse Development Approaches 103 APPLICATION CASE 3.5 Starwood Hotels & Resorts Manages Hotel Profitability with Data Warehousing 106

Additional Data Warehouse Development Considerations 107 Representation of Data in Data Warehouse 108 Analysis of Data in the Data Warehouse 109 OLAP Versus OLTP 110 OLAP Operations 11 0

3.7 Data Warehousing Implementation Issues 113 APPLICATION CASE 3.6 EDW Helps Connect State Agencies in Michigan 115

Massive Data Warehouses and Scalability 116 3.8 Real-Time Data Warehousing 117

APPLICATION CASE 3.7 Egg Pie Fries the Competition in Near Real Time 118

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3.9 Data Warehouse Administration, Security Issues, and Future Trends 121 The Future of Data Warehousing 123

3.10 Resources, Links, and the Teradata University Network Connection 126 Resources and Links 126 Cases 126 Vendors, Products, and Demos 127 Periodicals 127 Additional References 127 The Teradata University Network (TUN) Connection 127 Chapter Highlights 128 • Key Terms 128 Questions for Discussion 128 • Exercises 129 .... END-OF-CHAPTER APPLICATION CASE Continental Airlines Flies High

with Its Real-Time Data Warehouse 131 References 132

Chapter 4 Business Reporting, Visual Analytics, and Business Performance Management 135

4.1 Opening Vignette:Self-Service Reporting Environment Saves Millions for Corporate Customers 136

4.2 Business Reporting Definitions and Concepts 139 What Is a Business Report? 140 ..,. APPLICATION CASE 4.1 Delta Lloyd Group Ensures Accuracy and

Efficiency in Financial Reporting 141 Components of the Business Reporting System 143 .... APPLICATION CASE 4.2 Flood of Paper Ends at FEMA 144

4.3 Data and Information Visualization 145 ..,. APPLICATION CASE 4.3 Tableau Saves Blastrac Thousands of Dollars

with Simplified Information Sharing 146

A Brief History of Data Visualization 147 .... APPLICATION CASE 4.4 TIBCO Spotfire Provides Dana-Farber Cancer

Institute with Unprecedented Insight into Cancer Vaccine Clinical Trials 149

4.4 Different Types of Charts and Graphs 150 Basic Charts and Graphs 150 Specialized Charts and Graphs 151

4.5 The Emergence of Data Visualization and Visual Analytics 154 Visual Analytics 156 High-Powered Visual Analytics Environments 158

4.6 Performance Dashboards 160 .... APPLICATION CASE 4.5 Dallas Cowboys Score Big with Tableau and

Teknion 161

Dashboard Design 162 APPLICATION CASE 4.6 Saudi Telecom Company Excels with Information Visualization 163

What to Look For in a Dashboard 164 Best Practices in Dashboard Design 165 Benchmark Key Performance Indicators with Industry Standards 165 Wrap the Dashboard Metrics with Contextual Metadata 165 Validate the Dashboard Design by a Usability Specialist 165 Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard 165 Enrich Dashboard with Business Users' Comments 165 Present Information in Three Different Levels 166 Pick the Right Visual Construct Using Dashboard Design Principles 166 Provide for Guided Analytics 166

4.7 Business Performance Management 166 Closed-Loop BPM Cycle 167

APPLICATION CASE 4.7 IBM Cognos Express Helps Mace for Faster and Better Business Reporting 169

4.8 Performance Measurement 170 Key Performance Indicator (KPI) 171 Performance Measurement System 172

4.9 Balanced Scorecards 172 The Four Perspectives 173 The Meaning of Balance in BSC 17 4 Dashboards Versus Scorecards 174

4.10 Six Sigma as a Performance Measurement System 175 The DMAIC Performance Model 176 Balanced Scorecard Versus Six Sigma 176 Effective Performance Measurement 1 77

APPLICATION CASE 4.8 Expedia.com's Customer Satisfaction Scorecard 178

Chapter Highlights 179 • Key Terms 180 Questions for Discussion 181 • Exercises 181

END-OF-CHAPTER APPLICATION CASE Smart Business Reporting Helps Healthcare Providers Deliver Better Care 182

References 184

Part Ill Predictive Analytics 185 Chapter 5 Data Mining 186

5.1 Opening Vignette: Cabela's Reels in More Customers with Advanced Analytics and Data Mining 187

5.2 Data Mining Concepts and Applications 189 APPLICATION CASE 5.1 Smarter Insurance: Infinity P&C Improves Customer Service and Combats Fraud with Predictive Analytics 191

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Definitions, Characteristics, and Benefits 192 ..,. APPLICATION CASE 5.2 Harnessing Analytics to Combat Crime:

Predictive Analytics Helps Memphis Police Department Pinpoint Crime and Focus Police Resources 196

How Data Mining Works 197 Data Mining Versus Statistics 200

5.3 Data Mining Applications 201 .... APPLICATION CASE 5.3 A Mine on Terrorist Funding 203

5.4 Data Mining Process 204 Step 1: Business Understanding 205 Step 2: Data Understanding 205 Step 3: Data Preparation 206 Step 4: Model Building 208 .... APPLICATION CASE 5.4 Data Mining in Cancer Research 210 Step 5: Testing and Evaluation 211 Step 6: Deployment 211 Other Data Mining Standardized Processes and Methodologies 212

5.5 Data Mining Methods 214 Classification 214 Estimating the True Accuracy of Classification Models 215 Cluster Analysis for Data Mining 220 ..,. APPLICATION CASE 5.5 2degrees Gets a 1275 Percent Boost in Churn

Identification 221 Association Rule Mining 224

5.6 Data Mining Software Tools 228 .... APPLICATION CASE 5.6 Data Mining Goes to Hollywood: Predicting

Financial Success of Movies 231 5.7 Data Mining Privacy Issues, Myths, and Blunders 234

Data Mining and Privacy Issues 234 .... APPLICATION CASE 5.7 Predicting Customer Buying Patterns-The

Target Story 235 Data Mining Myths and Blunders 236 Chapter Highlights 237 • Key Terms 238 Questions for Discussion 238 • Exercises 239 .... END-OF-CHAPTER APPLICATION CASE Macys.com Enhances Its

Customers' Shopping Experience with Analytics 241 References 241

Chapter 6 Techniques for Predictive Modeling 243 6.1 Opening Vignette: Predictive Modeling Helps Better

Understand and Manage Complex Medical Procedures 244

6.2 Basic Concepts of Neural Networks 247 Biological and Artificial Neural Networks 248 ..,. APPLICATION CASE 6.1 Neural Networks Are Helping to Save Lives in

the Mining Industry 250 Elements of ANN 251

Network Information Processing 2 52 Neural Network Architectures 254

APPLICATION CASE 6.2 Predictive Modeling Is Powering the Power Generators 256

6.3 Developing Neural Network-Based Systems 258 The General ANN Learning Process 259 Backpropagation 260

6.4 Illuminating the Black Box of ANN with Sensitivity Analysis 262

APPLICATION CASE 6.3 Sensitivity Analysis Reveals Injury Severity Factors in Traffic Accidents 264

6.5 Support Vector Machines 265 APPLICATION CASE 6.4 Managing Student Retention with Predictive Modeling 266

Mathematical Formulation of SVMs 270 Primal Form 271 Dual Form 271 Soft Margin 271 Nonlinear Classification 272 Kernel Trick 272

6.6 A Process-Based Approach to the Use of SVM 273 Support Vector Machines Versus Artificial Neural Networks 274

6.7 Nearest Neighbor Method for Prediction 275 Similarity Measure: The Distance Metric 276 Parameter Selection 277

APPLICATION CASE 6.5 Efficient Image Recognition and Categorization with kNN 278

Chapter Highlights 280 • Key Terms 280 Questions for Discussion 281 • Exercises 281

END-OF-CHAPTER APPLICATION CASE Coors Improves Beer Flavors with Neural Networks 284

References 285

Chapter 7 Text Analytics, Text Mining, and Sentiment Analysis 288 7.1 Opening Vignette: Machine Versus Men on Jeopardy!: The

Story of Watson 289 7.2 Text Analytics and Text Mining Concepts and

Definitions 291 APPLICATION CASE 7.1 Text Mining for Patent Analysis 295

7.3 Natural Language Processing 296 APPLICATION CASE 7.2 Text Mining Improves Hong Kong Government's Ability to Anticipate and Address Public Complaints 298

7.4 Text Mining Applications 300 Marketing Applications 301 Security Applications 301

APPLICATION CASE 7.3 Mining for Lies 302 Biomedical Applications 304

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Academic Applications 305 .... APPLICATION CASE 7.4 Text Mining and Sentiment Analysis Help

Improve Customer Service Performance 306 7.5 Text Mining Process 307

Task 1: Establish the Corpus 308 Task 2: Create the Term-Document Matrix 309 Task 3: Extract the Knowledge 312 ..,. APPLICATION CASE 7.5 Research Literature Survey with Text

Mining 314 7.6 Text Mining Tools 317

Commercial Software Tools 317 Free Software Tools 317 ..,. APPLICATION CASE 7.6 A Potpourri ofText Mining Case Synopses 318

7.7 Sentiment Analysis Overview 319 ..,. APPLICATION CASE 7.7 Whirlpool Achieves Customer Loyalty and

Product Success with Text Analytics 321 7.8 Sentiment Analysis Applications 323 7.9 Sentiment Analysis Process 325

Methods for Polarity Identification 326 Using a Lexicon 327 Using a Collection of Training Documents 328 Identifying Semantic Orientation of Sentences and Phrases 328 Identifying Semantic Orientation of Document 328

7.10 Sentiment Analysis and Speech Analytics 329 How Is It Done? 329 ..,. APPLICATION CASE 7.8 Cutting Through the Confusion: Blue Cross

Blue Shield of North Carolina Uses Nexidia's Speech Analytics to Ease Member Experience in Healthcare 331

Chapter Highlights 333 • Key Terms 333 Questions for Discussion 334 • Exercises 334 .... END-OF-CHAPTER APPLICATION CASE BBVA Seamlessly Monitors

and Improves Its Online Reputation 335 References 336

Chapter 8 Web Analytics, Web Mining, and Social Analytics 338 8.1 Opening Vignette: Security First Insurance Deepens

Connection with Policyholders 339 8.2 Web Mining Overview 341 8.3 Web Content and Web Structure Mining 344

.... APPLICATION CASE 8.1 Identifying Extremist Groups with Web Link and Content Analysis 346

8.4 Search Engines 347 Anatomy of a Search Engine 347 1. Development Cycle 348 Web Crawler 348 Document Indexer 348

2. Response Cycle 349 Query Analyzer 349 Document Matcher/Ranker 349 How Does Google Do It? 351

APPLICATION CASE 8.2 IGN Increases Search Traffic by 1500 Percent 353

8.5 Search Engine Optimization 354 Methods for Search Engine Optimization 355

APPLICATION CASE 8.3 Understanding Why Customers Abandon Shopping Carts Results in $10 Million Sales Increase 357

8.6 Web Usage Mining (Web Analytics) 358 Web Analytics Technologies 359

APPLICATION CASE 8.4 Allegro Boosts Online Click-Through Rates by 500 Percent with Web Analysis 360

Web Analytics Metrics 362 Web Site Usability 362 Traffic Sources 363 Visitor Profiles 364 Conversion Statistics 364

8.7 Web Analytics Maturity Model and Web Analytics Tools 366 Web Analytics Tools 368 Putting It All Together-A Web Site Optimization Ecosystem 370 A Framework for Voice of the Customer Strategy 372

8.8 Social Analytics and Social Network Analysis 373 Social Network Analysis 374 Social Network Analysis Metrics 375

APPLICATION CASE 8.5 Social Network Analysis Helps Telecommunication Firms 375

Connections 376 Distributions 376 Segmentation 377

8.9 Social Media Definitions and Concepts 377 How Do People Use Social Media? 378

APPLICATION CASE 8.6 Measuring the Impact of Social Media at Lollapalooza 379

8.10 Social Media Analytics 380 Measuring the Social Media Impact 381 Best Practices in Social Media Analytics 381

APPLICATION CASE 8.7 eHarmony Uses Social Media to Help Take the Mystery Out of Online Dating 383

Social Media Analytics Tools and Vendors 384 Chapter Highlights 386 • Key Terms 387 Questions for Discussion 387 • Exercises 388

END-OF-CHAPTER APPLICATION CASE Keeping Students on Track with Web and Predictive Analytics 388

References 390

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Part IV Prescriptive Analytics 391 Chapter 9 Model-Based Decision Making: Optimization and

Multi-Criteria Systems 392 9.1 Opening Vignette: Midwest ISO Saves Billions by Better

Planning of Power Plant Operations and Capacity Planning 393

9.2 Decision Support Systems Modeling 394 APPLICATION CASE 9.1 Optimal Transport for ExxonMobil Downstream Through a DSS 395

Current Modeling Issues 396 APPLICATION CASE 9.2 Forecasting/Predictive Analytics Proves to Be a Good Gamble for Harrah's Cherokee Casino and Hotel 397

9.3 Structure of Mathematical Models for Decision Support 399 The Components of Decision Support Mathematical Models 399 The Structure of Mathematical Models 401

9.4 Certainty, Uncertainty, and Risk 401 Decision Making Under Certainty 402 Decision Making Under Uncertainty 402 Decision Making Under Risk (Risk Analysis) 402

APPLICATION CASE 9.3 American Airlines Uses Should-Cost Modeling to Assess the Uncertainty of Bids for Shipment Routes 403

9.5 Decision Modeling with Spreadsheets 404 APPLICATION CASE 9.4 Showcase Scheduling at Fred Astaire East Side Dance Studio 404

9.6 Mathematical Programming Optimization 407 APPLICATION CASE 9.5 Spreadsheet Model Helps Assign Medical Residents 407

Mathematical Programming 408 Linear Programming 408 Modeling in LP: An Example 409 Implementation 414

9.7 Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking 416 Multiple Goals 416 Sensitivity Analysis 417 What-If Analysis 418 Goal Seeking 418

9.8 Decision Analysis with Decision Tables and Decision Trees 420 Decision Tables 420 Decision Trees 422

9.9 Multi-Criteria Decision Making With Pairwise Comparisons 423 The Analytic Hierarchy Process 423

APPLICATION CASE 9.6 U.S. HUD Saves the House by Using AHP for Selecting IT Projects 423

Tutorial on Applying Analytic Hierarchy Process Using Web-HIPRE 425 Chapter Highlights 429 • Key Terms 430 Questions for Discussion 430 • Exercises 430

END-OF-CHAPTER APPLICATION CASE Pre-Positioning of Emergency Items for CARE International 433

References 434

Chapter 10 Modeling and Analysis: Heuristic Search Methods and Simulation 435 10.1 Opening Vignette: System Dynamics Allows Fluor

Corporation to Better Plan for Project and Change Management 436

10.2 Problem-Solving Search Methods 437 Analytical Techniques 438 Algorithms 438 Blind Searching 439 Heuristic Searching 439

APPLICATION CASE 10.1 Chilean Government Uses Heuristics to Make Decisions on School Lunch Providers 439

10.3 Genetic Algorithms and Developing GA Applications 441 Example: The Vector Game 441 Terminology of Genetic Algorithms 443 How Do Genetic Algorithms Work? 443 Limitations of Genetic Algorithms 445 Genetic Algorithm Applications 445

10.4 Simulation 446 APPLICATION CASE 10.2 Improving Maintenance Decision Making in the Finnish Air Force Through Simulation 446 APPLICATION CASE 10.3 Simulating Effects of Hepatitis B Interventions 447

Major Characteristics of Simulation 448 Advantages of Simulation 449 Disadvantages of Simulation 450 The Methodology of Simulation 450 Simulation Types 451 Monte Carlo Simulation 452 Discrete Event Simulation 453

10.5 Visual Interactive Simulation 453 Conventional Simulation Inadequacies 453 Visual Interactive Simulation 453 Visual Interactive Models and DSS 454

APPLICATION CASE 10.4 Improving Job-Shop Scheduling Decisions Through RFID: A Simulation-Based Assessment 454

Simulation Software 457

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10.6 System Dynamics Modeling 458 10.7 Agent-Based Modeling 461

APPLICATION CASE 10.5 Agent-Based Simulation Helps Analyze Spread of a Pandemic Outbreak 463

Chapter Highlights 464 • Key Terms 464 Questions for Discussion 465 • Exercises 465

END-OF-CHAPTER APPLICATION CASE HP Applies Management Science Modeling to Optimize Its Supply Chain and Wins a Major Award 465

References 467

Chapter 11 Automated Decision Systems and Expert Systems 469 11.1 Opening Vignette: I nterContinental Hotel Group Uses

Decision Rules for Optimal Hotel Room Rates 470 11.2 Automated Decision Systems 471

APPLICATION CASE 11.1 Giant Food Stores Prices the Entire Store 472

11.3 The Artificial Intelligence Field 475 11.4 Basic Concepts of Expert Systems 477

Experts 477 Expertise 478 Features of ES 478

APPLICATION CASE 11.2 Expert System Helps in Identifying Sport Talents 480

11.5 Applications of Expert Systems 480 APPLICATION CASE 11.3 Expert System Aids in Identification of Chemical, Biological, and Radiological Agents 481

Classical Applications of ES 481 Newer Applications of ES 482 Areas for ES Applications 483

11.6 Structure of Expert Systems 484 Knowledge Acquisition Subsystem 484 Knowledge Base 485 Inference Engine 485 User Interface 485 Blackboard (Workplace) 485 Explanation Subsystem (Justifier) 486 Knowledge-Refining System 486

APPLICATION CASE 11.4 Diagnosing Heart Diseases by Signal Processing 486

11.7 Knowledge Engineering 487 Knowledge Acquisition 488 Knowledge Verification and Validation 490 Knowledge Representation 490 Inferencing 491 Explanation and Justification 496

11.8 Problem Areas Suitable for Expert Systems 497 11.9 Development of Expert Systems 498

Defining the Nature and Scope of the Problem 499 Identifying Proper Experts 499 Acquiring Knowledge 499 Selecting the Building Tools 499 Coding the System 501 Evaluating the System 501 .... APPLICATION CASE 11.5 Clinical Decision Support System for Tendon Injuries 501

11.10 Concluding Remarks 502 Chapter Highlights 503 • Key Terms 503 Questions for Discussion 504 • Exercises 504 .... END·OF·CHAPTER APPLICATION CASE Tax Collections Optimization

for New York State 504 References 505

Chapter 12 Knowledge Management and Collaborative Systems 507 12.1 Opening Vignette: Expertise Transfer System to Train

Future Army Personnel 508 12.2 Introduction to Knowledge Management 512

Knowledge Management Concepts and Definitions 513 Knowledge 513 Explicit and Tacit Knowledge 515

12.3 Approaches to Knowledge Management 516 The Process Approach to Knowledge Management 517 The Practice Approach to Knowledge Management 51 7 Hybrid Approaches to Knowledge Management 51 8 Knowledge Repositories 518

12.4 Information Technology (IT) in Knowledge Management 520 The KMS Cyde 520 Components of KMS 521 Technologies That Support Knowledge Management 521

12.5 Making Decisions in Groups: Characteristics, Process, Benefits, and Dysfunctions 523 Characteristics of Groupwork 523 The Group Decision-Making Process 524 The Benefits and Limitations of Groupwork 524

12.6 Supporting Groupwork with Computerized Systems 526 An Overview of Group Support Systems (GSS) 526 Groupware 527 Time/Place Framework 527

12.7 Tools for Indirect Support of Decision Making 528 Groupware Tools 528

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Groupware 530 Collaborative Workflow 530 Web 2.0 530 Wikis 531 Collaborative Networks 531

12.8 Direct Computerized Support for Decision Making: From Group Decision Support Systems to Group Support Systems 532 Group Decision Support Systems (GOSS) 532 Group Support Systems 533 How GOSS (or GSS) Improve Groupwork 533 Facilities for GOSS 534 Chapter Highlights 535 • Key Terms 536 Questions for Discussion 536 • Exercises 536

END-OF-CHAPTER APPLICATION CASE Solving Crimes by Sharing Digital Forensic Knowledge 537

References 539

Part V Big Data and Future Directions for Business Analytics 541

Chapter 13 Big Data and Analytics 542 13.1 Opening Vignette: Big Data Meets Big Science at CERN 543 13.2 Definition of Big Data 546

The Vs That Define Big Data 547 APPLICATION CASE 13.1 Big Data Analytics Helps Luxottica Improve Its Marketing Effectiveness 550

13.3 Fundamentals of Big Data Analytics 551 Business Problems Addressed by Big Data Analytics 554

APPLICATION CASE 13.2 Top 5 Investment Bank Achieves Single Source of Truth 555

13.4 Big Data Technologies 556 MapReduce 557 Why Use Map Reduce? 558 Hadoop 558 How Does Hadoop Work? 558 Hadoop Technical Components 559 Hadoop: The Pros and Cons 560 NoSQL 562

APPLICATION CASE 13.3 eBay's Big Data Solution 563 13.5 Data Scientist 565

Where Do Data Scientists Come From? 565 APPLICATION CASE 13.4 Big Data and Analytics in Politics 568

13.6 Big Data and Data Warehousing 569 Use Case(s) for Hadoop 570 Use Case(s) for Data Warehousing 571

The Gray Areas (Any One of the Two Would Do the Job) 572 Coexistence of Hadoop and Data Warehouse 572

13.7 Big Data Vendors 574 APPLICATION CASE 13.5 Dublin City Council Is Leveraging Big Data to Reduce Traffic Congestion 575 APPLICATION CASE 13.6 Creditreform Boosts Credit Rating Quality with Big Data Visual Analytics 580

13.8 Big Data and Stream Analytics 581 Stream Analytics Versus Perpetual Analytics 582 Critical Event Processing 582 Data Stream Mining 583

13.9 Applications of Stream Analytics 584 e-commerce 584 Telecommunications 584

APPLICATION CASE 13.7 Turning Machine-Generated Streaming Data into Valuable Business Insights 585

Law Enforcement and Cyber Security 586 Power Industry 587 Financial Services 587 Health Sciences 587 Government 587 Chapter Highlights 588 • Key Terms 588 Questions for Discussion 588 • Exercises 589

END-OF-CHAPTER APPLICATION CASE Discovery Health Turns Big Data into Better Healthcare 589

References 591

Chapter 14 Business Analytics: Emerging Trends and Future Impacts 592 14.1 Opening Vignette: Oklahoma Gas and Electric Employs

Analytics to Promote Smart Energy Use 593 14.2 Location-Based Analytics for Organizations 594

Geospatial Analytics 594 APPLICATION CASE 14.1 Great Clips Employs Spatial Analytics to Shave Time in Location Decisions 596

A Multimedia Exercise in Analytics Employing Geospatial Analytics 597 Real-Time Location Intelligence 598

APPLICATION CASE 14.2 Quiznos Targets Customers for Its Sandwiches 599

14.3 Analytics Applications for Consumers 600 APPLICATION CASE 14.3 A Life Coach in Your Pocket 601

14.4 Recommendation Engines 603 14.5 Web 2.0 and Online Social Networking 604

Representative Characteristics of Web 2.0 605 Social Networking 605 A Definition and Basic Information 606 Implications of Business and Enterprise Social Networks 606

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14.6 Cloud Computing and Bl 607 Service-Oriented DSS 608 Data-as-a-Service (DaaS) 608 Information-as-a-Service (Information on Demand) (laaS) 611 Analytics-as-a-Service (AaaS) 611

14.7 Impacts of Analytics in Organizations: An Overview 613 New Organizational Units 613 Restructuring Business Processes and Virtual Teams 614 The Impacts of ADS Systems 614 Job Satisfaction 614 Job Stress and Anxiety 614 Analytics' Impact on Managers' Activities and Their Performance 615

14.8 Issues of Legality, Privacy, and Ethics 616 Legal Issues 616 Privacy 617 Recent Technology Issues in Privacy and Analytics 618 Ethics in Decision Making and Support 619

14.9 An Overview of the Analytics Ecosystem 620 Analytics Industry Clusters 620 Data Infrastructure Providers 620 Data Warehouse Industry 621 Middleware Industry 622 Data Aggregators/Distributors 622 Analytics-Focused Software Developers 622 Reporting/Analytics 622 Predictive Analytics 623 Prescriptive Analytics 623 Application Developers or System Integrators: Industry Specific or General 624 Analytics User Organizations 625 Analytics Industry Analysts and Influencers 627 Academic Providers and Certification Agencies 628 Chapter Highlights 629 • Key Terms 629 Questions for Discussion 629 • Exercises 630

END·OF·CHAPTER APPLICATION CASE Southern States Cooperative Optimizes Its Catalog Campaign 630

References 632 Glossary 634 Index 648

PREFACE

Analytics has become the technology driver of this decade. Companies such as IBM, Oracle , Microsoft, and others are creating new organizational units focused on analytics that help businesses become more effective and efficient in their operations. Decision makers are using more computerized tools to support their work. Even consumers are using analytics tools directly or indirectly to make decisions on routine activities such as shopping, healthcare, and entertainment. The field of decision support systems (DSS)/ business intelligence (BI) is evolving rapidly to become more focused on innovative appli- cations of data streams that were not even captured some time back, much less analyzed in any significant way. New applications turn up daily in healthcare, sports, entertain- ment, supply chain management, utilities, and virtually every industry imaginable.

The theme of this revised edition is BI and analytics for enterprise decision support. In addition to traditional decision support applications, this edition expands the reader's understanding of the various types of analytics by providing examples, products, services, and exercises by discussing Web-related issues throughout the text. We highlight Web intelligence/Web analytics, which parallel Bl/business analytics (BA) for e-commerce and other Web applications. The book is supported by a Web site (pearsonhighered.com/ sharda) and also by an independent site at dssbibook.com. We will also provide links to software tutorials through a special section of the Web site.

The purpose of this book is to introduce the reader to these technologies that are generally called analytics but have been known by other names. The core technology consists of DSS, BI, and various decision-making techniques. We use these terms inter- changeably. This book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEE approach to introduc- ing these topics: Exposure, Experience, and Explore. The book primarily provides exposure to various analytics techniques and their applications. The idea is that a student will be inspired to learn from how other organizations have employed analytics to make decisions or to gain a competitive edge. We believe that such exposure to what is being done with analytics and how it can be achieved is the key component of learning about analytics. In describing the techniques, we also introduce specific software tools that can be used for developing such applications. The book is not limited to any one software tool , so the students can experience these techniques using any number of available software tools. Specific suggestions are given in each chapter, but the student and the professor are able to use this book with many different software tools. Our book's com- panion Web site will include specific software guides, but students can gain experience with these techniques in many different ways. Finally, we hope that this exposure and experience enable and motivate readers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct them to Teradata University Network and other sites as well that include team-oriented exer- cises where appropriate. We will also highlight new and innovative applications that we learn about on the book's companion Web sites.

Most of the specific improvements made in this tenth edition concentrate on three areas: reorganization, content update, and a sharper focus. Despite the many changes, we have preserved the comprehensiveness and user friendliness that have made the text a market leader. We have also reduced the book's size by eliminating older and redundant material and by combining material that was not used by a majority of professors. At the same time, we have kept several of the classical references intact. Finally, we present accurate and updated material that is not available in any other text. We next describe the changes in the tenth edition.

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WHAT'S NEW IN THE TENTH EDITION? With the goal of improving the text, this edition marks a major reorganization of the text to reflect the focus on analytics. The last two editions transformed the book from the traditional DSS to BI and fostered a tight linkage with the Teradata University Network (TUN). This edition is now organized around three major types of analytics. The new edition has many timely additions , and the dated content has been deleted. The following major specific changes have been made:

• New organization. The book is now organized around three types of analytics: descriptive, predictive, and prescriptive, a classification promoted by INFORMS. After introducing the topics of DSS/ BI and analytics in Chapter 1 and covering the founda- tions of decision making and decision support in Chapter 2, the book begins with an overview of data warehousing and data foundations in Chapter 3. This part then cov- ers descriptive or reporting analytics, specifically, visualization and business perfor- mance measurement. Chapters 5-8 cover predictive analytics. Chapters 9-12 cover prescriptive and decision analytics as well as other decision support systems topics. Some of the coverage from Chapter 3-4 in previous editions will now be found in the new Chapters 9 and 10. Chapter 11 covers expert systems as well as the new rule-based systems that are commonly built for implementing analytics. Chapter 12 combines two topics that were key chapters in earlier editions-knowledge manage- ment and collaborative systems. Chapter 13 is a new chapter that introduces big data and analytics. Chapter 14 concludes the book with discussion of emerging trends and topics in business analytics , including location intelligence, mobile computing, cloud-based analytics, and privacy/ethical considerations in analytics. This chapter also includes an overview of the analytics ecosystem to help the user explore all of the different ways one can participate and grow in the analytics environment. Thus , the book marks a significant departure from the earlier editions in organization. Of course, it is still possible to teach a course with a traditional DSS focus with this book by covering Chapters 1-4, Chapters 9-12, and possibly Chapter 14.

• New chapters. The following chapters have been added:

Chapter 8, "Web Analytics, Web Mining, and Social Analytics." This chapter covers the popular topics of Web analytics and social media analytics. It is an almost entirely new chapter (95% new material). Chapter 13, "Big Data and Analytics." This chapter introduces the hot topics of Big Data and analytics . It covers the basics of major components of Big Data tech- niques and charcteristics. It is also a new chapter (99% new material) . Chapter 14, "Business Analytics: Emerging Trends and Future Impacts." This chapter examines several new phenomena that are already changing or are likely to change analytics . It includes coverage of geospatial in analytics , location- based analytics applications , consumer-oriented analytical applications , mobile plat- forms , and cloud-based analytics. It also updates some coverage from the previous edition on ethical and privacy considerations. It concludes with a major discussion of the analytics ecosystem (90% new material).

• Streamlined coverage. We have made the book shorter by keeping the most commonly used content. We also mostly eliminated the preformatted online con- tent. Instead, we will use a Web site to provide updated content and links on a regular basis. We also reduced the number of references in each chapter.

• Revamped author team. Building upon the excellent content that has been prepared by the authors of the previous editions (Turban, Aronson , Liang, King , Sharda, and Delen) , this edition was revised by Ramesh Sharda and Dursun Delen.

Both Ramesh and Dursun have worked extensively in DSS and analytics and have industry as well as research experience.

• A live-update Web site. Adopters of the textbook will have access to a Web site that will include links to news stories, software, tutorials, and even YouTube videos related to topics covered in the book. This site will be accessible at http://dssbibook.com.

• Revised and updated content. Almost all of the chapters have new opening vignettes and closing cases that are based on recent stories and events. In addition, application cases throughout the book have been updated to include recent exam- ples of applications of a specific technique/model. These application case stories now include suggested questions for discussion to encourage class discussion as well as further exploration of the specific case and related materials . New Web site links have been added throughout the book. We also deleted many older product links and references. Finally, most chapters have new exercises, Internet assign- ments, and discussion questions throughout.

Specific changes made in chapters that have been retained from the previous edi- tions are summarized next:

Chapter 1, "An Overview of Business Intelligence, Analytics, and Decision Support," introduces the three types of analytics as proposed by INFORMS: descriptive , predictive, and prescriptive analytics. A noted earlier, this classification is used in guiding the complete reorganization of the book itself. It includes about 50 percent new material. All of the case stories are new.

Chapter 2, "Foundations and Technologies for Decision Making," combines mate- rial from earlier Chapters 1, 2, and 3 to provide a basic foundation for decision making in general and computer-supported decision making in particular. It eliminates some dupli- cation that was present in Chapters 1-3 of the previous editions. It includes 35 percent new material. Most of the cases are new.

Chapter 3, "Data Warehousing" • 30 percent new material, including the cases • New opening case • Mostly new cases throughout • NEW: A historic perspective to data warehousing-how did we get here? • Better coverage of multidimensional modeling (star schema and snowflake schema) • An updated coverage on the future of data warehousing

Chapter 4, "Business Reporting, Visual Analytics, and Business Performance Management"

• 60 percent of the material is new-especially in visual analytics and reporting • Most of the cases are new

Chapter 5, "Data Mining" • 25 percent of the material is new • Most of the cases are new

Chapter 6, "Techniques for Predictive Modeling" • 55 percent of the material is new • Most of the cases are new • New sections on SVM and kNN

Chapter 7, "Text Analytics, Text Mining, and Sentiment Analysis" • 50 percent of the material is new • Most of the cases are new • New section (1 / 3 of the chapter) on sentiment analysis

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Chapter 8, "Web Analytics, Web Mining, and Social Analytics" (New Chapter) • 95 percent of the material is new

Chapter 9, "Model-Based Decision Making: Optimization and Multi-Criteria Systems" • All new cases • Expanded coverage of analytic hierarchy process • New examples of mixed-integer programming applications and exercises • About 50 percent new material

In addition, all the Microsoft Excel-related coverage has been updated to work with Microsoft Excel 2010. Chapter 10, "Modeling and Analysis: Heuristic Search Methods and Simulation"

• This chapter now introduces genetic algorithms and various types of simulation models

• It includes new coverage of other types of simulation modeling such as agent-based modeling and system dynamics modeling

• New cases throughout • About 60 percent new material

Chapter 11, "Automated Decision Systems and Expert Systems" • Expanded coverage of automated decision systems including examples from the

airline industry • New examples of expert systems • New cases • About 50 percent new material

Chapter 12, "Knowledge Management and Collaborative Systems" • Significantly condensed coverage of these two topics combined into one chapter • New examples of KM applications • About 25 percent new material

Chapters 13 and 14 are mostly new chapters , as described earlier. We have retained many of the enhancements made in the last editions and updated

the content. These are summarized next: • Links to Teradata University Network (TUN). Most chapters include new links

to TUN (teradatauniversitynetwork.com). We encourage the instructors to regis- ter and join teradatauniversitynetwork.com and explore various content available through the site. The cases, white papers , and software exercises available through TUN will keep your class fresh and timely.

• Book title. As is already evident, the book's title and focus have changed substantially.

• Software support. The TUN Web site provides software support at no charge . It also provides links to free data mining and other software. In addition, the site provides exercises in the use of such software.

THE SUPPLEMENT PACKAGE: PEARSONHIGHERED.COM/SHARDA A comprehensive and flexible technology-support package is available to enhance the teaching and learning experience. The following instructor and student supplements are available on the book's Web site, pearsonhighered.com/sharda:

• Instructor's Manual. The Instructor's Manual includes learning objectives for the entire course and for each chapter, answers to the questions and exercises at the end of each chapter, and teaching suggestions (including instructions for projects). The Instructor's Manual is available on the secure faculty section of pearsonhighered .com/sharda.

• Test Item File and TestGen Software. The Test Item File is a comprehensive collection of true/false, multiple-choice, fill-in-the-blank, and essay questions. The questions are rated by difficulty level, and the answers are referenced by book page number. The Test Item File is available in Microsoft Word and in TestGen. Pearson Education's test-generating software is available from www.pearsonhighered. com/ire. The software is PC/MAC compatible and preloaded with all of the Test Item File questions. You can manually or randomly view test questions and drag- and-drop to create a test. You can add or modify test-bank questions as needed. Our TestGens are converted for use in BlackBoard , WebCT, Moodie, D2L, and Angel. These conversions can be found on pearsonhighered.com/sharda. The TestGen is also available in Respondus and can be found on www.respondus.com.

• PowerPoint slides. PowerPoint slides are available that illuminate and build on key concepts in the text. Faculty can download the PowerPoint slides from pearsonhighered.com/ sharda.

ACKNOWLEDGMENTS Many individuals have provided suggestions and criticisms since the publication of the first edition of this book. Dozens of students participated in class testing of various chap- ters , software , and problems and assisted in collecting material. It is not possible to name everyone who participated in this project, but our thanks go to all of them. Certain indi- viduals made significant contributions, and they deserve special recognition.

First, we appreciate the efforts of those individuals who provided formal reviews of the first through tenth editions (school affiliations as of the date of review):

Robert Blanning, Vanderbilt University Ranjit Bose , University of New Mexico Warren Briggs, Suffolk University Lee Roy Bronner, Morgan State University Charles Butler, Colorado State University Sohail S. Chaudry, University of Wisconsin-La Crosse Kathy Chudoba , Florida State University Wingyan Chung, University of Texas Woo Young Chung, University of Memphis Paul "Buddy" Clark, South Carolina State University Pi'Sheng Deng, California State University-Stanislaus Joyce Elam, Florida International University Kurt Engemann, Iona College Gary Farrar, Jacksonville University George Federman, Santa Clara City College Jerry Fjermestad, New Jersey Institute of Technology Joey George , Florida State University Paul Gray , Claremont Graduate School Orv Greynholds, Capital College (Laurel, Maryland) Martin Grossman, Bridgewater State College Ray Jacobs, Ashland University Leonard Jessup , Indiana University Jeffrey Johnson , Utah State University Jahangir Karimi , University of Colorado Denver Saul Kassicieh , University of New Mexico Anand S. Kunnathur, University of Toledo

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xxvi Preface

Shao-ju Lee, California State University at Northridge Yair Levy, Nova Southeastern University Hank Lucas, New York University Jane Mackay, Texas Christian University George M. Marakas, University of Maryland Dick Mason, Southern Methodist University Nick McGaughey, San Jose State University Ido Millet, Pennsylvania State University-Erie Benjamin Mittman, Northwestern University Larry Moore, Virginia Polytechnic Institute and State University Simitra Mukherjee, Nova Southeastern University Marianne Murphy, Northeastern University Peter Mykytyn, Southern Illinois University Natalie Nazarenko, SUNY College at Fredonia Souren Paul, Southern Illinois University Joshua Pauli, Dakota State University Roger Alan Pick, University of Missouri-St. Louis W. "RP" Raghupaphi, California State University-Chico Loren Rees, Virginia Polytechnic Institute and State University David Russell, Western New England College Steve Ruth, George Mason University Vartan Safarian, Winona State University Glenn Shephard, San Jose State University Jung P. Shim, Mississippi State University Meenu Singh, Murray State University Randy Smith, University of Virginia James T.C. Teng, University of South Carolina John VanGigch, California State University at Sacramento David Van Over, University of Idaho Paul J.A. van Vliet, University of Nebraska at Omaha B. S. Vijayaraman, University of Akron Howard Charles Walton, Gettysburg College Diane B. Walz, University of Texas at San Antonio Paul R. Watkins, University of Southern California Randy S. Weinberg, Saint Cloud State University Jennifer Williams, University of Southern Indiana Steve Zanakis, Florida International University Fan Zhao, Florida Gulf Coast University

Several individuals contributed material to the text or the supporting material. Susan Baxley and Dr. David Schrader of Teradata provided special help in identifying new TUN content for the book and arranging permissions for the same. Peter Horner, editor of OR/MS Today, allowed us to summarize new application stories from OR/ MS Today and Analytics Magazine. We also thank INFORMS for their permission to highlight content from Inteifaces. Prof. Rick Wilson contributed some examples and exercise questions for Chapter 9 . Assistance from Natraj Ponna, Daniel Asamoah, Amir Hassan-Zadeh, Kartik Dasika, Clara Gregory, and Amy Wallace (all of Oklahoma State University) is gratefully acknowledged for this edition. We also acknowledge Narges Kasiri (Ithaca College) for the write-up on system dynamics modeling and Jongswas Chongwatpol (NIDA, Thailand) for the material on SIMIO software. For the previous edi- tion , we acknowledge the contributions of Dave King QDA Software Group, Inc.) and

Jerry Wagner (University of Nebraska-Omaha) . Major contributors for earlier editions include Mike Gou! (Arizona State University) and Leila A. Halawi (Bethune-Cookman College), who provided material for the chapter on data warehousing; Christy Cheung (Hong Kong Baptist University), who contributed to the chapter on knowledge man- agement; Linda Lai (Macau Polytechnic University of China); Dave King QDA Software Group, Inc.); Lou Frenzel, an independent consultant whose books Crash Course in Artificial Intelligence and Expert Systems and Understanding of Expert Systems (both published by Howard W. Sams, New York , 1987) provided material for the early edi- tions; Larry Medsker (American University), who contributed substantial material on neu- ral networks; and Richard V. McCarthy (Quinnipiac University), who performed major revisions in the seventh edition.

Previous editions of the book have also benefited greatly from the efforts of many individuals who contributed advice and interesting material (such as problems), gave feedback on material, or helped with class testing. These individuals are Warren Briggs (Suffolk University) , Frank DeBalough (University of Southern California), Mei-Ting Cheung (University of Hong Kong), Alan Dennis (Indiana University), George Easton (San Diego State University), Janet Fisher (California State University, Los Angeles), David Friend (Pilot Software, Inc .) , the late Paul Gray (Claremont Graduate School), Mike Henry (OSU), Dustin Huntington (Exsys , Inc.), Subramanian Rama Iyer (Oklahoma State University), Angie Jungermann (Oklahoma State University), Elena Karahanna (The University of Georgia), Mike McAulliffe (The University of Georgia), Chad Peterson (The University of Georgia), Neil Rabjohn (York University), Jim Ragusa (University of Central Florida) , Alan Rowe (University of Southern California) , Steve Ruth (George Mason University), Linus Schrage (University of Chicago), Antonie Stam (University of Missouri), Ron Swift (NCR Corp.) , Merril Warkentin (then at Northeastern University), Paul Watkins (The University of Southern California), Ben Mortagy (Claremont Graduate School of Management), Dan Walsh (Bellcore), Richard Watson (The University of Georgia), and the many other instructors and students who have provided feedback.

Several vendors cooperated by providing development and/or demonstra- tion software: Expert Choice , Inc. (Pittsburgh, Pennsylvania), Nancy Clark of Exsys , Inc. (Albuquerque, New Mexico), Jim Godsey of GroupSystems, Inc. (Broomfield, Colorado), Raimo Hamalainen of Helsinki University of Technology, Gregory Piatetsky- Shapiro of KDNuggets .com, Logic Programming Associates (UK), Gary Lynn of NeuroDimension Inc. (Gainesville, Florida), Palisade Software (Newfield, New York), Jerry Wagner of Planners Lab (Omaha, Nebraska) , Promised Land Technologies (New Haven, Connecticut) , Salford Systems (La Jolla , California), Sense Networks (New York , New York), Gary Miner of StatSoft, Inc . (Tulsa, Oklahoma) , Ward Systems Group, Inc . (Frederick, Maryland), Idea Fisher Systems, Inc. (Irving, California), and Wordtech Systems (Orinda , California) .

Special thanks to the Teradata University Network and especially to Hugh Watson, Michael Gou!, and Susan Baxley, Program Director, for their encouragement to tie this book with TUN and for providing useful material for the book.

Many individuals helped us with administrative matters and editing, proofreading, and preparation. The project began with Jack Repcheck (a former Macmillan editor), who initiated this project with the support of Hank Lucas (New York University). Judy Lang collaborated with all of us , provided editing, and guided us during the entire project through the eighth edition.

Finally, the Pearson team is to be commended: Executive Editor Bob Horan, who orchestrated this project; Kitty Jarrett, who copyedited the manuscript; and the produc- tion team, Tom Benfatti at Pearson, George and staff at Integra Software Services , who transformed the manuscript into a book.

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We would like to thank all these individuals and corporations. Without their help, the creation of this book would not have been possible. Ramesh and Dursun want to specifically acknowledge the contributions of previous coauthors Janine Aronson, David King, and T. P. Liang, whose original contributions constitute significant components of the book.

R.S.

D.D.

E.T

Note that Web site URLs are dynamic. As this book went to press, we verified that all the cited Web sites were active and valid. Web sites to which we refer in the text sometimes change or are discontinued because compa- nies change names , are bought or sold, merge, or fail. Sometimes Web sites are down for maintenance, repair, or redesign. Most organizations have dropped the initial "www" designation for their sites, but some still use it . If you have a problem connecting to a Web site that we mention , please be patient and simply run a Web search to try to identify the new site. Most times , the new site can be found quickly. Some sites also require a free registration before allowing you to see the content. We apologize in advance for this inconvenience.

ABOUT THE AUTHORS

Ramesh Sharda (M.B.A., Ph.D ., University of Wisconsin-Madison) is director of the Ph.D. in Business for Executives Program and Institute for Research in Information Systems (IRIS), ConocoPhillips Chair of Management of Technology, and a Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University (OSU) . About 200 papers describing his research have been published in major journals, including Operations Research, Management Science, Information Systems Research, Decision Support Systems, and journal of MIS. He cofounded the AIS SIG on Decision Support Systems and Knowledge Management (SIGDSS). Dr. Sharda serves on several editorial boards, including those of INFORMS journal on Computing, Decision Support Systems , and ACM Transactions on Management Information Systems . He has authored and edited several textbooks and research books and serves as the co-editor of several book series (Integrated Series in Information Systems , Operations Research/ Computer Science Interfaces, and Annals of Information Systems) with Springer. He is also currently serving as the executive director of the Teradata University Network. His current research interests are in decision support sys- tems, business analytics, and technologies for managing information overload.

Dursun Delen (Ph.D ., Oklahoma State University) is the Spears and Patterson Chairs in Business Analytics, Director of Research for the Center for Health Systems Innovation, and Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University (OSU). Prior to his academic career, he worked for a privately owned research and consultancy company, Knowledge Based Systems Inc. , in College Station, Texas, as a research scientist for five years, during which he led a number of decision support and other information systems-related research projects funded by federal agencies such as DoD, NASA, NIST, and DOE. Dr. Delen's research has appeared in major journals including Decision Support Systems, Communications of the ACM, Computers and Operations Research, Computers in Industry, journal of Production Operations Management, Artificial Intelligence in Medicine, and Expert Systems with Applications, among others. He recently published four textbooks: Advanced Data Mining Techniques with Springer, 2008; Decision Support and Business Intelligence Systems with Prentice Hall, 2010; Business Intelligence: A Managerial Approach , with Prentice Hall, 2010; and Practical Text Mining, with Elsevier, 2012 . He is often invited to national and international conferences for keynote addresses on topics related to data/ text mining, business intelligence, decision support systems , and knowledge management. He served as the general co-chair for the 4th International Conference on Network Computing and Advanced Information Management (September 2-4, 2008, in Seoul, South Korea) and regularly chairs tracks and mini-tracks at various information systems conferences. He is the associate editor-in-chief for International journal of Experimental Algorithms, associ- ate editor for International journal of RF Technologies and journal of Decision Analytics, and is on the editorial boards of five other technical journals. His research and teaching interests are in data and text mining , decision support systems , knowledge management, business intelligence, and enterprise modeling.

Efraim Turban (M .B.A., Ph .D., University of California, Berkeley) is a visiting scholar at the Pacific Institute for Information System Management, University of Hawaii. Prior to this, he was on the staff of several universities, including City University of Hong Kong; Lehigh University; Florida International University; California State University, Long

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XXX About the Authors

Beach; Eastern Illinois University; and the University of Southern California. Dr. Turban is the author of more than 100 refereed papers published in leading journals, such as Management Science, MIS Quarterly, and Decision Support Systems. He is also the author of 20 books , including Electronic Commerce: A Managerial Perspective and Information Technology for Management. He is also a consultant to major corporations worldwide. Dr. Turban's current areas of interest are Web-based decision support systems, social commerce, and collaborative decision making.

P A R T

Decision Making and Analytics An Overview

LEARNING OBJECTIVES FOR PART I

• Understand the need for business analytics • Understand the foundations and key issues of

managerial decision making • Understand the major categories and

applications of business analytics

• Learn the major frameworks of computerized decision support: analytics, decision support systems (DSS), and business intelligence (BI)

This book deals with a collection of computer technologies that support managerial work-essentially, decision making. These technologies have had a profound impact on corporate strategy, perfor- mance, and competitiveness. These techniques broadly encompass analytics, business intelligence, and decision support systems, as shown throughout the book. In Part I, we first provide an overview of the whole book in one chapter. We cover several topics in this chapter. The first topic is managerial decision making and its computerized support; the second is frameworks for decision support. We then introduce business analytics and business intelligence. We also provide examples of applications of these analytical techniques, as well as a preview of the entire book. The second chapter within Part I introduces the foundational methods for decision making and relates these to computerized decision support. It also covers the components and technologies of decision support systems.

An Overview of Business Intelligence, Analytics, and Decision Support

LEARNING OBJECTIVES

• Understand today's turbulent business environment and describe how organizations survive and even excel in such an environment (solving problems and exploiting opportunities)

• Understand the need for computerized support of managerial decision making

• Understand an early framework for managerial decision making

• Learn the conceptual foundations of the decision support systems (DSS1) methodology

• Describe the business intelligence (BI) methodology and concepts and relate them to DSS

• Understand the various types of analytics • List the major tools of computerized

decision support

The business environment (climate) is constantly changing, and it is becoming more and more complex. Organizations, private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex. Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these, in the framework of the needed decisions, must be done quickly, frequently in real time, and usually requires some computerized support.

This book is about using business analytics as computerized support for manage- rial decision making. It concentrates on both the theoretical and conceptual founda- tions of decision support, as well as on the commercial tools and techniques that are available. This introductory chapter provides more details of these topics as well as an overview of the book. This chapter has the following sections:

1.1 Opening Vignette: Magpie Sensing Employs Analytics to Manage a Vaccine Supply Chain Effectively and Safely 3

1.2 Changing Business Environments and Computerized Decision Support 5

'The acronym DSS is treated as both singular and plural throughout this book. Similarly, other acronyms, such as MIS and GSS, designate both plural and singular forms. This is also true of the word analytics.

Chapter 1 • An Overview of Business Intelligence, Analytics, and Decision Support 3

1.3 Managerial Decision Making 7 1.4 Information Systems Support for Decision Making 9 1.5 An Early Framework for Computerized Decision Support 11 1.6 The Concept of Decision Support Systems (DSS) 13 1. 7 A Framework for Business Intelligence (BI) 14 1.8 Business Analytics Overview 19 1.9 Brief Introduction to Big Data Analytics 27

1.10 Plan of the Book 29 1.11 Resources, Links, and the Teradata University Network Connection 31

1.1 OPENING VIGNETTE: Magpie Sensing Employs Analytics to Manage a Vaccine Supply Chain Effectively and Safely

Cold chain in healthcare is defined as the temperature-controlled supply chain involving a system of transporting and storing vaccines and pharmaceutical drugs. It consists of three major components-transport and storage equipment, trained personnel, and efficient management procedures. The majority of the vaccines in the cold chain are typically main- tained at a temperature of 35--46 degrees Fahrenheit [2-8 degrees Centigrade]. Maintaining cold chain integrity is extremely important for healthcare product manufacturers.

Especially for the vaccines, improper storage and handling practices that compromise vaccine viability prove a costly, time-consuming affair. Vaccines must be stored properly from manufacture until they are available for use. Any extreme temperatures of heat or cold will reduce vaccine potency; such vaccines, if administered, might not yield effective results or could cause adverse effects .

Effectively maintaining the temperatures of storage units throughout the healthcare supply chain in real time-Le., beginning from the gathering of the resources, manufac- turing, distribution, and dispensing of the products-is the most effective solution desired in the cold chain. Also, the location-tagged real-time environmental data about the storage units helps in monitoring the cold chain for spoiled products. The chain of custody can be easily identified to assign product liability.

A study conducted by the Centers for Disease Control and Prevention ( CDC) looked at the handling of cold chain vaccines by 45 healthcare providers around United States and reported that three-quarters of the providers experienced serious cold chain violations.

A WAY TOWARD A POSSIBLE SOLUTION

Magpie Sensing, a start-up project under Ebers Smith and Douglas Associated LLC, pro- vides a suite of cold chain monitoring and analysis technologies for the healthcare indus- try. It is a shippable, wireless temperature and humidity monitor that provides real-time, location-aware tracking of cold chain products during shipment. Magpie Sensing's solu- tions rely on rich analytics algorithms that leverage the data gathered from the monitor- ing devices to improve the efficiency of cold chain processes and predict cold storage problems before they occur.

Magpie sensing applies all three types of analytical techniques-descriptive, predic- tive, and prescriptive analytics-to tum the raw data returned from the monitoring devices into actionable recommendations and warnings.

The properties of the cold storage system, which include the set point of the storage system's thermostat, the typical range of temperature values in the storage system, and

4 Part I • Decision Making and Analytics: An Oveiview

the duty cycle of the system's compressor, are monitored and reported in real time. This information helps trained personnel to ensure that the storage unit is properly configured to store a particular product. All the temperature information is displayed on a Web dash- board that shows a graph of the temperature inside the specific storage unit.

Based on information derived from the monitoring devices, Magpie's predictive ana- lytic algorithms can determine the set point of the storage unit's thermostat and alert the system's users if the system is incorrectly configured, depending upon the various types of products stored. This offers a solution to the users of consumer refrigerators where the thermostat is not temperature graded. Magpie's system also sends alerts about pos- sible temperature violations based on the storage unit's average temperature and subse- quent compressor cycle runs, which may drop the temperature below the freezing point. Magpie 's predictive analytics further report possible human errors, such as failure to shut the storage unit doors or the presence of an incomplete seal, by analyzing the tempera- ture trend and alerting users via Web interface, text message , or audible alert before the temperature bounds are actually violated. In a similar way, a compressor or a power failure can be detected; the estimated time before the storage unit reaches an unsafe tem- perature also is reported, which prepares the users to look for backup solutions such as using dry ice to restore power.

In addition to predictive analytics, Magpie Sensing's analytics systems can provide prescriptive recommendations for improving the cold storage processes and business decision making. Prescriptive analytics help users dial in the optimal temperature setting, which helps to achieve the right balance between freezing and spoilage risk; this, in turn, provides a cushion-time to react to the situation before the products spoil. Its prescriptive analytics also gather useful meta-information on cold storage units, including the times of day that are busiest and periods where the system's doors are opened, which can be used to provide additional design plans and institutional policies that ensure that the system is being properly maintained and not overused.

Furthermore, prescriptive analytics can be used to guide equipment purchase deci- sions by constantly analyzing the performance of current storage units. Based on the storage system's efficiency, decisions on distributing the products across available storage units can be made based on the product's sensitivity.

Using Magpie Sensing's cold chain analytics, additional manufacturing time and expenditure can be eliminated by ensuring that product safety can be secured throughout the supply chain and effective products can be administered to the patients. Compliance with state and federal safety regulations can be better achieved through automatic data gathering and reporting about the products involved in the cold chain.

QUESTIONS FOR THE OPENING VIGNETTE

1. What information is provided by the descriptive analytics employed at Magpie Sensing?

2. What type of support is provided by the predictive analytics employed at Magpie Sensing?

3. How does prescriptive analytics help in business decision making? 4. In what ways can actionable information be reported in real time to concerned

users of the system? 5. In what other situations might real-time monitoring applications be needed?

WHAT WE CAN LEARN FROM THIS VIGNETIE

This vignette illustrates how data from a business process can be used to generate insights at various levels. First, the graphical analysis of the data (termed reporting analytics) allows

Chapter 1 • An Overview of Business Intelligence, Analytics, and Decision Support 5

users to get a good feel for the situation. Then, additional analysis using data mining techniques can be used to estimate what future behavior would be like. This is the domain of predictive analytics. Such analysis can then be taken to create specific recommendations for operators. This is an example of what we call prescriptive analytics. Finally, this open- ing vignette also suggests that innovative applications of analytics can create new business ventures. Identifying opportunities for applications of analytics and assisting with decision making in specific domains is an emerging entrepreneurial opportunity.

Sources: Magpiesensing.com, "Magpie Sensing Cold Chain Analytics and Monitoring," magpiesensing.com/ wp-content/uploads/2013/01/ColdChainAnalyticsMagpieSensing-Whitepaper.pdf (accessed July 2013); Centers for Disease Control and Prevention, Vaccine Storage and Handling, http://www.cdc.gov/vaccines/pubs/ pinkbook/vac-storage.html#storage (accessed July 2013); A. Zaleski, "Magpie Analytics System Tracks Cold- Chain Products to Keep Vaccines, Reagents Fresh" (2012), technicallybaltimore.com/profiles/startups/magpie- analytics-system-track.s-cold-chain-products-to-keep-vaccines-reagents-fresh (accessed February 2013).

1.2 CHANGING BUSINESS ENVIRONMENTS AND COMPUTERIZED DECISION SUPPORT

The opening vignette illustrates how a company can employ technologies to make sense of data and make better decisions. Companies are moving aggressively to computerized support of their operations. To understand why companies are embracing computer- ized support, including business intelligence, we developed a model called the Business Pressures-Responses-Support Model, which is shown in Figure 1.1.

The Business Pressures-Responses-Support Model The Business Pressures-Responses-Support Model, as its name indicates, has three com- ponents: business pressures that result from today's business climate, responses (actions taken) by companies to counter the pressures (or to take advantage of the opportunities available in the environment), and computerized support that facilitates the monitoring of the environment and enhances the response actions taken by organizations.

Business Environmental Factors

Globalization Customer demand Government regulations Market conditions Competition Etc.

Pressures

Opportunities

Organization Respon ses

Strategy Partners' collaboration Real-time response Agility Increased productivity New vendors New business models Etc.

FIGURE 1.1 The Business Pressures-Responses-Support Model.

Decisions and Support

Analyses Predictions Decisions

i i i Integrated computerized decision support

Business intelligence

6 Part I • Decision Making and Analytics: An Overview

THE BUSINESS ENVIRONMENT The environment in which organizations operate today is becoming more and more complex. This complexity creates opportunities on the one hand and problems on the other. Take globalization as an example. Today, you can eas- ily find suppliers and customers in many countries, which means you can buy cheaper materials and sell more of your products and services; great opportunities exist. However, globalization also means more and stronger competitors. Business environment factors can be divided into four major categories: markets, consumer demands, technology, and societal. These categories are summarized in Table 1.1.

Note that the intensity of most of these factors increases with time, leading to more pressures, more competition, and so on. In addition, organizations and departments within organizations face decreased budgets and amplified pressures from top managers to increase performance and profit. In this kind of environment, managers must respond quickly, innovate, and be agile. Let's see how they do it.

ORGANIZATIONAL RESPONSES: BE REACTIVE, ANTICIPATIVE, ADAPTIVE, AND PROACTIVE Both private and public organizations are aware of today's business environment and pressures. They use different actions to counter the pressures. Vodafone New Zealand Ltd (Krivda, 2008), for example, turned to BI to improve communication and to support executives in its effort to retain existing customers and increase revenue from these cus- tomers. Managers may take other actions, including the following:

• Employ strategic planning. • Use new and innovative business models. • Restructure business processes. • Participate in business alliances. • Improve corporate information systems. • Improve partnership relationships.

TABLE 1.1 Business Environment Factors That Create Pressures on Organizations

Factor Description

Markets Strong competition Expanding global markets

Consumer demands

Technology

Societal

Booming electronic markets on the Internet Innovative marketing methods Opportunities for outsourcing with IT support Need for real-time, on-demand transactions Desire for customization Desire for quality, diversity of products, and speed of delivery Customers getting powerful and less loyal More innovations, new products, and new services Increasing obsolescence rate Increasing information overload Social networking, Web 2.0 and beyond Growing government regulations and deregulation Workforce more diversified, older, and composed of more women Prime concerns of homeland security and terrorist attacks Necessity of Sarbanes-Oxley Act and other reporting-related legislation Increasing social responsibility of companies Greater emphasis on sustainability

Chapter 1 • An Overview of Business Intelligence , Analytics, and Decision Support 7

• Encourage innovation and creativity. • Improve customer service and relationships. • Employ social media and mobile platforms for e-commerce and beyond. • Move to make-to-order production and on-demand manufacturing and services . • Use new IT to improve communication, data access (discovery of information) , and

collaboration. • Respond quickly to competitors' actions (e.g., in pricing, promotions, new products

and services). • Automate many tasks of white-collar employees. • Automate certain decision processes, especially those dealing with customers. • Improve decision making by employing analytics.

Many, if not all, of these actions require some computerized support. These and other response actions are frequently facilitated by computerized decision support (DSS).

CLOSING THE STRATEGY GAP One of the major objectives of computerized decision support is to facilitate closing the gap between the current performance of an organi- zation and its desired performance, as expressed in its mission, objectives, and goals, and the strategy to achieve them. In order to understand why computerized support is needed and how it is provided, especially for decision-making support, let's look at managerial decision making.

SECTION 1.2 REVIEW QUESTIONS

1. List the components of and explain the Business Pressures-Responses-Support Model.

2. What are some of the major factors in today's business environment? 3. What are some of the major response activities that organizations take?

1.3 MANAGERIAL DECISION MAKING Management is a process by which organizational goals are achieved by using resources . The resources are considered inputs, and attainment of goals is viewed as the output of the process. The degree of success of the organization and the manager is often measured by the ratio of outputs to inputs. This ratio is an indication of the organization's productivity, which is a reflection of the organizational and managerial pe,fonnance.

The level of productivity or the success of management depends on the perfor- mance of managerial functions, such as planning, organizing, directing, and control- ling. To perform their functions , managers engage in a continuous process of making decisions. Making a decision means selecting the best alternative from two or more solutions.

The Nature of Managers' Work Mintzberg's (2008) classic study of top managers and several replicated studies suggest that managers perform 10 major roles that can be classified into three major categories : interpersonal, infonnational, and decisional (see Table 1.2).

To perform these roles, managers need information that is delivered efficiently and in a timely manner to personal computers (PCs) on their desktops and to mobile devices. This information is delivered by networks, generally via Web technologies.

In addition to obtaining information necessary to better perform their roles, manag- ers use computers directly to support and improve decision making, which is a key task

8 Part I • Decision Making and Analytics: An Overview

TABLE 1.2 Mintzberg's 10 Managerial Roles

Role Interpersonal Figurehead

Leader

Liaison

Informational Monitor

Disseminator

Spokesperson

Decisional Entrepreneur

Disturbance handler

Resource allocator

Negotiator

Description

Is symbolic head; obliged to perform a number of routine duties of a legal or social nature

Is responsible for the motivation and activation of subordinates; responsible for staffing, training, and associated duties

Maintains self-developed network of outside contacts and informers who provide favors and information

Seeks and receives a wide variety of special information (much of it current) to develop a thorough understanding of the organization and environment; emerges as the nerve center of the organization's internal and external information

Transmits information received from outsiders or from subordinates to members of the organization; some of this information is factual, and some involves interpretation and integration

Transmits information to outsiders about the organization's plans, policies, actions, results, and so forth; serves as an expert on the organization's industry

Searches the organization and its environment for opportunities and initiates improvement projects to bring about change; supervises design of certain projects

Is responsible for corrective action when the organization faces important, unexpected disturbances

Is responsible for the allocation of organizational resources of all kinds; in effect, is responsible for the making or approval of all significant organizational decisions

Is responsible for representing the organization at major negotiations

Sources: Compiled from H. A. Mintzberg, The Nature of Managerial Work. Prentice Hall, Englew ood Cliffs, NJ, 1980; and H. A. Mintzberg, The Rise and Fall of Strategic Planning. The Free Press, New York, 1993.

that is part of most of these roles. Many managerial activities in all roles revolve around decision making. Managers, especially those at high managerial levels, are primarily deci- sion makers. We review the decision-making process next but will study it in more detail in the next chapter.

The Decision-Making Process

For years, managers considered decision making purely an art-a talent acquired over a long period through experience (i.e., learning by trial-and-error) and by using intuition. Management was considered an art because a variety of individual styles could be used in approaching and successfully solving the same types of managerial problems. These styles were often based on creativity, judgment, intuition, and experience rather than on systematic quantitative methods grounded in a scientific approach. However, recent research suggests that companies with top managers who are more focused on persistent work (almost dullness) tend to outperform those with leaders whose main strengths are interpersonal communication skills (Kaplan et al., 2008; Brooks, 2009). It is more impor- tant to emphasize methodical, thoughtful, analytical decision making rather than flashi- ness and interpersonal communication skills.

Chapter 1 • An Overview of Business Intelligence , Analytics, and Decision Support 9

Managers usually make decisions by following a four-step process C we learn more about these in Chapter 2):

1. Define the problem (i.e., a decision situation that may deal with some difficulty or with an opportunity).

2. Construct a model that describes the real-world problem. 3. Identify possible solutions to the modeled problem and evaluate the solutions. 4. Compare, choose, and recommend a potential solution to the problem.

To follow this process, one must make sure that sufficient alternative solutions are being considered, that the consequences of using these alternatives can be reasonably predicted, and that comparisons are done properly. However, the environmental factors listed in Table 1.1 make such an evaluation process difficult for the following reasons:

• Technology, information systems, advanced search engines, and globalization result in more and more alternatives from which to choose.

• Government regulations and the need for compliance, political instability and ter- rorism, competition, and changing consumer demands produce more uncertainty, making it more difficult to predict consequences and the future.

• Other factors are the need to make rapid decisions, the frequent and unpredictable changes that make trial-and-error learning difficult, and the potential costs of making mistakes.

• These environments are growing more complex every day. Therefore, making deci- sions today is indeed a complex task.

Because of these trends and changes, it is nearly impossible to rely on a trial-and- error approach to management, especially for decisions for which the factors shown in Table 1.1 are strong influences. Managers must be more sophisticated; they must use the new tools and techniques of their fields. Most of those tools and techniques are discussed in this book. Using them to support decision making can be extremely rewarding in making effective decisions. In the following section, we look at why we need computer support and how it is provided.

SECTION 1.3 REVIEW QUESTIONS

1. Describe the three major managerial roles , and list some of the specific activities in each. 2. Why have some argued that management is the same as decision making? 3. Describe the four steps managers take in making a decision.

1.4 INFORMATION SYSTEMS SUPPORT FOR DECISION MAKING From traditional uses in payroll and bookkeeping functions, computerized systems have penetrated complex managerial areas ranging from the design and management of auto- mated factories to the application of analytical methods for the evaluation of proposed mergers and acquisitions. Nearly all executives know that information technology is vital to their business and extensively use information technologies.

Computer applications have moved from transaction processing and monitoring activities to problem analysis and solution applications, and much of the activity is done with Web-based technologies, in many cases accessed through mobile devices. Analytics and BI tools such as data warehousing, data mining, online analytical processing (OLAF) , dashboards , and the use of the Web for decision support are the cornerstones of today's modern management. Managers must have high-speed, networked information sys- tems (wireline or wireless) to assist them with their most important task: making deci- sions. Besides the obvious growth in hardware, software, and network capacities, some

10 Part I • Decision Making and Analytics: An Oveiview

developments have clearly contributed to facilitating growth of decision support and analytics in a number of ways, including the following:

• Group communication and collaboration. Many decisions are made today by groups whose members may be in different locations. Groups can collaborate and communicate readily by using Web-based tools as well as the ubiquitous smartphones. Collaboration is especially important along the supply chain, where partners-all the way from vendors to customers-must share information. Assembling a group of decision makers, especially experts, in one place can be costly. Infonnation systems can improve the collaboration process of a group and enable its members to be at dif- ferent locations (saving travel costs). We will study some applications in Chapter 12.

• Improved data management. Many decisions involve complex computations. Data for these can be stored in different databases anywhere in the organization and even possibly at Web sites outside the organization. The data may include text, sound, graphics, and video, and they can be in different languages. It may be neces- sary to transmit data quickly from distant locations. Systems today can search, store, and transmit needed data quickly, economically, securely, and transparently.

• Managing giant data warehouses and Big Data. Large data warehouses, like the ones operated by Walmart, contain terabytes and even petabytes of data. Special methods , including parallel computing, are available to organize, search, and mine the data. The costs related to data warehousing are declining. Technologies that fall under the broad category of Big Data have enabled massive data coming from a variety of sources and in many different forms, which allows a very different view into organizational performance that was not possible in the past.

• Analytical support. With more data and analysis technologies, more alterna- tives can be evaluated, forecasts can be improved, risk analysis can be performed quickly, and the views of experts (some of whom may be in remote locations) can be collected quickly and at a reduced cost. Expertise can even be derived directly from analytical systems. With such tools , decision makers can perform complex simulations, check many possible scenarios, and assess diverse impacts quickly and economically. This, of course, is the focus of several chapters in the book.

• Overcoming cognitive limits in processingandstoringinformation. According to Simon 0977), the human mind has only a limited ability to process and store infor- mation. People sometimes find it difficult to recall and use infonnation in an error-free fashion due to their cognitive limits. The term cognitive limits indicates that an indi- vidual's problem-solving capability is limited when a wide range of diverse information and knowledge is required. Computerized systems enable people to overcome their cognitive limits by quickly accessing and processing vast amounts of stored information (see Chapter 2).

• Knowledge management. Organizations have gathered vast stores of informa- tion about their own operations, customers, internal procedures, employee interac- tions , and so forth through the unstructured and structured communications taking place among the various stakeholders. Knowledge management systems (KMS , Chapter 12) have become sources of formal and informal support for decision making to managers, although sometimes they may not even be called KMS.

• Anywhere, any time support. Using wireless technology, managers can access information anytime and from any place , analyze and interpret it, and communicate with those involved. This perhaps is the biggest change that has occurred in the last few years. The speed at which information needs to be processed and converted into decisions has truly changed expectations for both consumers and businesses .

These and other capabilities have been driving tl1e use of computerized decision support since the late 1960s, but especially since the mid-1990s. The growth of mobile technologies ,

Chapter 1 • An Overview of Business Intelligence, Analytics, and Decision Support 11

social media platforms, and analytical tools has enabled a much higher level of information systems support for managers. In the next sections we study a historical classification of decision support tasks. This leads us to be introduced to decision support systems. We will then study an overview of technologies that have been broadly referred to as business intel- ligence. From there we will broaden our horizons to introduce various types of analytics.

SECTION 1.4 REVIEW QUESTIONS

1. What are some of the key system-oriented trends that have fostered IS-supported decision making to a new level?

2. List some capabilities of information systems that can facilitate managerial decision making.

3. How can a computer help overcome the cognitive limits of humans?

1.5 A N EARLY FRAMEWORK FOR COMPUTERIZED DECISION SUPPORT

An early framework for computerized decision support includes several major concepts that are used in forthcoming sections and chapters of this book. Gorry and Scott-Morton created and used this framework in the early 1970s, and the framework then evolved into a new technology called DSS.

The Gorry and Scott-Morton Classical Framework Gorry and Scott-Morton 0971) proposed a framework that is a 3-by-3 matrix, as shown in Figure 1.2. The two dimensions are the degree of structuredness and the types of control.

Type of Control

Operational Managerial S trategic Type of Decision Contro l Control Planning

L!_ l!_ l!_ Accounts receivable Budget analysis Financial management

Structured Accounts payable Short-term forecasting Investment portfolio Order entry Personnel reports Warehouse location

Make-or-buy Distribution systems

l!_ l!_ Production scheduling Credit evaluation Building a new plant Inventory control Budget preparation Mergers & acquisitions

S emistructured Plant layout New product planning Project scheduling Compensation planning Reward system design Quality assurance Inventory HR policies

categorization Inventory planning

L!_ l!_ l!_ Buying software Negotiating R & D planning

Unstructured Approving loans Recruiting an executive New tech development Operating a help desk Buying hardware Social responsibility Selecting a cover for Lobbying planning

a magazine

FIGURE 1.2 Decision Support Frameworks.

12 Part I • Decision Making and Analytics: An Oveiview

DEGREE OF STRUCTUREDNESS The left side of Figure 1.2 is based on Simon's (1977) idea that decision-making processes fall along a continuum that ranges from highly structured (sometimes called programmed) to highly unstructured (i.e., nonprogrammed) decisions. Structured processes are routine and typically repetitive problems for which standard solution methods exist. Unstrnctured processes are fuzzy, complex problems for which there are no cut-and-dried solution methods.

An unstructured problem is one where the articulation of the problem or the solu- tion approach may be unstructured in itself. In a structured problem, the procedures for obtaining the best (or at least a good enough) solution are known. Whether the prob- lem involves finding an appropriate inventory level or choosing an optimal investment strategy, the objectives are clearly defined. Common objectives are cost minimization and profit maximization.

Semistructured problems fall between structured and unstructured problems, hav- ing some structured elements and some unstructured elements. Keen and Scott-Morton 0978) mentioned trading bonds, setting marketing budgets for consumer products, and performing capital acquisition analysis as semistructured problems.

TYPES OF CONTROL The second half of the Gorry and Scott-Morton framework (refer to Figure 1.2) is based on Anthony's 0965) taxonomy, which defines three broad categories that encompass all managerial activities: strategic planning, which involves defining long-range goals and policies for resource allocation; manage- ment control, the acquisition and efficient use of resources in the accomplishment of organizational goals; and operational control, the efficient and effective execution of specific tasks.

THE DECISION SUPPORT MATRIX Anthony's and Simon's taxonomies are combined in the nine-cell decision support matrix shown in Figure 1.2. The initial purpose of this matrix was to suggest different types of computerized support to different cells in the matrix. Gorry and Scott-Morton suggested, for example, that for semistructured decisions and unstrnctured decisions, conventional management information systems (MIS) and man- agement science (MS) tools are insufficient. Human intellect and a different approach to computer technologies are necessary. They proposed the use of a supportive information system, which they called a DSS.

Note that the more structured and operational control-oriented tasks (such as those in cells 1, 2, and 4) are usually performed by lower-level managers, whereas the tasks in cells 6, 8, and 9 are the responsibility of top executives or highly trained specialists.

Computer Support for Structured Decisions Computers have historically supported structured and some semistructured decisions, especially those that involve operational and managerial control, since the 1960s. Operational and managerial control decisions are made in all functional areas , especially in finance and production (i.e., operations) management.

Structured problems, which are encountered repeatedly, have a high level of struc- ture . It is therefore possible to abstract, analyze , and classify them into specific catego- ries. For example, a make-or-buy decision is one category. Other examples of categories are capital budgeting, allocation of resources, distribution, procurement, planning, and inventory control decisions. For each category of decision, an easy-to-apply prescribed model and solution approach have been developed, generally as quantitative formulas. Therefore, it is possible to use a scientific approach for automating portions of manage- rial decision making.

Chapter 1 • An Overview of Business Intelligence , Analytics, and Decision Support 13

Computer Support for Unstructured Decisions Unstructured problems can be only partially supported by standard computerized quan- titative methods. It is usually necessary to develop customized solutions. However, such solutions may benefit from data and information generated from corporate or external data sources. Intuition and judgment may play a large role in these types of decisions, as may computerized communication and collaboration technologies, as well as knowledge management (see Chapter 12).

Computer Support for Semistructured Problems Solving semistructured problems may involve a combination of standard solution pro- cedures and human judgment. Management science can provide models for the portion of a decision-making problem that is structured. For the unstructured portion, a DSS can improve the quality of the information on which the decision is based by providing, for example, not only a single solution but also a range of alternative solutions, along with their potential impacts. These capabilities help managers to better understand the nature of problems and, thus, to make better decisions.

SECTION 1.5 REVIEW QUESTIONS

1. What are structured, unstructured, and semistructured decisions? Provide two exam- ples of each.

2. Define operational control, managerial control, and strategic planning. Provide two examples of each.

3. What are the nine cells of the decision framework? Explain what each is for. 4. How can computers provide support for making structured decisions? 5. How can computers provide support to semistructured and unstructured decisions?

1.6 THE CONCEPT OF DECISION SUPPORT SYSTEMS (DSS) In the early 1970s, Scott-Morton first articulated the major concepts of DSS. He defined decision support systems (DSS) as "interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems" (Gorry and Scott-Morton, 1971). The following is another classic DSS definition, provided by Keen and Scott-Morton 0978):

Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision makers who deal with semistructured problems.

Note that the term decision support system, like management information system (MIS) and other terms in the field of IT, is a content-free expression (i.e., it means different things to different people). Therefore, there is no universally accepted definition of DSS. (We present additional definitions in Chapter 2.) Actually, DSS can be viewed as a con- ceptual methodology-that is, a broad, umbrella term. However, some view DSS as a nar- rower, specific decision support application.

DSS as an Umbrella Term The term DSS can be used as an umbrella term to describe any computerized system that supports decision making in an organization. An organization may have a knowledge

14 Part I • Decision Making and Analytics: An Oveiview

management system to guide all its personnel in their problem solving. Another organiza- tion may have separate support systems for marketing, finance , and accounting; a sup- ply chain management (SCM) system for production; and several rule-based systems for product repair diagnostics and help desks . DSS encompasses them all.

Evolution of DSS into Business Intelligence

In the early days of DSS , managers let their staff do some supportive analysis by using DSS tools. As PC technology advanced, a new generation of managers evolved-one that was comfortabl e with computing and knew that technology can directly h elp make intelligent business decisions faster. New tools such as OLAP, data warehousing, data mining, and intelligent systems , delivered via Web technology, added promised capabilities and easy access to tools, models, and data for computer-aided decision making. These tools started to appear under the names BI and business analytics in the mid-1990s . We introduce these concepts next , and relate the DSS and BI concepts in the following section s.

SECTION 1.6 REVIEW QUESTIONS

1. Provide two definitions of DSS. 2. Describe DSS as an umbrella term.

1.7 A FRAMEWORK FOR BUSINESS INTELLIGENCE (Bl) The decision support concepts presented in Sections 1.5 and 1.6 have been implemented incrementally, under different names, by many vendors that have created tools and meth- odologies for decision support. As the enterprise-wide systems grew, managers were able to access user-friendly reports that enabled them to make decisions quickly . These systems, which were generally called executive information systems (EIS), then began to offer additional visualization, alerts , and performance measurement capabilities. By 2006, the major commercial products and services appeared under the umbrella term business intelligence (BI).

Definitions of Bl

Business intelligence (BI) is an umbrella term that combines architectures, tools , data- bases, analytical tools, applications, and methodologies. It is, like DSS, a content-free expression, so it means different things to different peopl e. Part of the confusion about BI lies in the flurry of acronyms and buzzwords that are associated with it (e.g., business performance management [BPM]). Bi's major objective is to enable interactive access (sometimes in real time) to data, to enable manipulation of data, and to give business managers and analysts the ability to conduct appropriate analyses . By analyzing historical and current data, situations, and performances, decision makers get valuable insights that enable them to make more informed and better decisions . The process of BI is based on the traniformation of data to information , then to decisions, and finally to actions .

A Brief History of Bl

The term BJ was coined by the Gartner Group in the mid-1990s. However, the concept is much older; it has its roots in the MIS reporting systems of the 1970s. During that period, reporting systems were static, two dimensional, and had no analytical capabilities . In the early 1980s, the concept of executive infonnation systems (EIS) emerged. This concept expanded the computerized support to top-level managers and executives. Some of the

Chapter 1 • An Overview of Business Intelligence , Analytics, and Decision Support 15

Querying and reportin

Data warehouse

EIS/ESS

Financial reporting

DLAP

I Digital cockpits L

. and dashboardsr,-----------··

I Scorecards and Ll-------------11>

_ dashboards I Workflow

Alerts and notifications

mining Predictive analytics

FIGURE 1.3 Evolution of Business Intelligence (Bl).

Data marts

Business intelligence

Broadcasting tools Portals

capabilities introduced were dynamic multidimensional (ad hoc or on-demand) reporting, forecasting and prediction, trend analysis, drill-down to details , status access, and criti- cal success factors. These features appeared in dozens of commercial products until the mid-1990s . Then the same capabilities and some new ones appeared under the name BI. Today, a good BI-based enterprise information system contains all the information execu- tives need. So, the original concept of EIS was transformed into BI. By 2005 , BI systems started to include a-rtificial intelligence capabilities as well as powerful analytical capabili- ties. Figure 1.3 illustrates the various tools and techniques that may be included in a BI system. It illustrates the evolution of BI as well. The tools shown in Figure 1.3 provide the capabilities of BI. The most sophisticated BI products include most of these capabilities; others specialize in only some of them. We will study several of these capabilities in more detail in Chapters 5 through 9.

The Architecture of Bl

A BI system has four major components: a data warehouse, with its source data; business analytics, a collection of tools for manipulating, mining, and analyzing the data in the data warehouse; business peiformance management {BPM) for monitoring and analyzing perfor- mance; and a userinteiface (e.g., a dashboard). The relationship among these components is illustrated in Figure 1.4. We will discuss these components in detail in Chapters 3 through 9.

Styles of Bl

The architecture of BI depends on its applications. MicroStrategy Corp. distinguishes five styles of BI and offers special tools for each. The five styles are report delivery and alert- ing; enterprise reporting (using dashboards and scorecards); cube analysis (also known as slice-and-dice analysis); ad hoc queries; and statistics and data mining.

16 Part I • Decision Making and Analytics: An Oveiview

Data

------------

Data Warehouse Environment

Technical staff Build the data warehouse

Organizing Summarizing Standardizing

Future component: intelligent systems

Business Analytics Environment

Manipulacion, results ------ User interface

Performance and Strategy

Managers/ executives -- 8PM strategies

I Browser Portal 0 Dashboard

FIGURE 1.4 A High-Level Architecture of Bl. Source: Based on W. Eckerson, Smart Companies in the 21st Century: The Secrets of Creating Successful Business Intelligent Solutions. The Data Warehousing Institute, Seattle, WA, 2003, p. 32, Illustration 5.

The Origins and Drivers of Bl

Where did modern approaches to data warehousing (DW) and BI come from? What are their roots, and how do those roots affect the way organizations are managing these initia- tives today? Today's investments in information technology are under increased scrutiny in terms of their bottom-line impact and potential. The same is true of DW and the BI applications that make these initiatives possible.

Organizations are being compelled to capture , understand, and harness their data to support decision making in order to improve business operations . Legislation and regulation (e.g., the Sarbanes-Oxley Act of 2002) now require business leaders to docu- ment their business processes and to sign off on the legitimacy of the information they rely on and report to stakeholders. Moreover, business cycle times are now extremely compressed; faster, more informed, and better decision making is therefore a competitive imperative. Managers need the right infonnation at the right time and in the right place. This is the mantra for modern approaches to BI.

Organizations have to work smart. Paying careful attention to the management of BI initiatives is a necessary aspect of doing business. It is no surprise, then, that organizations are increasingly championing BL You will hear about more BI successes and the funda- mentals of those successes in Chapters 3 through 9. Examples of many applications of BI are provided in Table 1.3. Application Case 1.1 illustrates one such application of BI that has helped many airlines, as well as the companies offering such services to the airlines.

A Multimedia Exercise in Business Intelligence

Teradata University Network (TUN) includes some videos along the lines of the televi- sion show CSI to illustrate concepts of analytics in different industries. These are called "BSI Videos (Business Scenario Investigations)." Not only these are entertaining, but they also provide the class with some questions for discussion. For starters, please go to teradatauniversitynetwork.com/teach-and-learn/library-item/?Libraryltemld=889. Watch the video that appears on YouTube. Essentially, you have to assume the role of a customer service center professional. An incoming flight is running late, and several pas- sengers are likely to miss their connecting flights. There are seats on one outgoing flight that can accommodate two of the four passengers. Which two passengers should be given

Chapter 1 • An Overview of Business Intelligence, Analytics, and Decision Support 17

TABLE 1.3 Business Value of Bl Analytical Applications

Analytic Application Business Question Business Value Customer segmentation What market segments do my customers fall

into, and what are their characteristics? Personalize customer relationships for higher

satisfaction and retention. Propensity to buy Which customers are most likely to respond

to my promotion? Target customers based on their need to

increase their loyalty to your product line. Also, increase campaign profitability by focusing

on the most likely to buy.

Customer profitability What is the lifetime profitability of my customer?

Make individual business interaction decisions based on the overall profitability of customers.

Fraud detection How can I tell which transactions are likely to be fraudulent?

Quickly determine fraud and take immediate action to minimize cost.

Customer attrition Which customer is at risk of leaving? Prevent loss of high-value customers and let go of lower-value customers.

Channel optimization What is the best channel to reach my cus- tomer in each segment?

Interact with customers based on their preference and your need to manage cost.

Source: A. Ziama and J. Kasher, Data Mining Primer for the Data Warehousing Professional. Teradata, Dayton, OH, 2004.

Application Case 1.1 Sabre Helps Its Clients Through Dashboards and Analytics Sabre is one of the world leaders in the travel indus- try, providing both business-to-consumer services as well as business-to-business services. It serves travel- ers, travel agents, corporations, and travel suppliers through its four main companies: Travelocity, Sabre Travel Network, Sabre Airline Solutions, and Sabre Hospitality Solutions. The current volatile global eco- nomic environment poses significant competitive chal- lenges to the airline industry. To stay ahead of the competition, Sabre Airline Solutions recognized that airline executives needed enhanced tools for manag- ing their business decisions by eliminating the tradi- tional, manual, time-consuming process of collect- ing and aggregating financial and other information needed for actionable initiatives. This enables real-time decision support at airlines throughout the world that maximize their (and, in tum, Sabre's) return on infor- mation by driving insights, actionable intelligence, and value for customers from the growing data.

Sabre developed an Enterprise Travel Data Warehouse (ETDW) using Teradata to hold its mas- sive reservations data. ETDW is updated in near-real time with batches that run every 15 minutes, gathering

data from all of Sabre's businesses. Sabre uses its ETDW to create Sabre Executive Dashboards that pro- vide near-real-time executive insights using a Cognos 8 BI platform with Oracle Data Integrator and Oracle Goldengate technology infrastructure. The Executive Dashboards offer their client airlines' top-level man- agers and decision makers a timely, automated, user- friendly solution, aggregating critical petformance metrics in a succinct way and providing at a glance a 360-degree view of the overall health of the airline. At one airline, Sabre's Executive Dashboards provide senior management with a daily and intra-day snap- shot of key petformance indicators in a single appli- cation, replacing the once-a-week, &-hour process of generating the same report from various data sources. The use of dashboards is not limited to the external customers; Sabre also uses them for their assessment of internal operational petformance.

The dashboards help Sabre's customers to have a clear understanding of the data through the visual displays that incorporate interactive drill-down capa- bilities. It replaces flat presentations and allows for more focused review of the data with less effort and

(Continued)

18 Part I • Decision Making and Analytics: An Oveiview

Application Case 1.1 (Continued) time. This facilitates team dialog by making the data/ metrics pertaining to sales performance, including ticketing, seats sold and flown, operational perfor- mance such as data on flight movement and track- ing, customer reservations, inventory, and revenue across an airline's multiple distribution channels, avail- able to many stakeholders. The dashboard systems provide scalable infrastructure, graphical user interface (GUI) support, data integration, and data aggregation that empower airline executives to be more proactive in taking actions that lead to positive impacts on the overall health of their airline.

With its EIDW, Sabre could also develop other Web-based analytical and reporting solutions that lev- erage data to gain customer insights through analysis of customer profiles and their sales interactions to cal- culate customer value. This enables better customer segmentation and insights for value-added services.

QUESTIONS FOR DISCUSSION

1. What is traditional reporting? How is it used in organizations?

2. How can analytics be used to transform tradi- tional reporting?

3. How can interactive reporting assist organiza- tions in decision making?

What We Can Learn from This Application Case This Application Case shows that organizations that earlier used reporting only for tracking their internal business activities and meeting compliance requirements set out by the government are now moving toward generating actionable intelligence from their transactional business data. Reporting has become broader as organizations are now try- ing to analyze archived transactional data to under- stand underlying hidden trends and patterns that would enable them to make better decisions by gaining insights into problematic areas and resolv- ing them to pursue current and future market opportunities. Reporting has advanced to interac- tive online reports that enable users to pull and quickly build custom reports as required and even present the reports aided by visualization tools that have the ability to connect to the database, providing the capabilities of digging deep into summarized data .

Source: Teradata .com, "Sabre Airline Solutions," teradata.com/t/ case-studies/Sabre-Airline-Solutions-EB6281 (accessed February 2013).

priority? You are given information about customers' profiles and relationship with the air- line. Your decisions might change as you learn more about those customers' profiles.

Watch the video, pause it as appropriate, and answer the questions on which pas- sengers should be given priority. Then resume the video to get more information. After the video is complete, you can see the slides related to this video and how the analysis was prepared on a slide set at teradatauniversitynetwork.corn/templates/Download. aspx?Contentltemld=891. Please note that access to this content requires initial registration.

This multimedia excursion provides an example of how additional information made available through an enterprise data warehouse can assist in decision making.

The DSS-BI Connection

By now , you should be able to see some of the similarities and differences between DSS and BI. First, their architectures are very similar because BI evolved from DSS . However, BI implies the use of a data warehouse, whereas DSS may or may not have such a feature . BI is, therefore, more appropriate for large organizations (because data warehouses are expensive to build and maintain) , but DSS can be appropriate to any type of organization.

Second, most DSS are constructed to directly support specific decision making. BI systems, in general, are geared to provide accurate and timely information, and they sup- port decision support indirectly. This situation is changing, however, as more and more decision support tools are being added to BI software packages.

Chapter 1 • An Overview of Business Intelligence , Analytics, and Decision Support 19

Third, BI has an executive and strategy orientation, especially in its BPM and dash- board components. DSS , in contrast, is oriented toward analysts.

Fourth, most BI systems are constructed with commercially available tools and com- ponents that are fitted to the needs of organizations. In building DSS, the interest may be in constructing solutions to very unstructured problems. In such situations , more pro- gramming (e.g., using tools such as Excel) may be needed to customize the solutions.

Fifth, DSS methodologies and even some tools were developed mostly in the aca- demic world. BI methodologies and tools we re deve loped mostly by software companies. (See Zaman, 2005, for information on how BI has evolved.)

Sixth, many of the tools that BI uses are also considered DSS tools . For example, data mining and predictive analysis are core tools in both areas.

Although some people equate DSS with BI, these systems are not, at present, the same. It is interesting to note that some people believe that DSS is a part of BI-one of its analytical tools. Others think that BI is a special case of DSS that deals mostly with report- ing, communication, and collaboration (a form of data-oriented DSS) . Another explana- tion (Watson, 2005) is that BI is a result of a continuous revolution and, as such, DSS is one of Bi's original elements. In this book, we separate DSS from BI. However, we point to the DSS-BI connection frequently. Further, as noted in the next section onward, in many circles BI has been subsumed by the new term analytics or data science.

SECTION 1. 7 REVIEW QUESTIONS

1. Define BI. 2. List and describe the major components of BI. 3. What are the major similarities and differences of DSS and BI?

1.8 BUSINESS ANALYTICS OVERVIEW The word "analytics" has replaced the previous individual components of computerized decision support technologies that have been available under various labels in the past. Indeed , many practitioners and academics now use the word analytics in place of BI. Although many authors and consultants have defined it slightly differently, one can view analytics as the process of developing actionable decisions or recommendation for actions based upon insights generated from historical data . The Institute for Operations Research and Management Science (INFORMS) has created a major initiative to organize and pro- mote analytics. According to INFORMS, analytics represents the combination of computer technology, management science techniques, and statistics to solve real problems. Of course, many other organizations have proposed their own interpretations and motivation for analytics. For example, SAS Institute Inc. proposed eight levels of analytics that begin with standardized reports from a computer system. These reports essentially provide a sense of what is happening with an organization. Additional technologies have enabled us to create more customized reports that can be generated on an ad hoc basis. The next extension of reporting takes us to online analytical processing (OLAP)-type queries that allow a user to dig deeper and determine the specific source of concern or opportuni- ties . Technologies available today can also automatically issue alerts for a decision maker when performance issues warrant such alerts. At a consumer leve l we see such alerts for weather or other issues. But similar alerts can also be generated in specific settings when sales fall above or below a certain level within a certain time period or when the inventory for a specific product is running low. All of these applications are made possible through analysis and queries on data being collected by an organization. The next level of analysis might entail statistical analysis to better understand patterns. These can then be taken a step further to develop forecasts or models for predicting how customers might respond to

20 Part I • Decision Making and Analytics: An Overview

Predictive Statistical Analysis and

Data Mining

Reporting Visualization

Periodic, ad hoc Reporting Trend Analysis

Prescriptive Management Science Models and Solution

Reporting Visualization

Periodic , ad hoc Reporting

Trend Analysis

FIGURE 1.5 Three Types of Analytics.

Predictive Statistical Analysis

and Data Mining

Prescriptive Management Science

Models and Solution

a specific marketing campaign or ongoing service/product offerings. When an organization has a good view of what is happening and what is likely to happen, it can also employ other techniques to make the best decisions under the circumstances. These eight levels of analytics are described in more detail in a white paper by SAS (sas.com/news/sascom/ analytics_levels.pdf).

This idea of looking at all the data to understand what is happening, what will happen, and how to make the best of it has also been encapsulated by INFORMS in proposing three levels of analytics. These three levels are identified (inforrns.org/ Community/Analytics) as descriptive, predictive, and prescriptive. Figure 1.5 presents two graphical views of these three levels of analytics. One view suggests that these three are somewhat independent steps (of a ladder) and one type of analytics application leads to another. The interconnected circles view suggests that there is actually some overlap across these three types of analytics. In either case, the interconnected nature of different types of analytics applications is evident. We next introduce these three levels of analytics.

Descriptive Analytics

Descriptive or reporting analytics refers to knowing what is happening in the organization and understanding some underlying trends and causes of such occur- rences. This involves, first of all, consolidation of data sources and availability of

Chapter 1 • An Overview of Business Intelligence , Analytics, a nd Decision Support 21

all relevant data in a form that enables appropriate reporting and analysis . Usually development of this data infrastructure is part of data warehouse s , which we study in Chapter 3. From this data infrastructure we can develop appropriate reports , queries , alerts , and trends using various reporting tools and techniques. We study these in Chapter 4.

A significant technology that has become a key player in this area is visua lizatio n . Using the latest visualization tools in the marketplace , we can now develop powerful insights into the operatio ns of our organization. Applicatio n Cases 1.2 a nd 1.3 highlight some such applications in the healthcare domain. Color renderings of such applications are available on the companion Web site and also on Tableau 's Web site. Chapter 4 covers visualization in more detail.

Application Case 1.2 Eliminating Inefficiencies at Seattle Children's Hospital Seattle Children's was the seventh highest ranked children's hospital in 2011 , according to U.S. News & World Report. For any organization that is com- mitted to saving lives, identifying and removing the inefficiencies from systems and processes so that more resources become available to cater to patient care become very important. At Seattle Children's , management is continuously looking for new ways to improve the quality, safety, and processes from the time a patient is admitted to the time they are discharged. To this end, they spend a lot of time in analyzing the data associated w ith the patient visits.

To quickly turn patient and hospital data into insights, Seattle Children's implemented Tableau Software's business intelligence application. It pro- vides a browser based on easy-to-use analytics to the stakeholders; this makes it intuitive for individuals to create visualizations and to understand what the data has to offer. The data analysts, business managers, and financial an alysts as well as clinicians, doctors, and researchers are a ll using descriptive analytics to solve different problems in a much faster way. They are developing visual systems o n their own, resulting in dashboards and scorecards that help in defining the standards, the current performance achieved measured against the standards, and how these systems will grow into the future. Through the use of monthly and daily dashboards, day-to-day decision making at Seattle Children's has improved significantly .

Seattle Children's measures patient wait-times and analyzes them with the help of visualizations to discover the root causes and contributing factors

for patient wa1tmg. They found that early delays cascaded during the day. They focused o n on-time appointments of patient services as on e of the solu- tions to improving patient overall waiting time and increasing the availability of beds. Seattle Children's saved about $3 million from the supply chain, and with the help of tools like Tableau, they are find- ing new ways to increase savings while treating as many patients as possible by making the existing processes more efficient.

QUESTIONS FOR DISCUSSION

1. Who are the users of the tool? 2. What is a dashboard? 3 . How does visualization help in decision making? 4 . What are the significant results achieved by the

use of Tableau?

What We Can Learn from This Application Case

This Application Case shows that reporting analyt- ics involving visualizations such as dashboards can offer major insights into existing data and show how a variety of users in different domains and depart- ments can contribute toward process and qual- ity improvements in an organization. Furthermore, exploring the data visually can help in identifying the root causes of problems and provide a basis for working toward possible solutions.

Source: Tableausoftware.com, "Eliminating Waste at Seattle Childre n's," tableausoftware.com/eliminating-waste-at-seattle- childrens (accessed Fe bruary 2013).

22 Pan I • Decision Making and Analytics: An Overview

Application Case 1.3 Analysis at the Speed of Thought Kaleida Health, the largest healthcare provider in western New York, has more than 10,000 employ- ees, five hospitals, a number of clinics and nursing homes, and a visiting-nurse association that deals with millions of patient records. Kaleida's traditional reporting tools were inadequate to ha ndle the grow- ing data, and they were faced with the challenge of finding a business intelligence tool that could handle large data sets effortlessly, quickly, and with a much deeper analytic capability.

At Kaleida, many of the calculations are now done in Tableau, primarily pulling the data from Oracle databases into Excel and importing the data into Tableau. For many of the monthly ana- lytic reports, data is directly extracted into Tableau from the data warehouse ; many of the data queries are saved and rerun, resulting in time savings when dealing with millions of records-each having more than 40 fields per record. Besides speed, Kaleida also uses Tableau to me rge different tables for gen- erating extracts.

Using Tableau, Kaleida can analyze emergency room data to determin e the number of patients who visit more than 10 times a year. The data often reveal that people frequently use emergency room and ambulance services inappropriately for stomach- aches, headaches, and fevers. Kaleida can manage resource utilizations-the use a nd cost of supplies- which will ultimately lead to efficie ncy and standard- ization of supplies management across the system.

Kaleida now has its own business intelligence department and uses Tableau to compare itself to

Predictive Analytics

other hospitals across the country. Comparisons are made o n various aspects, such as length of patient stay, hospital practices, market share, and partner- ships with doctors .

QUESTIONS FOR DISCUSSION

1. What are the desired functionalities of a report- ing tool?

2. What advantages were derived by using a report- ing tool in the case?

What We Can Learn from This Application Case

Correct selection of a reporting tool is extremely important, especially if an organization wants to derive value from reporting. The generated reports and visualizations should be easily discernible; they should help people in different sectors make sense out of the reports, identify the problematic areas, a nd contribute toward improving them. Many future organizations will require reporting analytic tools that are fast and capable of h andling huge amounts of data efficiently to generate desired reports with- out the need for third-party consultants and service providers. A truly useful reporting tool can exempt organizations from unnecessary expenditure.

Source: Tableausoftware .com, "Kaleida Health Finds Efficiencies, Stays Compe titive," tableausoftware.com/learn/stories/user- experience-speed-thought-kaleida-health (accessed February 2013).

Predictive analytics aims to determine what is likely to happen in the future . This an aly- sis is based o n statistical techniques as well as other more recently developed techniques that fa ll under the general category of data mining. The goal of these techniques is to be able to predict if the customer is likely to switch to a competitor ("churn") , what the cus- tomer is likely to buy next and how much, what promotion a customer would respond to, or w hether this customer is a creditworthy risk. A number of techniques are u sed in developing predictive analytical applications, including various classification algorithms. For example, as described in Chapters 5 an d 6, we can use classification techniques su ch as decision tree models and neural networks to predict how well a motion picture will do at the box office. We can also use clustering algorithms for segmenting customers into different clusters to be able to target specific promotions to them. Fina lly, we can

Chapter 1 • An Overview of Business Intelligence , Analytics, a nd Decision Support 23

use association mining techniques to estimate relationships between different purchasing behaviors. That is, if a customer buys one product, what e lse is the customer likely to pur- chase? Such analysis can assist a retailer in recommending or promoting related products. For example , any product search on Amazon.com results in the retailer also suggesting other similar products that may interest a customer. We will study these techniques and their applications in Ch apters 6 through 9. Application Cases 1.4 and 1.5 highlight some similar applications. Application Case 1.4 introduces a movie you may have heard of: Moneyball. It is perhaps one of the best examples of applications of predictive analysis in sports.

Application Case 1.4 Moneyba/1: Analytics in Sports and Movies Moneyball, a biographical, sports, drama film, was released in 2011 and directed by Bennett Miller. The film was based on Michael Lewis's book, Moneyball. The movie gave a detailed account of the Oakland Athletics baseball team during the 2002 season and the Oakland general manager's efforts to assemble a competitive team.

The Oakland Athletics suffered a big loss to the New York Yankees in 2001 postseason. As a result, Oakland lost many of its star players to free agency and ended up with a weak team with unfavorable financial prospects. The general manager's efforts to reassemble a competitive team were denied because Oakland had limited payroll. The scouts for the Oakland Athletics followed the o ld baseball custom of making subjective decisions when selecting the team members. The general manager then met a young, computer whiz with an economics degree from Yale. The general manager decided to appoint him as the n ew assistant general manager.

The assistant general manager had a deep pas- sion for baseball and had the expertise to crunch the numbers for the game. His love for the game made him develop a radical way of understanding baseball statistics. He was a disciple of Bill James, a marginal figure w ho offered rationalized techniques to analyze baseball. James looked at baseball statis- tics in a different way, crunching the numbers purely on facts and eliminating subjectivity. James pio- neered the nontraditional a nalysis method called the Sabermetric approach, which derived from SABR- Society for American Baseball Research.

The assistant general manager followed the Sabermetric approach by building a prediction

model to help the Oakland Athletics select play- ers based on their "on-base percentage" (OBP), a statistic that measured how often a batter reached base for any reason other than fielding error, field- er's choice, dropped/ uncaught third strike, fielder's obstruction, or catcher's interference. Rather than relying on the scout's experience and intuition, the assistant general manager selected players based almost exclusively o n OBP.

Spoiler Alert: The new team beat all odds, won 20 consecutive games, and set an American League record.

QUESTIONS FOR DISCUSSION

1. How is predictive analytics applied in Moneyball? 2. What is the difference between objective and

subjective approaches in decision making?

What We Can Learn from This Application Case Analytics finds its use in a variety of industries. It helps organizations rethink their traditional prob- lem-solving abilities, which are most often subjec- tive , relying o n the same old processes to find a solutio n. Analytics takes the radical approach of using historical data to find fact-based solutions that w ill remain appropriate for making even future decisions.

Source.- Wikipedia , "On-Base Percentage," en.wikipedia.org/ wiki/On_base_percentage (accessed Ja nuary 2013); Wikipedia , "Saberme tricsm," wikipedia.org/wiki/Sabennetrics (accessed January 2013) .

24 Pan I • Decision Making and Analytics: An Overview

Application Case 1.5 Analyzing Athletic Injuries Any athletic activity is prone to injuries. If the inju- ries are not handled properly, then the team suf- fers. Using analytics to understand injuries can help in deriving valuable insights that would enable the coaches and team doctors to manage the team composition , understand player profiles, and ulti- mately a id in better decision making concerning which players might be available to play at any given time.

In an exploratory study, Oklahoma State University analyzed American football-related sport injuries by using reporting and predictive analytics. The project followed the CRISP-DM methodol- ogy to understand the problem of making recom- me ndations on managing injuries, understanding the various data elements collected about injuries, cleaning the data, developing visualizations to draw various inferences, building predictive models to analyze the injury healing time period, and drawing sequence rules to predict the relationship among the injuries and the various body part parts afflicted with injuries.

The injury data set consisted of more than 560 football injury records, which were categorized into injury-specific variables-body part/ site/ later- ality, action taken, severity, injury type, injury statt and healing dates-and player/sport-specific varia- bles-player ID, position played, activity, onset, and game location. Healing time was calculated for each record, which was classified into different sets of time periods: 0-1 month, 1-2 months, 2-4 months, 4- 6 months, and 6- 24 months .

Various visualizations were built to draw inferences from injury data set information depict- ing the healing time period associated w ith players' positions, severity of injuries and the h ealing time period, treatment offered and the associated healing time period, major injuries afflicting body parts, and so forth.

Prescriptive Analytics

Neural network models were built to pre- dict each of the healing categories using IBM SPSS Modeler. Some of the predictor variables were cur- rent status of injury, severity, body part, body site, type of injury, activity, event location, action taken, and position played. The success of classifying the healing categoty was quite good: Accuracy was 79.6 percent. Based on the analysis, many business rec- ommendations were suggested, including e mploy- ing more specialists' input from injury onset instead of letting the training room staff screen the injured players; training players at defensive positions to avoid being injured; and holding practice to thor- oughly safety-check mechanisms.

QUESTIONS FOR DISCUSSION

1. What types of a nalytics are applied in the injury analysis?

2. How do visualizations aid in understanding the data and delivering in sights into the data?

3. What is a classification problem? 4 . What can be derived by performing sequence

analysis?

What We Can Learn from This Application Case For any analytics project, it is always important to understand the busin ess domain and the cur- rent state of the business problem through exten- sive analysis of the only resource-historical data . Visualizations often provide a great tool for gaining the initial insights into data, which can be further refined based on expett opinions to identify the rela- tive importance of the data e lements related to the problem. Visualizations also aid in generating ideas for obscure business problems, which can be pur- sued in building predictive models that could help organizations in decision making.

The third category of analytics is termed prescriptive analytics . The goal of prescriptive analytics is to recognize what is going on as well as the likely forecast and make decisions to achieve the best performance possible. This group of techniques h as historically been studied under the umbrella of operations research or management sciences and h as gen- erally been aimed at optimizing the performance of a system. The goal h e re is to provide

Chapter 1 • An Overview of Business Intelligence , Analytics, a nd Decision Support 25

a decision or a recommendation for a specific action. These recommendations can be in the forms of a specific yes/ no decision for a problem, a specific amount (say, price for a specific item or airfare to ch arge) , or a complete set of production plans. The decisions may be presented to a decision maker in a report or may directly be used in an automated decision rules system (e.g., in airline pricing systems). Thus, these types of analytics can also be termed decision or normative analytics. Application Case 1.6 gives an example of such prescriptive analytic applications . We will learn about some of these techniques and several additional applicatio ns in Chapters 10 through 12.

Application Case 1.6 Industrial and Commercial Bank of China (ICBC) Employs Models to Reconfigure Its Branch Network The Industrial and Commercial Bank of China (ICBC) has more than 16,000 branches and serves over 230 million individual customers and 3.6 mil- lion corporate clients. Its daily financial transactions total about $180 million. It is also the largest pub- licly traded bank in the world in terms of market capitalization, deposit volume , and profitability. To stay competitive and increase profitability, ICBC was faced with the challenge to quickly adapt to the fast- paced economic growth, urbanization, and increase in personal wealth of the Chinese. Changes had to be implemented in over 300 cities with high variability in customer behavior and financial status. Obviously, the nature of the challenges in such a huge economy meant that a large-scale optimization solution had to be developed to locate branches in the right places, with right services, to serve the right customers.

With their existing method, ICBC used to decide w here to open new branches through a scoring model in which different variables with varying weight were used as inputs. Some of the variables were customer flow, number of residential households, and number of competitors in the intended geographic region. This method was deficient in determining the customer dis- tribution of a geographic area. The existing method was also unable to optimize the distribution of bank branches in the branch network. With support from IBM, a branch reconfiguration (BR) tool was devel- oped. Inputs for the BR system are in three parts:

a. Geographic data with 83 different categories b . Demographic and economic data with 22 dif-

ferent categories c. Branch transactions and performance data that

consisted of more than 60 million transaction records each day

These three inputs helped generate accurate cus- tomer distribution for each area and, h ence, helped the bank optimize its branch network. The BR system consisted of a market potential calculation model, a branch network optimization model, and a branch site evaluation model. In the market potential model, the customer volume and value is measured based on input data and expert knowledge. For instance , expe1t knowledge would help determine if per- sonal income should be weighted more than gross domestic product (GDP). The geographic areas are also demarcated into cells, and the preference of one cell over the other is determined. In the branch net- work optimization model, mixed integer program- ming is used to locate branches in candidate cells so that they cover the largest market potential areas. In the branch site evaluation model, the value for establishing bank branches at specific locations is determined.

Since 2006, the development of the BR has been improved through an iterative process. ICBC's branch reconfiguration tool has increased deposits by $21.2 billio n since its inception. This increase in deposit is because the bank can now reach more customers with the right services by use of its optimization tool. In a specific example , when BR was implemented in Suzhou in 2010, deposits increased to $13 .67 billion from an initial leve l of $7 .56 billion in 2007. Hence, the BR tool assisted in an increase of deposits to the tune of $6.11 b illion between 2007 and 2010. This project was selected as a finalist in the Edelman Competition 2011 , which is run by INFORMS to promote actual app lications of management science/ operations research models.

(Continued)

26 Pan I • Decision Making and Analytics: An Overview

Application Case 1.6 (Continued} QUESTIONS FOR DISCUSSION

1. How can analytical techniques help organiza- tions to re tain competitive advantage?

2. How can descriptive and predictive an alytics help in pursuing prescriptive analytics?

3. What kinds of p rescriptive analytic techniques are employed in the case study?

4. Are the prescriptive models once built good forever?

What We Can Learn from This Application Case Many organizations in the world are now embrac- ing analytical techniqu es to stay compe titive and achieve growth. Many o rga nizations provide

con sulting solutions to the businesses in employ- ing prescriptive analytical solutions. It is equ ally important to have proactive decis ion m akers in the organizations who are aware of the ch anging eco- nomic environment as well as the advan cem ents in the field of an alytics to e n sure that appropriate models are e mployed . This case shows an example o f geographic m a rke t segmentatio n and customer beh avioral segmentation tec hniques to isolate the profitability of customers a nd e mploy optimizatio n techniques to locate the branches that deliver hig h profitability in each geographic segment.

Source: X. Wang e t al. , "Branch Reconfiguration Practice Through Operations Research in Industrial a nd Commercial Bank of China," Interfaces, January/ February 2012, Vol. 42, No . 1, pp. 33-44; DOI: 10.1287/ inte.1110.0614.

Analytics Applied to Different Domains

Applicatio ns of analytics in various industry sectors h ave spawn ed m any related areas or at least buzzwords. It is almost fashio nable to attach the word analytics to any specific industry or type of data . Besides the general category of text analytics- aimed at getting value out of text (to be studied in Ch apter 6)- or Web analytics- analyzing Web data streams (Chapte r 7)- many industry- o r p roblem-sp ecific a nalytics p rofessio ns/ streams have come up. Examples of such areas are marketing analytics, retail analytics, fraud ana- lytics, transportation a nalytics, health analytics, sports analytics, tale nt a nalytics, behav- ioral analytics, and so forth. For example, Application Case 1. 1 could also be terme d as a case study in airline analytics . Application Cases 1.2 and 1.3 would belo ng to health analytics; Applicatio n Cases 1.4 and 1.5 to sports an alytics; Application Case 1.6 to bank analytics; and Application Case 1.7 to reta il analytics. The End-of-Chapter Application Case could be termed insurance analytics . Literally, any systematic analysis of data in a sp ecific secto r is b eing labeled as "(fill-in-blanks)" Analytics. Although this may result in overselling the concepts of analytics, the benefit is that more people in specific in dustries are aware of the power a nd p otential of an alytics. It also provides a focus to professionals developing a nd applying the concepts of analytics in a vertical secto r. Altho ugh m any of the techniques to develop analytics applications may be commo n , there are unique issues w ithin each vertical segment that influe nce how the data may b e collected, processed, analyzed , and the applications implemented. Thus , the differentiatio n o f analytics based o n a vertical focus is good for the overall growth of the d iscipline.

Analytics or Data Science?

Even as the concept of analytics is getting popular among industry and academic circles , another term h as already been introduced and is becoming popular. The n ew term is data science. Thus the practitio n ers of data scien ce are data scie ntists. Mr. D . J. Patil of Linkedin is sometimes credited w ith creating the term data science. There have been some attempts to describe the differences between data analysts and data scientists (e.g., see this study at emc.com/collateral/about/news/emc-data-science-study-wp.pdf). One view is that

Chapter 1 • An Overview of Business Intelligence, Analytics, a nd Decision Support 27

data analyst is just another term for professionals w ho were doing business intelligence in the form of data compilation, cleaning, reporting, and perhaps some visualization. Their skill sets included Excel , some SQL knowledge, a nd reporting. A reader of Section 1.8 would recognize that as descriptive or reporting analytics. In contrast, a data scientist is responsible for predictive analysis, statistical analysis, and more advanced analytical tools and algorithms. They may have a deeper knowledge of algorithms and may recognize them under various labels-data mining, knowledge discovery, machine learning, and so forth. Some of these professionals may also need deeper programming knowledge to be able to write code for data cleaning and analysis in current Web-oriented languages su ch as Java and Python. Again, our readers should recognize these as falling under the predictive a nd prescriptive a nalytics umbrella. Our view is that the distinction between analytics and data science is more of a degree of technical knowledge and skill sets than the functions. It may also be more of a distinction across d iscip lines. Computer science, statistics, and applied mathematics programs appear to prefer the data science label, reserving the analytics label for more business-oriented professionals. As another example of this, applied physics professionals have proposed using network science as the term for describing analytics that relate to a group of people-social networks, supply chain networks, and so forth. See barabasilab.neu.edu/networksciencebook/downlPDF. html for a n evolving textbook on this topic .

Aside from a clear difference in the skill sets of professionals w ho only h ave to do descriptive/ reporting analytics versus those who e ngage in all three types of analytics, the distinction is fuzzy between the two labels, at best. We observe that graduates of our analytics programs tend to be responsible for tasks more in line with data science profes- sionals (as defined by some circles) than just reporting analytics. This book is clearly aimed at introducing the capabilities and functionality of all analytics (which includes data sci- ence), not just reporting analytics. From now o n , we w ill use these terms interch angeably .

SECTION 1.8 REVIEW QUESTIONS

1. Define analytics. 2. What is descriptive analytics? What various tools are employed in descriptive analytics? 3. How is descriptive analytics different from traditional reporting? 4. What is a data warehouse? How can data warehousing technology help in ena-

bling analytics? 5. What is predictive analytics? How can organizations employ predictive analytics? 6. What is prescriptive analytics? What kinds of problems can be solved by prescrip-

tive analytics? 7. Define modeling from the analytics perspective. 8. Is it a good idea to follow a hierarchy of descriptive and predictive analytics before

applying prescriptive analytics? 9. How can analytics aid in objective decision making?

1.9 BRIEF INTRODUCTION TO BIG DATA ANALYTICS What Is Big Data?

Our brains work extreme ly quickly and are efficient and versatile in processing large amounts of all kinds of data: images , text, sounds, smells, and video. We process all different forms of data relatively easily. Computers, on the other hand, are still finding it hard to keep up with the pace at which data is generated-let alone analyze it quickly. We have the problem of Big Data. So w hat is Big Data? Simply put, it is data that cannot

28 Pan I • Decision Making and Analytics: An Overview

be stored in a single storage unit. Big Data typically refe rs to data that is arnv mg in many different forms, be they structured, unstructured, or in a stream. Major sources of su c h data are clickstreams from Web sites, postings o n social media sites such as Facebook, o r data from traffic , sen sors, o r weather. A Web search e ngine like Google n eed s to search a nd index billions of Web pages in o rde r to give you relevant search results in a fraction of a second. Although this is n o t done in real time , generating an index of a ll the Web pages o n the Inte rnet is not an easy task. Luckily for Google , it was able to solve this proble m . Among o the r tools, it h as e mployed Big Data a nalytical techniques.

There are two aspects to managing data o n this scale: storing a n d p rocessing . If we could purchase an extreme ly expensive storage solutio n to store all the d ata at o n e place o n one unit, making this unit fault tolerant would involve major expense . An ingenious solutio n was proposed that involved storing this data in chunks o n different machines connected by a network, putting a copy or two of this chunk in diffe rent locations on the netwo rk, both logically and physically . It was originally used at Google (then called Google File System) and later developed and re leased as an Apache project as the Hadoop Distributed File System (HDFS).

However, sto ring this data is only h alf the problem. Data is worthless if it does n ot provide business value, a nd for it to provide bu siness value, it has to be a nalyzed. How are such vast amounts of data a n alyzed? Passing all computation to o ne powerful compute r does n o t work; this scale would create a huge overhead on such a power- ful computer. Another ingenious solutio n was proposed: Push computation to the data, instead of pushing data to a computing no de. This was a new paradigm, and it gave rise to a w ho le new way of processing data. This is w h at we know today as the MapRedu ce programming paradigm, w hich made processing Big Data a reality. MapReduce was origi- n ally develo p ed at Google, and a subseque n t versio n was released by th e Apache project called Hadoop MapReduce.

Today, w hen we talk about storing, processing, o r analyzing Big Data, HDFS and MapReduce are involved at some level. Other relevant stan dards and software solutions have been proposed. Although the majo r toolkit is available as open source, several companies have been launched to provide training or specialized analytical hardware or software services in this space. Some examples are HortonWorks, Clo udera , an d Teradata Aster.

Over the p ast few years , w h at was called Big Data changed m ore and more as Big Data applicatio n s appeared. The n eed to process data coming in at a rapid rate added velocity to the equatio n . One example of fast data processing is algorithmic trading . It is the use of electronic platforms based o n algorithms for trading sh ares o n the finan cial m arke t, which operates in the order of microseconds. The n eed to process different kinds of data added variety to the equation. Another example of the wide varie ty of data is sentiment a nalysis, w hic h uses various forms of data from social media p latforms a nd c ustomer responses to gauge sentime nts. Tod ay Big Data is associated w ith al most a ny kind of large data that h as the characteristics of volume, velocity, and variety. Applicatio n Case 1.7 illustrates one example of Big Data analytics . We w ill study Big Data characteristics in more detail in Chapters 3 and 13 .

SECTION 1.9 REVIEW QUESTIONS

1. What is Big Data a nalytics? 2 . What are the sources o f Big Data? 3. What are the characteristics of Big Data? 4. What processing technique is applied to p rocess Bi ta?

Chapter 1 • An Overview of Business Inte lligence, Analytics, and Decision Support 29

Application Case 1.7 Gilt Groupe's Flash Sales Streamlined by Big Data Analytics Gilt Groupe is an online destination offering flash sales for major brands by selling their clothing and accessories. It offers its members exclusive discounts on high-end clothing and other apparel. After regis- tering with Gilt, customers are sent e-mails containing a variety of offers. Customers are given a 36-48 hour window to make purchases using these offers. There are about 30 different sales each day. While a typical department store turns over its inventory two or three times a year, Gilt does it eight to 10 times a year. Thus, they have to manage their inventory extremely well or they could incur extremely high inventory costs. In order to do this, analytics software developed at Gilt keeps track of eve1y customer click-ranging from what brands the customers click on, what colors they choose, what styles they pick, and what they end up buying. Then Gilt tries to predict what these customers are more likely to buy and stocks inve n- tory according to these predictions. Customers are sent customized alerts to sale offers depending on the suggestions by the analytics software.

That, however, is not the whole process. The software also monitors what offers the custome rs choose from the recommended offers to make more accurate predictions and to increase the effectiveness of its personalized recommendations. Some custom- ers do not check e-mail that often. Gilt's analytics

1.10 PLAN OF THE BOOK

software keeps track of responses to offers and sends the same offer 3 days later to those customers who h aven't responded. Gilt also keeps track of what customers are saying in general about Gilt's prod- ucts by analyzing Twitter feeds to analyze sentiment. Gilt's recomme ndation software is based on Teradata Aster's technology solution that includes Big Data analytics technologies .

QUESTIONS FOR DISCUSSION

1. What makes this case study an example of Big Data analytics?

2. What types of decisions does Gilt Groupe have to make?

What We Can Learn From this Application Case There is continuous growth in the amount of struc- tured and unstructured data, and many organiza- tions are now tapping these data to make actionable decisions. Big Data analytics is now enabled by the advancements in technologies that aid in storage and processing of vast amounts of rapidly growing data.

Source: Asterdata.com, "Gilt Groupe Speaks o n Digital Ma rketing Optimizatio n ," asterdata.com/gilt_groupe_video.php (accesse d Febrnary 2013).

The previous sections have given you an understanding of the n eed for using informa- tion technology in decision making; an IT-oriented v iew of various types of decisions; and the evolution of decision support systems into business intelligence, and now into analytics. In the last two sections we have seen an overview of various types of analyt- ics and their applications. Now we are ready for a more detailed managerial excursion into these topics, along with some potentially deep hands-on experience in some of the technical topics. The 14 chapters of this book are organized into five parts, as shown in Figure 1.6.

Part I: Business Analytics: An Overview

In Chapter 1, we provided an introduction, definitions , and an overview of decision sup- port systems, business intelligence, and analytics, including Big Data analytics. Chapter 2 covers the basic phases of the decision-making process and introduces decision support systems in more detail.

30 Pan I • Decision Making and Analytics: An Ove rview

Part II Descriptive Analytics

Chapter 3 Data Warehousing

Part I Decision Making and Analytics: An Overview

r - - - - - - - - - - - - - - - - - - - - - - - - - - ,

' Chapter 1 1 An Overview of Business

Intelligence, Analytics, and Decision Support

Chapter 2 Foundations and Technologies for

Decision Making

Part Ill Part IV Predictive Analytics Prescriptive Analytics

Chapter 9 Chapter 5 Model-Based Decision Making: Data Mining Optimization and Multi-Criteria

Chapter 6 Techniques for Predictive

Modeling

Chapter 7 Text Analytics, Text Mining, and

Sentiment Analysis

Systems

Chapter 10 Modeling and Analysis :

Heuristic Sear ch Methods and Simulation

Chapter 11 Automated Decision Systems and

Expert Systems

Part V Big Data and Future Directions

for Business Analytics ,--------------------------

Chapter 13 Big D ata and Analytics

Chapter 4 Business Reporting , Visual

Analytics, and Business Performance Management

Chapter 8 Web Analytics, Web Mining, and

Social Analytics

Chapter 12 Knowledge Management and

Collabor ative Systems

Chapter 14 Business Analytics: Emer ging Trends and Future Impacts

-------------r- ------------ ------------r------------, ._ ------------1- ------------- -------------T- ----------- I

FIGURE 1.6 Plan of the Book.

Part II: Descriptive Analytics

Part VI Online Supplements

Software Demos

Data Files for Exercises

PowerPoint Slides

Part II begins with an introduction to data warehousing issues , applications, and technolo- gies in Chapter 3. Data re present the fundamental backbone of any decision support and analytics application. Chapter 4 describes business reporting, visualization technologies, and applications. It also includes a brief overview of business performance management techniques and applications, a topic that has been a key p art of traditional BI.

Part Ill: Predictive Analytics

Part III comprises a large part of the book. It begins w ith an introduction to predictive analytics applications in Chapter 5. It includes many of the common application tech- niques: classification, clustering, association mining, and so forth. Chapter 6 includes a technical description of selected d ata minin g techniques, especially ne ural network m od- els. Chapter 7 focuses on text mining applications. Similarly, Chapter 8 focuses on Web analytics, including social media analytics, sentiment analysis, and other related to pics.

Chapter 1 • An Overview of Business Intelligence, Analytics, a nd Decision Support 31

Part IV: Prescriptive Analytics

Part IV introduces decision analytic techniques, which are also called prescriptive analyt- ics. Specifically, Chapter 9 covers selected models that may be implemented in spread- sheet environme nts. It also covers a popular multi-objective decision technique-analytic hierarchy processes.

Chapter 10 then introduces other model-based decision-making techniques, espe- cially heuristic models and simulation. Chapter 11 introduces automated decision systems including expert systems. This part concludes with a brief discussion of knowledge management and group support systems in Chapter 12.

Part V: Big Data and Future Directions for Business Analytics

Part V begins with a more detailed coverage of Big Data and analytics in Chapter 13. Chapter 14 attempts to integrate all the material covered in this book and

concludes with a discussion of emerging trends , such as how the ubiquity of wire- less and GPS devices and other sensors is resulting in the creation of massive new databases and unique applications. A new breed of data mining and BI companies is emerging to analyze these new databases and create a much better and deeper under- standing of customers' behaviors and movements. The chapter also covers cloud-based analytics, recommendation systems, and a brief discussion of security/ privacy dimen- sions of analytics. It concludes the book by also presenting a discussion of the analytics ecosystem. An understanding of the ecosystem and the various players in the a nalytics industry highlights the various career opportunities for students and practitioners of analytics .

1.11 RESOURCES, LINKS, AND THE TERADATA UNIVERSITY NETWORK CONNECTION

The use of this chapter and most other chapters in this book can be e nhanced by the tools described in the following sections.

Resources and Links

We recommend the following major resources and links:

• The Data Warehousing Institute (tdwi.org) • Informatio n Management (information-management.com) • DSS Resources (dssresources.com) • Microsoft Enterprise Consortium (enterprise.waltoncollege.uark.edu/mec.asp)

Vendors, Products, and Demos

Most vendors provide software demos of their products and applications. Information about products, architecture, and software is available at dssresources.com.

Periodicals

We recommend the following periodicals:

• Decision Support Systems • CIO Insight (cioinsight.com) • Technology Evaluation (technologyevaluation.com) • Baseline Magazine (baselinemag.com)

32 Pan I • Decision Making and Analytics: An Overview

The Teradata University Network Connection

This book is tightly connected with the free resources provided by Teradata University Network (TUN; see teradatauniversitynetwork.com) . The TUN portal is divided into two major parts: one for students and one for facu lty. This b ook is connected to the TUN portal v ia a sp ecial sectio n at the end of each ch a pter. That section includes appropriate links for the specific chapter, pointing to relevant resources. In addition, w e provide hands-on exercises, u s ing software and other material (e.g., cases) avail- able at TUN.

The Book's Web Site

This book's Web site, pearsonhighered.com/turban, contains supplemental textual m aterial o rganized as Web chapters that correspond to the printed b ook's chapters. The topics of these ch a pters are listed in the online chapter table of conte n ts . Other conte nt is also available on an indepe ndent Web site (dssbibook.com) .2

Chapter Highlights • The business e nvironment is becoming complex

and is rapidly changing, ma king decisio n making m o re difficult.

• Businesses must respond and adapt to the chang- ing e nvironment rapid ly by making faster and better decisions.

• The time frame for making decisio n s is shrinking, w h ereas the global nature of decision making is expa nding, necessitating the d evelopment and u se of computerized DSS.

• Computerized support for ma nagers is ofte n essential for the survival of a n organization .

• An early decision support framework divides decision situatio ns into nine categories, depending on the degree of structuredness and managerial activities. Each category is supported differently.

• Structured repetitive decisio ns are supported by standard quantitative analysis methods, such as MS, MIS, an d rule-based automated decision suppo rt.

• DSS use data , models, and sometimes knowledge management to find solutions for semistructured a nd some unstructured proble ms.

• BI metho ds utilize a central repository called a data warehouse that e na bles efficient data mining, OLAP , BPM, and data visu alizatio n.

• BI architecture includes a data wareh ouse, busi- ness analytics tools u sed by end u sers, and a u ser interface (su ch as a dashboard) .

• Many organizatio ns employ descriptive analytics to re place the ir traditional fla t reporting with inter- active reporting that provides insights , trends, and patterns in the transactional data.

• Predictive analytics enable organizations to estab- lish predictive rules that drive the business o ut- comes through historical d ata analysis of the existing beh avior of the cu stomers .

• Prescriptive analytics he lp in building models that involve forecasting and optimizatio n techniques based o n the principles of operatio ns research and management science to help organizations to make better decision s.

• Big Data analytics focuses o n un structured, la rge data sets that may also include vastly different types of data for analysis.

• Analytics as a fie ld is also known by industry- specific application names su ch as sports analytics. It is also known by oth e r rela ted names su ch as data scie nce or network scie nce.

2 As this book went to p ress, we verified that a ll the cited Web sites were active and valid . However, URLs a re d ynamic. Web sites to which we re fe r in the text sometimes ch a nge o r are discontinued b ecau se compan ies c ha nge na mes, are bought o r sold , merge, o r fail. Sometimes Web s ites a re down for maintenance, repair, o r re design. Many organizations h ave dropped the initial "www" d esignatio n for their sites, but some still use it. If you have a proble m connecting to a Web s ite that we mentio n , please be patient a nd simply ru n a Web search to try to ide ntify the possible new site. Most times , you can quickly find the n ew s ite through on e o f th e popular sea rch e ngines. We apologize in a dvance for this in convenie nce.

Chapter 1 • An Ove rview of Business Intelligence, Analytics, a nd Decision Support 33

Key Terms business intelligence

(BI) dashboard data mining

decision (or normative) analytics

decision support system (DSS)

Questions for Discussion 1. Give examples for the conte nt of each cell in Figure 1. 2. 2. Survey the literature fro m the p ast 6 mo nths to find o ne

application each for DSS, BI, and analytics. Summarize the applications o n o ne page and submit it with the exact sources.

3. Observe an o rganization with which you a re familiar. List three decisio ns it makes in each of the following categories :

Exercises Teradata University Network (TUN) and Other Hands-On Exercises 1. Go to teradatauniversitynetwork.com. Using the reg-

istration your instructor provides, log on and learn the conte nt of the site . Yo u will receive ass ig nme nts re lated to this site. Prepare a list of 20 items in the site tha t you think could be beneficial to you.

2 . Ente r the TUN site and select "cases, projects and assign- me nts." Then select the case study: "Harrah's High Payoff from Customer Informatio n ." Answer the following ques- tions about this case: a. What informatio n does the data mining generate? b. How is this information helpful to management in

d ecision making? (Be specific.) c. List the types of data that are mine d . d. Is this a DSS o r BI a pplication? Why?

3. Go to teradatauniversitynetwork.com and find the paper titled "Data Warehousing Supports Corporate Strategy at First American Corporation" (by Watson, Wixom, and Goodhue). Read the paper and answer the following questions: a. What were the drivers for the DW / BI project in the

company? b. What strategic advantages w e re realized? c. What o perational and tactical advantages were achieved? d. What were the critical success fa c tors (CSF) for the

imple me ntation? 4. Go to analytics-magazine.org/issues/digital-editions

and find the January/Februa1y 2012 edition titled "Special Issue: The Future of Healthcare. " Read the article "Predictive

descriptive (or re porting) analytics

predictive analytics prescriptive analytics

semistructured problem

structured problem unstructured proble m

strategic planning, manageme nt control (tactical planning), and o p e ratio nal planning a nd contro l.

4. Distinguish BI from DSS. 5. Compa re a nd contrast pre dictive a nalytics with prescrip-

tive and descriptive analytics. Use examples.

Analytics-Saving Lives and Lowering Medical Bills." Answer the following questions: a . What is the proble m that is be ing addressed by apply-

ing predictive analytics? b . What is the FICO Medication Adhere nce Score? c. How is a prediction mo del traine d to predict the FICO

Medicatio n Adherence Score? Did the prediction model classify FICO Medication Adhe re nce Score?

d. Zoom in o n Figure 4 and explain w h at kind of tech- nique is applied o n the generated results.

e. List some of the actio nable decisions that were based on the results of the p redictions.

5 . Go to analytics-magazine.org/issues/digital-editions and find the January/ February 2013 editio n titled "Work Social. " Read the a rticle "Big Data, Analytics and Elections" a nd answer the followin g questions: a . What kinds of Big Data were a nalyzed in the article?

Comment o n some of the sources of Big Data. b. Explain the term integrated system. Wha t othe r tech-

nical term suits integrated system? c. What kinds of da ta a nalysis techniques are e mployed

in the project? Comment on some initiatives that resulted fro m data a nalysis.

d . What a re the diffe re nt prediction problems a nswered by the models?

e. List some o f the actionable decisions taken that were based on the predicatio n results.

f. Identify two applications of Big Data a nalytics that are not listed in the article.

34 Pan I • Decision Making and Analytics: An Ove rview

6. Search the Inte rnet for mate rial regarding the work of man- agers and the role analytics play. What kind o f references to consulting firms, academic de paitme nts, and programs do you find? What major areas are re p resented? Select five sites that cover one area and repo rt your findings.

7. Explore the public areas of dssresources.corn. Pre p are a list of its majo r ava ila ble resources. Yo u might want to refe r to this site as you wo rk through the book.

End-of-Chapter Application Case

8. Go to rnicrostrategy.corn. Find information on the five styles o f BI. Prepare a su mmary ta ble fo r each style.

9. Go to oracle.corn and click the Hyp erion link u nder Applications. Determine what the company's major prod- u cts are. Re late these to the su p port tech nologies cited in this ch apter.

Nationwide Insurance Used Bl to Enhance Customer Service Nationwide Mutual Insurance Company, headqu aitered in Columbus, O hio, is one of the largest insurance and financial services companies, w ith $23 billion in revenues and more than $160 billion in statutory assets. It offers a comprehensive range of products through its family of 100-plus companies with insurance products for auto, motorcycle, boat, life, homeown- ers, and farms. It also offers financial products and services including annuities, mongages, mutual funds, pensions, and investment management.

Nationw ide strives to achieve greater efficie ncy in all operatio ns by managing its expenses along with its ability to grow its revenue . It recognizes the use of its su·ategic asset of info rmation comb ined with analytics to o utp ace competitors in strategic and o p eratio nal decision making even in complex and unpredictable environments.

Historically, Natio nw ide's bu siness units worked inde- pendently and w ith a lot of autonomy. This led to d uplication of effo rts, w idely dissimilar data processing environments, and exu·eme data redundancy, resulting in higher exp enses. The situatio n got comp licated w hen Natio nwide pursu ed any merg- ers o r acquisitions.

Nationw ide, using ente rprise data warehouse technology from Teradata, set out to create , from tl1e ground u p, a single, authoritative environn1ent for clean , consistent, and complete data that can be effectively used for best-p ractice analytics to make su-ategic and tactical business decisions in the areas of customer growth, retention , product profitability, cost contain- ment, and productivity improvements. Natio nw ide u-ansfonned its siloed business units, which were supponed by stove-piped data environments, into integrated units by using cutting-edge analytics that work w ith clear, consolidated data from all of its business units. The Teradata data warehouse at Nationwide has grown from 400 gigabytes to more than 100 terabytes and supports 85 p ercent of Nationw ide's business w ith more than 2,500 users.

Integrated Customer Knowledge Nationw ide 's Cu stome r Knowledge Sto re (CKS) m1t1at1ve develo p ed a cu sto me r-centric database that integrate d cu s- to mer, product, a nd externa lly acquire d d ata from mo re

than 48 sources into a single customer data ma rt to deliver a h o listic view of cu sto mers. This d ata mart was coupled w ith Teradata's customer relatio nship man agem ent application to create and ma nage effective customer ma rketing cam paigns th at u se behavioral analysis of cu stome r interactions to drive cu stomer ma nageme nt actio ns (CMAs) fo r target segments . Nationw ide ad ded mo re sophisticated cu stom er analytics that looke d at custome r p o rtfolios and the effectiveness of va riou s marketing camp aigns . This data analysis he lped Nation wide to initiate proactive customer commu nicatio ns around custo mer life time events like marriage, b irth of child , o r h o me p urchase and had significa n t imp act on improv- ing cu stomer satisfactio n. Also, by integra ting cu stomer contact history, produ ct own ership, a nd payment informa- tio n , Nationwide's be havioral an alytics teams fu rthe r created p rio ritized models that could identify w hich specific cus- tome r interaction was important for a customer at any given t ime. This resulted in o ne percentage point improve ment in cu stome r retention rates and significant improvement in custome r e n thusiasm scores. Nationwide also achieved 3 percent annual growth in incremental sales by using CKS . There are other uses of the customer database. In one of th e initiatives, by integrating customer te lepho ne d ata from multip le syste ms into CKS, the relation sh ip man agers at Natio nw ide tty to be proactives in contacting customers in adva nce of a possible weather catastroph e, su ch as a hur- rican e or flood, to provide the p rimary p o licyh older infor- matio n and explain th e claims p rocesses. These an d other analytic insights now d rive Natio nw ide to p rovide extrem ely personal customer service.

Financial Operations A sinillar p e rformance p ayoff from integrated information was also noted in finan cial operations. Nationwide's decentralized management style resulted in a fragme nted financial report- ing environment that included more than 14 general ledgers, 20 chans of accounts, 17 separate data re positories, 12 different repo1ting tools, and hundreds of thousands of spreadsheets. There was no common central view of the business, which resulte d in labor-intensive slow and inaccu rate reporting.

Chapter 1 • An Ove rview of Business Intelligence, Analytics, a nd Decision Support 35

About 75 percent of the effort was spent on acquiring, clean- ing, and consolidating and validating the data, and very little time was spent on meaningful analysis of the data.

The Financial Pe rforma nce Management initiative impleme nted a new o pe rating approac h that worked on a single d ata a nd technology architecture with a common set of systems standa rdizing the process of re porting. It e nabled Nationwide to opera te analytical centers of excelle nce with world-class planning, capital manageme nt, risk assessme nt, and other decision support capabilities that delive red timely , accurate, and efficie nt accounting, re porting, and analytical services.

The d ata from more tha n 200 operational systems was sent to the e nte rprise -w ide da ta warehouse and the n distrib- ute d to various applications and analytics. This resulte d in a 50 percent improvement in the monthly closing process with closing inte rvals reduced from 14 days to 7 days.

Postmerger Data Integration Na tionwide's Goal State Rate Manageme nt m1t1at1ve e n a - ble d the company to me rge Allied Insurance's automobile p o licy system into its existi ng syste m. Bo th atio nwide and Allied source systems were custom-built applica tions that did not share any common values or process data in the same m a nne r. Nationwide's IT d e partme nt de cide d to bring all the data from source systems into a centralized data ware house, organized in an integrated fash io n tha t resulted in standard dimensional reporting a nd helped Nationwide in performing what-if analyses. The data analysis tea m could identify previously unknown p otential diffe re nces in the data e nvironme nt where premiums ra tes were cal- culated diffe re ntly b etween Nationwide and Allied s ides. Correcting all of these benefited Nationwide's policyhold- ers because they were safeguarded from experiencing w ide pre mium rate swings.

Enhanced Reporting Nationwide's legacy re porting syste m , which catered to the nee ds of prope rty and casualty business units, took weeks to compile and d eliver the needed re ports to the agents. Nationw ide determined that it nee ded b e tte r access to sales and policy information to reach its sales targe ts. It chose a

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QUESTIONS FOR DISCUSSION

1. Why did Nationwide need an e nterprise -w ide d ata warehouse?

2. How did integrated data drive the bu siness value? 3 . What forms of analytics are e mployed at Natio nwide? 4. With integrated data available in an e nte rprise d ata

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What We Can Learn from This Application Case The proper u se of integra ted informa tion in organiza- tio ns can help achieve bette r business outcomes. Many organizatio ns now rely o n data w a re housing techno logies to p e rfo rm the o nline analytical processes o n the d ata to derive valuable insights. Th e insights are u sed to d evelop predictive mo d els that further e nable the growth of the organizations by m ore precisely assessing customer needs. Increasingly, o rganizations a re moving toward d e rivi ng value from analytical applications in real time with the he lp of integrated data fro m real-time data warehousing techno logies.

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CHAPTER

Foundations and Technologies for Decision Making

LEARNING OBJECTIVES

• Understand the conceptual foundations of decision making

• Understand Simon's four phases of decision making: intelligence, design, choice, and implementation

• Understand the essential definition of DSS

• Understand impo1tant DSS classifications • Learn how DSS suppo1t for decision

making can be provided in practice • Understand DSS components and how

they integrate

0 ur major focus in this book is the support of decision making through computer-based information systems. The purpose of this chapter is to describe the conceptual foundations of decision making and how decision support is provided. This chapter includes the fo llowing sections:

2.1 Opening Vignette: Decision Modeling at HP Using Spreadsheets 38 2.2 Decision Making: Introduction and Definitions 40 2.3 Phases of the Decision-Making Process 42 2.4 Decision Making: The Intelligence Phase 44 2.5 Decision Making: The Design Phase 47 2.6 Decision Making: The Choice Phase 55 2.7 Decision Making: The Implementation Phase 55 2.8 How Decisions Are Supported 56 2.9 Decision Support Systems: Capabilities 59

2.10 DSS Classifications 61 2.11 Components of Decision Support Systems 64

38 Pan I • Decision Making and Analytics: An Overview

2.1 OPENING VIGNETTE: Decision Modeling at HP Using Spreadsheets

HP is a major manufacturer of computers, printers, and many industrial products. Its vast product line leads to many decision problems. Olavson and Fry (2008) have worked on many spreadsheet models for assisting decision makers at HP and have identified several lessons from both their successes and their failures when it comes to constructing and applying spreadsheet-based tools. They define a tool as "a reusable, analytical solution designed to be handed off to nontechnical end users to assist them in solving a repeated business problem. "

When trying to solve a problem, HP developers consider the three phases in devel- oping a model. The first phase is problem framing, where they consider the following questions in order to develop the best solution for the problem:

• Will analytics solve the problem? • Can an existing solution be leveraged? • Is a tool needed?

The first question is important because the problem may not be of an analytic nature, and therefore, a spreadsheet tool may not be of much help in the long run without fixing the nonanalytical part of the problem first. For example , many inventory-related issues arise because of the inherent differences between the goals of marketing and supply chain groups. Marketing likes to have the maximum variety in the product line, whereas supply chain management focuses on reducing the inventory costs. This difference is par- tially outside the scope of any model. Coming up with nonmodeling solutions is impor- tant as well . If the problem arises due to "misalignment" of incentives or unclear lines of authority or plans, no model can help. Thus, it is important to identify the root issue.

The second question is important because sometimes an existing tool may solve a problem that then saves time and money. Sometimes modifying an existing tool may solve the problem, again saving some time and money, but sometimes a custom tool is neces- sary to solve the problem. This is clearly worthwhile to explore.

The third question is important because sometimes a new computer-based system is not required to solve the problem. The developers have found that they often use analytically derived decision guidelines instead of a tool. This solution requires less time for development and training, has lower maintenance requirements, and also provides simpler and more intuitive results. That is, after they have explored the problem deeper, the developers may determine that it is better to present decision rules that can be eas- ily implemented as guidelines for decision making rather than asking the managers to run some type of a computer model. This results in easier training, better understanding of the rules being proposed, and increased acceptance. It also typically leads to lower development costs and reduced time for deployment.

If a model has to be built, the developers move on to the second phase-the actual design and development of the tools. Adhering to five guidelines tends to increase the probability that the new tool will be successful. The first guideline is to develop a proto- type as quickly as possible. This allows the developers to test the designs , demonstrate various features and ideas for the new tools, get early feedback from the end users to see what works for them and what needs to be changed, and test adoption. Developing a prototype also prevents the developers from overbuilding the tool and yet allows them to construct more scalable and standardized software applications later. Additionally, by developing a prototype, developers can stop the process once the tool is "good enough, " rather than building a standardized solution that would take longer to build and be more expen sive .

Chapter 2 • Foundations and Technologies for Decis ion Making 39

The second guideline is to "build insight, n o t black boxes. " The HP spreadsheet model developers believe that this is impo rta nt, because ofte n just e n tering some data and receiving a calculated output is not e noug h. The u sers n eed to be able to think of alternative scen arios, and the tool d oes n o t support this if it is a "black box" that provides o nly o ne recomme ndatio n. They a rgue that a tool is best only if it provides informa tio n to help make and support decisio ns rather tha n just give the answers. They also believe that a n interactive tool h elps the u sers to unde rstand the problem better, therefore leading to mo re informed decisions.

The third guideline is to "remove unneeded complexity before handoff. " This is important, because as a tool becomes mo re complex it requires mo re training and exper- tise, more data, and more recalibrations. The risk of bugs and misuse also increases. Sometimes it is best to study the problem, begin modeling and analysis, and then start shaping the program into a simple -to -use tool for the end user.

The fourth guideline is to "partner w ith end users in discovery and design. " By work- ing with the end u sers the developers get a better feel of the problem a nd a better idea of what the e nd users want. It also increases the e nd users' ability to use analytic tools. The end users also gain a better understanding of the problem and how it is solved u sin g the new tool. Additionally, including the e nd users in the development process e nha nces the decision makers' a na lytical knowledge a nd capabilities. By working together, their knowledge and skills complement each other in the final solutio n .

The fifth guide line is to "develop a n Operations Research (OR) champio n ." By involv- ing e nd users in the development p rocess, the developers create champions for the new tools w h o then go back to their departments o r companies and encourage their cowork- ers to accept and use them. The champions are the n the experts on the tools in their areas and can then h e lp those being introduced to the new tools. Having champio ns increases the possibility that the tools w ill be adopted into the businesses su ccessfully .

The final stage is the h andoff, when the final tools that provide comple te solutions are given to the businesses. When pla nning the h andoff, it is important to answer the fol- lowing questions:

• Who w ill use the tool? • Who owns the decisions that the tool w ill support? • Who else must be involved? • Who is resp o n sible for maintenance and enhancement of the tool? • When will the too l be used? • How w ill the u se of the tool fit in w ith othe r processes? • Does it ch ange the processes? • Does it generate input into those processes? • How w ill the tool impact business performance? • Are the existing m e trics sufficient to reward this aspect of performance? • How should the metrics and incentives be changed to maximize impact to the busi-

n ess from the tool and process?

By keeping these lesson s in mind, developers and proponents of computerized deci- sion support in gene ral and spreadsheet-based models in particular are likely to enjoy greater success.

QUESTIONS FOR THE OPENING VIGNETTE

1. What are some of the key questions to be asked in supporting decision making through DSS?

2. What guidelines can be learned from this vig n e tte about developing DSS? 3. What lessons sh ould be kept in mind for successful m odel implementation?

40 Pan I • Decision Making and Analytics: An Overview

WHAT WE CAN LEARN FROM THIS VIGNETTE

This vignette relates to providing decision support in a large organization:

• Before building a mo del, decision makers should develop a good understanding of the problem that needs to be addressed.

• A model may not be necessary to address the problem. • Before developing a n ew tool, decision makers should explo re reuse of existing tools. • The goal of model building is to gain better insight into the problem, not just to

generate more numbers. • Implementatio n plan s should be developed alo ng w ith the model.

Source: Based on T. O lavson and C. Fry, "Spre adshee t Decision-Support Too ls : Lesso ns Learne d at Hew le tt- Packard," ln teifaces, Vol. 38, No. 4, Ju ly/ August 2008, pp. 300-310.

2.2 DECISION MAKING: INTRODUCTION AND DEFINITIONS We are about to examine how decision making is practiced a nd some of the underlying theories and models of decision m aking. You w ill a lso learn a b out the various traits of decision makers, including what ch aracterizes a good decision maker. Knowing this can help you to understand the types of decision support tools that m a nagers can use to m ake mo re effective decisions . In the following sections, we discuss variou s aspects of decision making.

Characteristics of Decision Making

In additio n to the ch aracteristics presented in the opening vignette, decision making may involve the fo llowing:

• Groupthink (i.e., group members accept the solution w ithout thinking for them- selves) can lead to bad decisions.

• Decision ma kers are interested in evaluating what-if scenarios. • Experimentation w ith a real system (e.g., develop a schedule, try it, and see h ow

well it works) m ay result in failure. • Experime ntation w ith a real system is possible only for one set of conditions at a

time and can be disastrous. • Ch anges in th e decision-making e nvironment may occur continuo u sly, leading to

invalidating assumptions about a situatio n (e.g., deliveries around holiday times may increase, requiring a different view of the problem).

• Changes in the decision-making environme nt may affect decision quality by impos- ing time pressure on the decision maker.

• Collecting informatio n and an alyzing a problem takes time a nd can be expen s ive. It is difficult to determine w he n to stop a nd make a decisio n .

• There m ay not be sufficient information to make an intelligent decision. • Too much information may b e available (i.e. , info rmatio n overload).

To determine how real decision makers make decisions, we must first understand the process and the important issues involved in decision making. The n we can understand appropriate metho dologies for assisting decision makers and the contributio ns informatio n systems can make . Only the n can we develop DSS to he lp decision makers.

This ch apter is organized based o n the three key words that form the term DSS: decision, support, and systems. A de cisio n maker sh ould n ot simply apply IT tools b lind ly. Rather, the decision ma ker gets support throug h a ra tio nal approach that

Chapter 2 • Foundations and Technologies for Decis ion Making 41

simplifies reality and provides a relatively quick a nd inexpen sive means of con sidering variou s alte rna tive courses of action to arrive at the best (or at least a ve1y good) solu- tio n to the problem.

A Working Definition of Decision Making

Decision making is a process of choosin g among two or more alternative courses of action for the purpose of attaining one or more goals. According to Simo n (1977), ma na- gerial decision making is syno nymo u s w ith the e ntire m anagem e nt process. Con side r the important managerial function of planning. Planning invo lves a series of decisions: What should be done? When? Where? Why? How? By w hom? Managers set goals, or plan; he nce, planning implies d ecision making. Other m anagerial functions, such as organizin g and controlling, also involve decisio n making.

Decision-Making Disciplines

Decision m aking is directly influe nced by several major disciplines, some of which are behavioral a nd some of which a re scien tific in nature. We must be aware of how their philosophies can affect our ability to ma ke decisions and provide support. Behavioral disciplines include anthropo logy, law, philosophy, political scien ce, psychology, social psycho logy, and socio logy. Scie ntific disciplines include computer scie n ce, decision analysis, econo mics, engineering, the h ard scien ces (e.g. , biology, ch emistry, p hysics), man ageme nt science/ operatio ns research, mathematics, an d statistics.

An important characte ristic of management support systems (MSS) is their e mpha- sis on the effectiveness, or "goodness," of the decision produced rathe r than o n th e computational efficie ncy of obtaining it; this is usually a major concern of a transaction processing system. Most Web-based DSS are focused on improving decision effectiveness. Efficiency may be a by-product.

Decision Style and Decision Makers

In the following sections, we examine the n otion of decision style and specific aspects about decision makers.

DECISION STYLE Decision style is the manner by which decision makers think and react to problems. This includes the way they perceive a problem, their cognitive respon ses, and how values and beliefs vary from individual to individual and from situation to situatio n . As a result, people make decisions in different ways. Although the re is a general process of decision making, it is far from linear. People do n o t follow the same steps of the process in the same sequence, nor do they use all the steps. Furthermore, the emphasis, time allo tme nt, and priorities given to each step vary significantly, not o nly fro m o ne person to a noth e r, but a lso from o ne situatio n to the next. The manner in which managers make decisions (and the way they inte ract w ith other people) describes their decision style . Because decision styles depend o n the factors described earlier, there are many decision styles. Personality temperame n t tests are often u sed to determine decision styles. Because the re are many su ch tests , it is important to try to equate them in deter- mining decision style. However, the vario u s te sts mea sure somewh at different aspects of pe rsonality, so they cannot be equated.

Researche rs h ave identified a numbe r of decision-making styles. These include heu- ristic and a nalytic styles. One can also distinguish between autocratic versus democratic styles. Another style is con sultative (with individuals or groups). Of course, there are many combinations and variatio ns of styles. For example, a person can b e an alytic and autocratic, o r consultative (with ind ividuals) a nd he uristic.

42 Pan I • Decision Making and Analytics: An Overview

For a computerized system to successfully support a man ager, it should fit the decision situation as well as the decision style. Therefore, the system sh ould be flexible and adaptable to different use rs. The a bility to ask w hat-if and goal-seeking questions provides flexibility in this direction. A Web-based interface using graphics is a desirable feature in supporting certain decision styles. If a DSS is to support varying styles, skills, and knowledge, it should not attempt to enforce a specific process. Rather, it sho uld help decision makers u se and develop their own styles, skills, an d knowledge .

Different decision styles re quire different types of support. A ma jo r factor that deter- mines the type of support required is w hether the decision maker is a n individual or a group. Individual decision makers need access to data and to experts who can provide advice, w hereas groups additio nally need collaboration tools. Web-based DSS can p ro- vide suppo rt to both.

A lo t of information is available on the Web about cognitive styles and d ecision styles (e.g., see Birkman Inte rnational, Inc. , birkman.com; Keirsey Temperament Sorter a nd Keirsey Temperament Theory-II, keirsey.com) . Many personality/ temperament tests a re available to h e lp managers identify their own styles a nd those of their employees. Identifying an individual's style can help establish the m ost effective communication p atterns a nd ideal tasks for which the p erson is suited.

DECISION MAKERS Decisio ns are often made by individuals, especially at lower manage- rial levels and in small organizatio n s. There may be conflicting objectives even for a sole decision maker. For example, w hen making an investment decision, an individual investor may consider the rate of return on the investment, liquidity, and safety as objectives. Finally, decisions may be fully automated (but only after a human decision maker decides to do so!).

This discussio n of decision m aking focuses in large p art o n an individual decision maker. Most major decisions in medium-sized and large organizatio ns a re mad e by groups. Obviously, there are often conflicting objectives in a group decision-making setting. Grou ps can be of variable size and may include people from different departments or from differ- e nt o rganizatio ns. Collaborating individuals may have different cognitive styles, personality types, and decision styles. Some clash, w hereas others a re mutually enhancing. Con sensus can be a difficult political problem. Therefore, the process of decision making by a group can be ve1y complicated. Computerized support can greatly enha nce group decision making. Computer support can be provided at a broad level, en abling members of w ho le departments, divisions , or even entire o rganization s to collaborate online . Su ch supp ort has evolved over the past few years into ente rprise info rmation systems (EIS) and inclu des group support syste ms (GSS), enterprise resource management (ERM)/enterprise resource p lanning (ERP), supply ch ain m anagem ent (SCM), knowledge management systems (KMS), and custome r relatio nship management (CRM) systems.

SECTION 2.2 REVIEW QUESTIONS

1. What are the vario us asp ects of decisio n making7 2. Ide ntify similarities and diffe re n ces between individual and group decisio n making . 3. Define decision style a nd describe w hy it is importa nt to consider in the d ecision-

making process. 4. What are the benefits of m athematical models?

2.3 PHASES OF THE DECISION-MAKING PROCESS It is advisable to follow a systematic decision-making process. Simon 0977) said that this involves three major phases: intelligence, design , and choice. He later added a fourth phase, imple me ntatio n . Monitoring can be considered a fifth phase- a form of feedback. However,

Success~

Simplification

Assumptions

Chapter 2 • Foundations and Technologies for Decision Making 43

Organization objectives Search and scanning procedures Data collection Problem identification Problem ownership Problem classification Problem statement

Formulate a model

-1- - - - - -;

Validation of the model Set criteria for choice

Verification, testing of ro osed solution

Search for alternatives • • • • • -: Predict and measure outcomes

Solution to the model Sensitivity analysis Selection of the best (good)

alternative(s) Plan for implementation

... ••••••I

Implementation of solution - - - - - - - - - - - - -?- ----------------------------------_:

• Failure

FIGURE 2.1 The Decision-Making/Modeling Process.

we view mo nito ring as the intelligence phase applied to the implementation phase. Simo n 's mod el is the most concise and yet comple te characte rizatio n of ratio nal decisio n making. A conceptual picture of the decision-making process is shown in Figure 2. 1.

There is a continuo us flow of activity fro m intelligence to design to ch oice (see the bold lines in Figure 2.1), but at any phase, there may be a return to a previous phase (feedback) . Modeling is a n essential part o f this process. The seemingly ch aotic n ature of following a haphazard path from problem discovery to solutio n via decision making can be explained by these feedback loops .

The decision-making process starts w ith the intelligence phase; in this phase, th e decision maker examines reality and identifies and defines the problem. Problem ownership is established as well. In the design phase, a model that represents the system is constrncted. This is done by making assumptions that simplify reality and w riting down the relationships among all the variables. The model is then validated, and crite ria are determined in a princi- ple of choice for evaluatio n of the alternative courses of action that a re identified. Often, the process of model development identifies alternative solutions and vice versa.

The choice phase includes selection of a proposed solution to the model (not necessarily to the problem it represents). This solutio n is tested to determine its viability. When the proposed solution seems reasonable, we are ready for the last phase: imple - me ntatio n of the decision (n o t n ecessarily of a system). Su ccessful implementation results in solv ing the real problem. Failure leads to a return to an earlier phase of the process. In fact, we can return to an earlie r phase during a ny o f the latter three phases. The decision- making situatio ns described in the opening vignette follow Simon 's four-ph ase model, as do almost a ll other d ecisio n-making situatio n s. Web impacts on the four phases, and vice versa, a re sh own in Table 2.1 .

44 Pan I • Decision Making and Analytics: An Overview

TABLE 2.1 Simon's Four Phases of Decision Making and the Web

Phase

Intell igence

Design

Choice

Implementation

Web Impacts

Access to information to identify problems and opportunities from internal and external data sources

Access to analytics methods to identify opportunities

Collaboration through group support systems (GSS) and knowledge management systems (KMS)

Access to data, models, and solution methods

Use of online analytical processing (OLAP), data mining, and data warehouses

Collaboration through GSS and KMS Similar solutions available from KMS Access to methods to evaluate the

impacts of proposed solutions

Web-based collaboration tools (e.g., GSS) and KMS, which can assist in implementing decisions

Tools, which monitor the performance of e-commerce and other sites, including intranets, extranets, and the Internet

Impacts on the Web

Identification of opportunities for e-commerce, Web infrastructure, hardware and softwa re tools, etc.

Intelligent agents, which reduce the burden of information overload

Smart search engines

Brainstorming methods (e.g ., GSS) to collaborate in Web infrastructure design

Models and solutions of Web infrast ructure issues

Decision support system (DSS) tools, which examine and establish criteria from models to determine Web, intranet, and extranet infrastructure

DSS tool s, wh ich determine how to route messages

Decisions implemented on browser and server design and access, which ultimately determined how to set up the various compone nts that have evolved into the Internet

Note that there are many other decision-making processes. Notable among them is the Kepner-Tregoe method (Kepner and Tregoe, 1998), which has been adopted by many firms because its tools are readily available from Kepner-Tregoe, Inc. (kepner-tregoe. com). We have found that these alternative models , including the Kepner-Tregoe method, readily map into Simon 's four-phase model.

We next turn to a detailed discussion of the four phases identified by Simon.

SECTION 2.3 REVIEW QUESTIONS

1. List a nd briefly describe Simon's four phases of decision making. 2. What are the impacts of the Web on the phases of decision making?

2.4 DECISION MAKING: THE INTELLIGENCE PHASE Intelligence in decision making involves scanning the environment, either intermitte ntly or continuously. It includes several activities aimed at identifying problem situations or opportunities. It may also include monitoring the results of the implementation phase of a decision-making process.

Chapter 2 • Foundations and Technologies for Decis ion Making 45

Problem (or Opportunity) Identification The intelligence phase begins with the ide ntification of organizational goals and objectives re lated to an issu e of concern (e.g ., inventory manageme nt, job selectio n, lack of or incorrect Web presence) and determination of w hether they are being met. Problems occur because of dissatisfactio n with the status quo. Dissatisfactio n is the result of a difference between w hat people desire (or expect) and what is occurring . In this first phase, a decision make r atte mpts to determine whether a problem exists, identify its symptoms, determine its magnitude, and explicitly define it. Often, what is described as a problem (e.g. , excessive costs) may be only a symptom (i.e., measure) of a proble m (e.g., improper invento1y levels). Because real- world problems are usually complicated by many interrelated factors, it is sometimes difficult to distinguish between the sympto ms and the real problem. New opportunities and prob- lems certainly may be uncovered w hile investigating the causes of symptoms. For example, Application Case 2.1 describes a classic story of recognizing the correct p roblem.

The existe n ce of a proble m can be determined by mo nitoring and analyzing the organization's productivity level. The measure ment of prod uctivity and the construction of a model are based o n real data. The collection of data and the estima tion of future data are amo ng the most difficult step s in the an alysis. The following are some issues that may arise during data collection and estimatio n and thus plagu e decisio n m akers:

• Data a re not available . As a result, the model is m ade with, and relies on, p otentially inaccurate estimates .

• Obtaining data may be expensive . • Data m ay not be accurate or precise enough. • Data estimatio n is o fte n subjective . • Data may be insecure. • Impo rtant data that influence the results may be qualitative (soft) . • There may be too ma ny data (i.e. , info rmatio n overload).

Application Case 2.1 Making Elevators Go Faster! This sto1y h as been reported in numerous places and has a lmost become a classic example to explain the n eed for problem identification. Ackoff (as cited in La rso n , 1987) described the problem of managing complaints about slow elevators in a tall hotel tower. After trying m any solutions for reducing the com- plaint: staggering elevators to go to different floors, adding o perators, and so on, the management deter- mined that the real p roble m was n ot about the actual waiting time but rathe r the p erceived waiting time. So the solution was to install full-length mirrors on e levator doors o n each floor. As Hesse and Woolsey (1975) put it, "the women would look at the mselves in the mirrors and make adjustme nts, w hile the me n would look at the women , and before they knew it, the e levator was the re ." By reducing the perceived waiting time, the problem went away. Baker and

Cameron 0996) give several other examples of dis- tractions, including lightin g, displays, and so on, that organizations use to reduce p erceived waiting time . If the real problem is identified as perceived waiting time, it can make a big difference in the proposed solutions and th e ir costs. For example, full-length mirrors probably cost a whole lot less tha n adding an elevator!

Sources: Based on J. Baker and M. Came ron , "The Effects of the Service Environment on Affect and Consu me r Perception o f Waiting Time: An Integrative Review and Research Propositions," Journal of the Academy of Marketing Science, Vol. 24, September 1996, pp. 338-349; R. He sse and G. Woolsey, Applied Management Science: A Quick and Dirty Approach, SRA Inc., Chicago, 1975; R. C. Larson, "Perspectives on Queues: Social J ustice and the Psychology of Queuing ," Operations Research, Vol. 35, No. 6, November/ December 1987, pp. 895- 905.

46 Pan I • Decision Making and Analytics: An Overview

• Outcomes (or results) may occur over an extended period. As a result, rev- enues, expen ses, a nd profits w ill be recorded at different points in time. To overcome this difficulty, a present-value a pproac h can be used if the results are quantifiable.

• It is assumed that future data w ill be similar to historical data. If this is n ot the case, the nature of the change has to be predicted and included in the analysis .

When the preliminary investigation is completed, it is possible to determine w hether a proble m really exists, where it is located, and h ow significant it is. A key issue is w he ther an informatio n system is reporting a problem o r o nly the sympto ms of a problem. For example, if reports indicate that sales are down, there is a p roblem, but the situation, no d oubt, is symptomatic of the problem. It is critical to know the real problem. Sometimes it may be a problem o f perception , incentive mismatch, or organizational processes rather than a poor decision model.

Problem Classification Problem classification is the conceptualization of a problem in an attempt to place it in a definable category, p ossibly leading to a standard solutio n approach . An important approach classifies problems according to the degree of structuredness evident in them. This ranges from totally structured (i.e., programmed) to totally unstructured (i.e., unpro- grammed), as described in Ch apte r 1.

Problem Decomposition

Many complex problems can be divided into subproblems. Solving the simpler subprob- le ms may help in solving a complex problem. Also, seemingly poorly structured problems sometimes have highly structured subproble ms. Just as a semistructured problem results w he n some phases of decisio n ma king are structured w h ereas other phases are unstruc- tured, so w h e n some subproblem s of a decision-making problem are structured w ith others unstructured, the problem itself is semistructured. As a DSS is develo ped and the d ecision maker and development staff learn more about the problem, it gains structure. Decomposition also facilitates communicatio n a mo ng decision makers. Decomposition is o ne of the m ost important aspects of the analytical hierarchy process. (AHP is discussed in Chapte r 11 , which h elps decision m akers incorp o rate both qualitative and quantitative factors into the ir decision-making models.)

Problem Ownership In the inte lligen ce phase , it is impo rta nt to establish problem own e rsh ip. A problem exists in a n organization o nly if someone or some group takes o n the responsibility of a ttacking it and if the o rganizatio n has the ability to solve it. The assignment of auth o r- ity to solve the problem is ca lled problem ownership. For example, a ma nager m ay feel that he or she has a problem because interest rates are too high. Becau se interest rate levels are determined at the n ational a nd international levels, a nd most managers ca n do nothing about the m , hig h interest rates a re the problem of the government, n ot a problem for a s p ecific company to solve. The problem compa nies actu ally face is h ow to operate in a high-inte rest-ra te e n vironme nt. For an individual compan y, the interest rate level s hould be h a ndle d as an uncontrollable (env ironme ntal) factor to be predicte d.

When problem owne rship is not establish ed, e ithe r someone is n o t doing his or her job or the problem at hand has yet to be identified as belonging to anyone . It is then important for someon e to e ithe r volunteer to own it or assig n it to someone.

The inte lligen ce p hase e nds w ith a fo rmal problem stateme nt.

Chapter 2 • Foundations and Technologies for Decis ion Making 47

SECTION 2.4 REVIEW QUESTIONS

1. What is the difference between a problem and its symptoms? 2. Why is it impo rtant to classify a p roblem? 3. What is meant by p roblem decomposition? 4. Why is establishing prob lem ownership so impo rta nt in the decision-making process?

2.5 DECISION MAKING: THE DESIGN PHASE The design phase involves finding or develo ping and a n alyzing possible courses of action . These include understanding the problem and testing solu tio ns for feasibility. A model of the decision-making problem is constructed, tested, and validated. Let u s first define a m odel.

Models1

A majo r ch aracteristic of a DSS and many BI tools (n o tably those of business a nalytics) is the inclusio n of at least o ne model. The basic idea is to perform the DSS analysis o n a m odel of reality rather than on the real system . A model is a simplified representation or abstrac- tio n of reality. It is usually simplified because reality is too complex to describe exactly and because much of the complexity is actually irre levant in solving a specific problem.

Mathematical (Quantitative) Models

The complexity of relationships in many organizatio nal systems is described mathemati- cally. Most DSS analyses are performed numerically with mathematical or oth er quantitative models.

The Benefits of Models

We use models for the following reason s:

• Manipulating a mo del (ch anging decisio n variables or the environment) is much easie r tha n manipulating a real system. Experimentation is easier and does not interfere w ith the o rga nizatio n's daily operations.

• Models enable the compression of time. Years of operation s can be simulated in minutes o r seconds of compute r time.

• The cost o f modeling analysis is much lower than the cost of a similar experiment conducted on a real system.

• The cost of m aking mistakes during a trial-and-error experime n t is much lower w hen models are used than w ith real systems .

• The business e nvironment involves considerable uncertainty. With modeling, a ma nager can estimate the risks resulting from specific action s.

• Mathematical models enable the analysis of a ve ry large, sometimes infinite , number of possible solutio ns. Even in simple proble ms, managers ofte n have a large number of alte rnatives fro m which to choose.

• Models enhance a nd reinforce learning and training . • Models and solution m e tho ds are readily available .

Modeling involves con ceptua lizing a proble m a nd ab stracting it to quantita tive and/ or qualitative form (see Chapter 9). For a mathematical model, the variables are

'Cautio n : Many students a nd professio nals view models strictly as those of "data modeling" in the context o f systems a nalysis and design. He re, we conside r analytical models su ch as those of linear progra mming, simula- tio n , a nd forecasting.

48 Pan I • Decisio n Making and Analytics: An Ove rview

identified, a nd their mutual relationships are established. Simplificatio ns are made , w h e n ever n ecessary, through assumptio n s . For example, a relationship b etween two variables may be assumed to be linear even though in reality there may be som e n on- linear effects. A proper b alan ce b e twee n the level of model simplification and the rep- rese ntatio n of reality must be obtained becau se of the cost-be n efit trade-off. A s impler model leads to lower development costs, easier manipulation, and a faster solution but is less representa tive o f the real proble m and can produce inaccurate results . However, a simple r model gene rally re quires fe wer data , o r the data are aggregated and easier to obtain.

The process of modeling is a combination of art a nd science. As a science, there a re many standard model classes available , and, w ith practice, an analyst can determine w hich one is applicable to a given situation. As an art, creativity a nd finesse are required w he n determining what simplifying assumptions can work, how to combine approp ri- ate features of the mode l classes, a nd how to integrate models to obtain valid solutio n s. Models h ave decision variables that describe the alte rnatives from among which a man ager must ch oose (e.g., h ow many cars to deliver to a sp ecific re n tal agen cy, h ow to advertise at specific times, w hich Web server to buy or lease), a result variable or a set of result variables (e.g. , profit, revenue, sales) that d escribes th e objective o r goal of the decis io n-making problem, and uncontrollable variables or pa ra me te rs (e.g., econo mic conditions) that describe the environment. The process of modeling involves determin- ing the (usu ally mathe matical, sometimes symbolic) relationships among the variables. These topics a re discussed in Chapter 9.

Selection of a Principle of Choice

A principle of choice is a crite rio n that d escribes the acceptability of a solutio n approach . In a model, it is a result variable. Selecting a principle of ch o ice is n ot part of the choice phase but involves h ow a p e rson establishes decision-making objective(s) a nd incorporates the objective(s) into the m odel(s). Are we w illing to assum e high risk , or do we pre fer a low-risk approach? Are we attempting to optimize or satisfice? It is also important to recognize the diffe re n ce b etween a criterion a nd a con straint (see Technology Ins ig hts 2.1). Among the many principles of choice, normative and descriptive are of prime importan ce.

TECHNOLOGY INSIGHTS 2.1 The Difference Between a Criterion and a Constraint

Many p eople new to the forma l study of decisio n making inadvenently confuse the concepts of criterio n a nd constraint. Ofte n , this is because a criterion may imply a constraint, either implicit or explicit, thereby adding to the confusion. For example, there may be a distance criterio n that the decisio n make r does no t want to travel too far from ho me . However, there is a n imp licit constraint that the alte rnatives from which he selects must be within a cen ain distance from his ho me . This constraint effectively says tha t if the distance fro m h o me is greater tha n a ceitain amo unt, the n the al ternative is not feasible- o r, rathe r, the distance to an alte rnative must b e less than or equ al to a certain numbe r (this would be a formal relatio nship in some models; in the model in this case, it reduces the search , considering fewer alternatives). This is similar to w h at happens in some cases w he n selecting a u niversity, w here schools beyond a single day's driv- ing distan ce would no t be conside red by most people, and, in fact, the utility fun ction (criterio n value) of dista nce can start o ut lo w close to home, p eak a t about 70 miles (about 100 km)- say, the dista nce between Atlanta (home) a nd Athe ns, Georgia- a nd sharply drop off th ereafter.

Chapter 2 • Foundations and Technologies for Decis ion Making 49

Normative Models

Nonnative models are models in which the chosen alternative is d emo nstrably the best of a ll possible alternatives. To find it, the decision maker should examine a ll the alterna- tives and prove that the o ne selected is indeed the best, which is what the person would no rmally want. This process is basically optimization. This is typically the goal of what we call prescriptive a nalytics (Part IV) . In o p eratio n al terms, optimizatio n can be achieved in o ne of three ways:

1 . Get the highest level of goal attainment from a given set of resources . For example, w hich alte rnative w ill yield the maximum profit from an investment of $10 million?

2. Find the alternative with the hig hest ratio of goa l attainment to cost (e.g., profit per d o llar invested) or maximize productivity.

3. Find the alternative w ith the lowest cost (or smallest amount of oth e r resources) that w ill meet an acceptable level of goals. For example, if your task is to select hardware for an intrane t with a minimum bandwidth, which alternative w ill accomplish this goal at the least cost?

Normative decision theory is based o n the following assumptio n s of rational decisio n m akers :

• Humans a re economic beings w hose objective is to maximize the attainment of goals; that is, the decision maker is ratio nal. (More of a good thing [revenue, fun] is better than less; less of a bad thing [cost, pa in] is better than more .)

• Fo r a decision-making situation, all viable alternative courses of actio n and their conseque n ces, o r at least the probability and the values of the consequ ences, are known .

• Decisio n make rs have an order or prefe ren ce that enables the m to rank the desir- ability of a ll conseque n ces of the an alysis (best to worst).

Are decision makers really ratio nal? Though there may be major an omalies in the pre- sumed rationality of financial and economic behavior, we take the view that they could be cau sed by incompetence, lack of knowledge, multiple goals being framed inadequ ately, mis- understanding of a decision maker's true expected utility, and time-pressure impacts. There are other anomalies, often caused by time pressure. For example, Stewart (2002) described a number of researchers working with intuitive decision making. The idea of "thinking with your gut" is obviously a heuristic approach to decision making. It works well for firefighters and military personnel o n the battlefield. One critical aspect of decision making in this mode is that many scen arios have been thought through in advance. Even when a situation is n ew, it can quickly be matched to an existing o ne o n-the-fly , and a reasonable solution can be obtained (through pattern recognition). Luce et al. (2004) described how emotions affect decisio n making, and Pauly (2004) discussed inconsistencies in decision making.

We believe that irrationality is cau sed by the factors listed previously. Fo r exam- ple, Tversky et al. 0990) investigated the phe nomen o n of preferen ce reve rsal, w hich is a known problem in applying the AHP to problems. Also, some crite rio n or preference may be omitted from the analysis. Ratner et a l. 0999) investigated how variety can cause individuals to choose less-preferred optio ns, even though they w ill e njoy them less. But we maintain that variety clearly has value, is part of a d ecision maker's utility, and is a criterion a nd/ or con straint that should be con sidered in decision m aking.

Suboptimization

By definition, optimizatio n requires a decision maker to consider the impact of each alter- native course o f actio n on the e ntire o rganizatio n because a decision made in o ne area may h ave significant effects (positive o r negative) o n other areas. Consider, for example , a

50 Pan I • Decision Making and Analytics: An Overview

marketing department that impleme nts a n electronic commerce (e-commerce) site. Within hours, o rders far exceed production capacity. The production department, which plans its own schedule , cannot meet demand. It may gear u p for as high demand as possi- ble. Ideally and inde pende ntly , the department sh ould produce o nly a few products in extre mely large quantities to minimize manufacturing costs. However, su ch a plan might result in large, costly inventories and marketing difficulties caused by the lack of a variety of products, especially if customers start to cancel orders that are n ot met in a timely way. This s ituation illustrates the sequential nature of decision making.

A systems point of view assesses the impact of every decision o n the entire sys- tem. Thus, the marketing department sho uld make its plans in conjunction with oth er departments. However, su ch an approach may require a complicated, expen sive, time- consuming analysis. In practice, the MSS builder may close the system within n arrow boundaries, considering o nly the part of the organization under study (the marketing a nd/ or production department, in this case). By simplifying, the m od e l th e n does n ot incorpo- rate certain complicated relatio n ships that describe interactio n s w ith and among the o ther departments. The oth er departments can be aggregated into simple model components. Such an approach is called suboptimization.

If a subo ptimal decision is made in one part of the o rganization w ithout considering the details of the rest o f the organizatio n , then a n optimal solutio n from the point of view of that p art may be inferior for the whole. However, su boptimizatio n may still be a very practical approach to decision making, and many problems are first approached from this perspective. It is possible to reach tentative conclusions (and generally u sable resu lts) by analyzing only a portion of a system, witho ut getting bogged down in too many details. After a solutio n is proposed, its potential effects on the remaining depa1tments of the organizatio n can be tested. If no significant negative effects are found, the solution can be imple m ented.

Suboptimization may also apply w h e n simplifying assumption s are used in mod- eling a specific problem. There may be too many details or too m any data to incorporate into a specific decision-making situatio n , and so n ot all of them are used in the model. If the solutio n to the mode l seems reasonable, it may be valid for the problem and thus be adopted. For example, in a production department, parts are often partitioned into A/ B/ C inventory categories. Generally, A items (e.g ., large gears, w h ole assemblies) are expen sive (say, $3,000 or more each), built to order in small batches, and inventoried in low quantities; C item s (e.g ., nuts, bolts, screws) are very inexpensive (say, less than $2) and o rdered a nd used in ve1y large quantities; and B ite ms fall in betwee n . All A items can be handle d by a detailed scheduling model and physically monitored closely by man- agement; B ite ms a re gen e rally som ewh at aggregated, their groupings are sch eduled, and m an ageme nt reviews these parts less frequently; an d C items are n ot sch eduled but are simply acquired or built based on a p o licy defined by management w ith a simple eco- n o mic order quantity (EOQ) o rdering system that assumes consta nt annual demand. The policy mig ht be reviewed o nce a year. This situation applies w he n determining all crite ria o r mo deling the entire problem becomes p rohibitively time-consuming or expensive.

Suboptimization may also involve simply bounding the search fo r an optimum (e.g., by a heuristic) by conside ring fewer criteria o r alternatives or by eliminating large portions of the problem from evaluation. If it takes too lo ng to solve a problem, a good- e nough solutio n found already may be used and the optimization effort terminated.

Descriptive Models

Descriptive models describe things as they are or as they are believed to be. These models are typically mathematically based. Descriptive models are extremely u seful in DSS for investigating the consequences of various alternative courses of actio n under

Chapter 2 • Foundations and Technologies for Decis ion Making 51

different configurations of inputs a nd processes. However, because a descriptive analysis checks the p erforma nce of the syste m for a given set of alternatives (rather than for all alternatives), there is no guarantee tha t a n alternative selected w ith the aid of descriptive analysis is o ptimal. In m any cases, it is o nly satisfactory.

Simulation is probably the most commo n descriptive modeling method. Simulation is the imitation of reality and has been applied to many areas of decisio n making. Computer and video games are a form of simulation : An a rtificial reality is created, and the game p layer lives within it. Vi11ual reality is also a form of s imulation because the e nvi- ronment is simulated, not real. A common u se of simulation is in manufacturing. Again, consider the productio n departme nt of a firm w ith complicatio n s caused by the marketing de paltme nt. The characteristics of each machine in a job shop along the supply chain can be described mathematically. Relatio nships can be established based o n how each machine physically runs and relates to others. Given a trial sch edule of batches of parts , it is possib le to measure how batches flow through the system and to use th e statistics fro m each machine. Alte rnative schedules may the n be tried and the statistics recorded until a reasonable schedule is found. Marketing can examine access and purchase pat- terns on its Web site. Simulation can be u sed to determine h ow to structure a Web site for improved p e rforma n ce an d to estimate future purchases. Both departments can therefore use primarily exp erime ntal modeling methods.

Classes of descriptive models include the following :

• Complex inventory decisions • Environmental impact analysis • Financial planning • Information flow • Markov a n alysis (predictio n s) • Scenario analysis • Simulation (alternative types) • Technological forecasting • Waiting-line (queuing) management

A number of nonmathematical descriptive models are available for d ecision mak- ing. One is the cognitive map (see Eden and Ackermann, 2002; and Jenkins, 2002). A cognitive map can he lp a decision ma ke r sketch out the impoltant qualitative factors and their causal re lationships in a messy decision-making situation. This helps the decision mak er (or decision-making group ) focus o n w hat is relevant and w hat is not, and the map evolves as more is learned about the problem. The map can h e lp the d ecisio n ma ker understand issues better, focus better, and reach closure. One inte resting software tool for cognitive mapping is Decisio n Explorer fro m Banxia Software Ltd. (banxia.com; try the demo) .

Another descriptive decisio n-ma king model is the u se of narratives to describe a decision-ma king situatio n. A narrative is a story that he lps a decisio n maker uncover the impoltant aspects of the situation and leads to better understanding and framing. This is extrem ely effective w he n a group is making a decision, and it can lead to a m o re com- mo n viewpoint, also called a frame. Juries in coult trials typically use n arrative-based approaches in reaching verdicts (see Allan, Frame, and Turney, 2003; Beach, 2005; and Denning, 2000).

Good Enough, or Satisficing

According to Simon 0977), most human decision making, w hethe r organizatio nal or indi- vidual, involves a w illingness to settle for a satisfactory solutio n , "something less than the best." When satisficing, the decisio n make r sets up a n aspiratio n , a goal, o r a desired

52 Pan I • Decision Making and Analytics: An Overview

level of performance and then searches the alternatives until o ne is found that achieves this level. The u sual reasons for satisficing are time pressures (e.g., decisions may lose value over time), the ability to achieve optimization (e.g., solving som e models could take a really long time, and recognition that the m arginal be nefit of a better solution is no t worth the marginal cost to obtain it (e.g. , in searching the Inte rnet, you can look at o nly so many Web sites before you run out of time and energy). In such a situation, the decision maker is behaving rationally, though in reality h e o r she is satisficing. Essen tially, satisficing is a form of suboptimizatio n . There may be a best solution , an optimum , but it would be difficult, if not impossible, to a ttain it. With a normative model, too much com- putation m ay be involved; w ith a descriptive model, it may n ot be possible to evaluate all the sets of alte rnatives.

Rela ted to satisficing is Simo n 's idea of bounded rationality. Humans h ave a limited capacity for rational thinking; they generally con stru ct and analyze a sim- plified model o f a real situation by considering fewer alternatives, criteria, and/ or con straints than actually exist. The ir behavior w ith respect to the simplified model m ay be ratio n a l. However, the ration al solutio n for the simplified model may n ot be rational for the real-world problem. Rationality is bounded not o nly by limitations on huma n processing cap acities, but also by individual differences, such as age, edu ca- tion, knowledge, a nd attitudes. Bounded ratio n ality is also why ma ny models are descriptive rather tha n n ormative. This may also explain why so m any good managers rely o n intuitio n , a n important aspect of good decis ion making (see Stewart, 2002 ; a nd Pauly, 2004).

Because rationality a nd the use of normative models lead to good decision s, it is n atural to ask w hy so ma ny bad decisions are made in practice. Intuitio n is a c ritical factor that d ecis io n m akers u se in solv ing unstructured and semistructured problem s. The best decision makers recognize the trade -off between the m argin al cost of obtain- ing further info rma tio n a nd an alysis versus the benefit of making a better decision. But sometimes decisions must be made quickly, and, ideally, the intuitio n of a season ed, excellent decision maker is called for. When ad equate planning, funding, or informa- tion is n o t ava ilable, o r w he n a decision maker is inexperie nced or ill trained, disaster can strike.

Developing (Generating) Alternatives

A significant part of the model-build ing process is gene rating alternatives. In optimization models (such as linear programming), the alternatives may be generated automatically by the model. In most decision situations, however, it is n ecessary to generate alternatives m anually . This can be a le ngthy process that involves searching an d creativity, perhaps utilizing electronic brainstorming in a GSS. It takes time and costs money. Issu es such as w hen to stop generating alternatives can be very impo rta nt. Too many alternatives can be d etrime ntal to the process o f decision making. A decisio n maker may suffer from info rma- tion overload.

Gen erating alternatives is heavily dependent o n the availability and cost of informa- tion a nd re quires expe rtise in the problem area. This is the least formal aspect of problem solving. Alternatives can be generated and evaluated u sing heuristics. The generatio n of a lte rnatives fro m either ind ividuals or groups can be supported by electronic brainstorm- ing software in a Web-based GSS.

Note that the search for alterna tives u sually occurs after the criteria for evaluating the a lte rnatives a re determined. This sequen ce can ease the search fo r alternatives and redu ce the effort involved in evalua ting them, but identifying potential alternatives can sometimes a id in ide ntifying c rite ria.

Chapter 2 • Foundations and Technologies for Decision Making 53

The outcome of every proposed alternative must be established. Depending on whether the decision-making problem is classified as one of certainty, risk, or uncertainty, diffe rent modeling approaches may be u sed (see Drummond, 2001; and Koller, 2000). These a re discussed in Chapter 9.

Measuring Outcomes

The value o f a n a lte rna tive is eva lu ated in terms of goal atta inme n t. Sometimes a n outcome is expressed directly in terms of a goal. For example, profit is a n outcome, profit maximization is a goal, and both are expressed in dollar terms. An outcome such as cu stomer satisfactio n may be measured by the number of complaints, by the level of loyalty to a product, or by ratings found through surveys . Ideally, a decision maker would wan t to deal w ith a single goal, but in practice, it is not unusual to have multiple goals (see Barba-Romero, 2001; and Koksalan a nd Zionts, 2001). When grou ps make decisions, each group participant may h ave a diffe re nt agenda . For example, executives mig ht want to maximize profit, marketing might want to maximize market penetration, operations might want to minimize costs, and stockholders mig ht want to maximize the bo tto m line . Typically, these goals conflict, so special multiple-criteria meth odologies have been developed to ha ndle this. One such me thod is the AHP. We will study AHP in Cha pter 9.

Risk

All decisions are made in an inhe re ntly unstable e nv ironment. This is d ue to the man y unpredictable events in both the economic and physical e nvironments . Some risk (m eas- ured as probability) may be due to inte rnal o rganizatio n al events, such as a valued employee quitting or becoming ill , w h ereas othe rs may be due to natural disasters, such as a hurricane . Aside fro m the human toll, one economic aspect of Hurricane Katrina was that the price of a gallon of gasoline doubled overnight due to uncertainty in the port capabilities, refining, a nd pipelines of the south e rn United States. What can a decisio n maker do in the face of such instability?

In gen eral, p eople have a tendency to measure uncertainty a nd risk badly. Purdy (2005) said that people tend to be overconfide nt and have an illusion of control in decisio n making. The results of experiments by Adam Goodie at the University of Georgia ind icate that m ost people are overconfident most of the tim e (Goodie, 2004). This m ay explain w hy people often feel that o ne more pull of a slot machine w ill definitely p ay o ff.

However, meth odologies for h andling extreme uncertainty do exist. For example, Yakov (2001) described a way to make good decisions based o n very little info rmatio n , using an information gap theory and methodology approach . Aside from estimating the potential utility o r va lue of a particular decision's o utcome, the best decision makers are capable of accurately estimating the risk associated with the outcomes that result from making each decision. Thus, on e impo rta nt task of a decision maker is to attribute a level of risk to the o utcome associated w ith each potential alte rnative being con sidered. Some decisions may lead to unacceptable risks in terms of success a nd can therefore be dis- carded or discounted immediately.

In some cases, some decisions a re assumed to be m ade under condition s of cer- ta inty simply because the e nvironment is assumed to be stable. Other decisions are made unde r conditio n s of uncertainty, w h ere risk is unknown. Still , a good decision ma ker can make working estimates of risk. Also, the p rocess of developing BI/DSS involves learning more about the situatio n , w hich leads to a more accurate assessment of the risks .

54 Pan I • Decision Making and Analytics: An Overview

Scenarios

A scenario is a statement of assumptio ns about the operating environment of a p articu- la r system at a given time; tha t is, it is a n arrative description of the d e cision-situation setting . A scenario d escribes the decision a nd uncontrollable variables and parameters for a sp ecific mo d eling situation. It may also p rovide the p rocedures and con straints for the modeling.

Scenarios originated in the theater, an d the term was borrowed for war gaming and large-scale simulations. Scenario planning and analysis is a DSS tool that can capture a whole range o f possibilities. A ma nager can constm ct a series of scenarios (i.e., w hat-if cases) , perform computerized analyses, and learn more abo u t the system and decision- making proble m w hile an alyzing it. Ideally, the manager can identify an excellent, p ossibly o ptimal, solution to the mo de l of the problem.

Scen arios are especially helpful in simulation s and what-if a nalyses . In both cases, we ch ange scenarios a nd examine the results. For example, we can ch ange the anticipated dema nd for hospitalization (an input variable for pla nning), thus creating a new scenario . The n we can measure the anticipated cash flow of the h ospital for each scenario .

Scenarios p lay an impo rtant role in decision making because they:

• Help identify opportunities a nd proble m areas • Provide flexibility in p lanning • Ide ntify the leading edges of cha nges that management sh ould monitor • He lp validate major mod e ling assumptions • Allow the decisio n maker to explore the behavior of a system throu g h a model • Help to check the sensitivity of proposed solutions to changes in the e nvironment,

as described by the scen ario

Possible Scenarios

The re may be thousands of possib le scena rios for every decision situatio n. However, the following are especia lly u seful in practice:

• The worst possible scenario • The best possible scenario • The most like ly scen a rio • The average scenario

The scen ario determines the context of the a nalysis to be performed .

Errors in Decision Making

The model is a critical component in the decisio n-making p rocess, but a decisio n maker m ay make a number of e rrors in its development and u se. Validating the model before it is used is critical. Gathering the rig ht amount of information , w ith th e right level of preci- sion and accuracy, to incorporate into the decision-making p rocess is also critical. Sawyer 0999) described "the seven deadly sins of decision making, " most of w hich are behavior o r informatio n re la ted .

SECTION 2.5 REVIEW QUESTIONS

1. Define optimization and contrast it w ith suboptimization. 2. Compare the normative a nd d escriptive approaches to d ecision making . 3. Define rational decision making. What does it really mean to be a rational decisio n

m aker? 4. Why do people exhibit bounded ratio n ality when solving problems?

Chapter 2 • Foundations and Technologies for Decision Making 55

5. Define scenario. How is a scenario u sed in decision m aking? 6. Some "errors" in decision making can be attributed to the n o tion of decisio n making

from the gut. Explain w h at is meant by this a nd h ow such e rrors can happ e n.

2.6 DECISION MAKING: THE CHOICE PHASE Choice is the critical act of decision making. The choice phase is the o ne in which the actual d ecisio n and the commitment to follow a certain course of action are m ade . The boundary between the design and ch oice phases is often unclear because certain activi- ties can be performed during both of them and because the decision maker can return freque ntly from cho ice activities to design activities (e.g. , generate new alternatives w hile performing an evaluatio n of existing o nes) . The cho ice phase includ es the search fo r, evaluation of, and recommendation of an appropriate solutio n to a model. A solution to a model is a sp ecific set of values for the decision variables in a selected alternative. Ch o ices can be evaluated as to the ir viability and profitability.

Note that solving a model is not the same as solving the problem the model represents. The solution to the mod e l yie lds a recomme nded solutio n to the problem. The p roblem is considered solved o nly if the recommended solution is su ccessfully implemented.

Solving a decision-making model invo lves searching for an appropriate course of actio n . Search approaches include analytical techniques (i.e. , solving a formula) , algorithms (i.e. , step-by-step procedures), heuristics (i.e., rules o f thumb), and blind search es (i.e ., sh ooting in the d ark, ideally in a logical way). These approaches are examined in Cha pter 9.

Each alternative must be evaluated. If an alternative has multiple goals, they must all be examine d and balanced against each oth er. Sensitivity analysis is u sed to deter- mine the robustness of any given alte rnative ; slig ht ch anges in the parameters sh o uld ideally lead to slight or no ch a nges in the alte rnative chosen. What-if analysis is used to explo re ma jo r ch an ges in the parameters. Goal seeking h e lps a m a nager deter- mine values of the decis ion variables to meet a specific objective . All this is discussed in Cha pte r 9 .

SECTION 2.6 REVIEW QUESTIONS

1. Explain the difference between a principle of choice and the actual choice phase of decisio n making.

2. Why do som e people claim that the choice phase is the point in time w h e n a decisio n is really made?

3. How can sen sitivity a nalysis h elp in the choice phase?

2.7 DECISION MAKING: THE IMPLEMENTATION PHASE In The Prince, Machiavelli astutely noted some 500 years ago that there was "nothing more difficult to carry out, nor more do ubtful of success, nor more dangerous to handle , than to initiate a new o rder of things. " The implementation of a proposed solution to a problem is , in effect, the initiatio n of a n ew order of things or the introduction of change. And change must be managed. User expectations must be managed as part of ch ange management.

The definitio n o f implementation is som ewh at complicated b ecau se impleme ntation is a lo ng, involved process w ith vague boundaries. Simplistically, the implementation phase involves putting a recommended solution to work, not necessarily implementing a compute r system. Many generic imple me nta tio n issues, such as resistan ce to change , degree of suppo rt o f top ma nageme nt, a nd u ser training, are important in dea ling w ith

56 Pan I • Decision Making and Analytics: An Overview

information system supported decision m aking. Indeed, many previous technology- related waves (e.g., business p rocess reengineering (BPR), knowledge ma nagement, e tc .) have faced mixed results mainly because of change management challenges and issues. Management of ch a nge is almost an entire discipline in itself, so we recogn ize its impo rtan ce a nd e n courage the readers to focus o n it indepen dently. Implemen tation a lso includes a thorou g h understanding of project management. Importan ce of project man- agement goes far beyond a nalytics, so the last few years have w itnessed a m ajor growth in ce1tificatio n progra ms fo r project ma nagers. A very p opular certificatio n now is P roject Management Professional (PMP). See pmi.org for more details .

Implementatio n must also involve collecting and a nalyzing data to learn from the previous decisions and improve the next decision. Although analysis of data is usually conducted to identify the problem and/ o r the solution, a n alytics should also be employed in the feedback process. This is especia lly true for any public p o licy decisions. We n eed to be sure that the data being used for problem identification is valid. Sometimes people find this o ut o nly afte r the impleme ntation phase.

The decisio n-making process, though conducted by people, can be improved with computer su pport, which is the subject of the n ext section.

SECTION 2. 7 REVIEW QUESTIONS

1. Define implementation. 2. How can DSS support the implementation of a decision?

2.8 HOW DECISIONS ARE SUPPORTED In Chapter 1, we d iscussed the n eed for computerized decision support and briefly described some decision aids. Here we relate specific technologies to the decision- making process (see Figure 2.2). Databases, data m arts, an d especially data ware h o u ses a re important technologies in supporting a ll phases of decisio n making. They provide the data that drive decision m a king.

Support for the Intelligence Phase

The primary requirement of decision support for the intelligence phase is the ability to scan exte rnal and internal information sources for opportunities and problems and to interpret w hat the scanning discovers. Web tools and sources are extrem ely useful for environmen tal

Phase

:- ---.-~1 __ 1n_t_e1_1ig_e_n_c_e_ 1~{ \~ --.-~1 __ o_e_s_ig_n __ I- \~ --.-~1 __ c_h_o_ic_e __ I- l ---.- ~1 _,m_p_le_m_e_n_ta_t_io_n~I-{

FIGURE 2.2 DSS Support.

ANN MIS

Data Mining, OLAP ES, ERP

ESS, ES, SCM CRM, ERP, KVS Management Science ANN

ESS, ES KMS, ERP

DSS ES

CRM SCM

Chapter 2 • Foundations and Technologies for Decision Making 57

scanning. Web browsers provide useful front ends for a variety of tools, from OLAF to data mining and data warehouses. Data sources can be internal or external. Internal sources may be accessible via a corporate intranet. External sources are many and varied.

Decision support/ BI technologies can be very helpful. For example, a data ware- house can support the intelligence phase by continuously monitoring both internal and external information, looking for early sign s of problems and opportunities through a Web-based enterprise information portal (also called a dashboard) . Similarly, (automatic) data (a nd Web) mining (which may include expert systems [ES], CRM, genetic a lgorithms , neural networks, and other analytics systems) and (manual) OLAP also support the intel- ligence phase by identifying relationships among activities and other factors. Geographic information systems (GIS) can be utilized either as stand-alone systems or integrated with these systems so that a decision maker can determine opportunities and problems in a spatial sense. These relationships can be exploited for competitive advantage (e.g., CRM identifies classes of customers to approach with specific products and services). A KMS can be used to identify similar past situations and how they were handled. GSS can be used to share information and for brainstorming. As seen in Chapter 14, even cell phone and GPS data can be captured to create a micro-view of customers and their habits.

Another aspect of identifying internal problems and capabilities involves monitoring the current status of operations. When something goes wrong, it can be identified quickly and the problem can be solved. Tools such as business activity monitoring (BAM), busi- ness process management (BPM) , and product life-cycle management (PLM) provide such capability to decision makers. Both routine and ad hoc reports can aid in the intelligence phase. For example, regular reports can be designed to assist in the problem-finding activity by comparing expectations with current and projected performance. Web-based OLAP tools are excellent at this task. So are visualization tools and electronic document management systems.

Expert systems (ES) , in contrast, can render advice regarding the nature of a prob- lem, its classification, its seriousness, and the like. ES can advise on the suitability of a solution approach and the likelihood of successfully solving the problem. One of the primary areas of ES success is interpreting information and d iagnosing problems. This capability can be exploited in the inte lligence phase. Even intelligent agents can be used to identify opportunities.

Much of the information used in seeking new opportunities is qualitative, or soft. This indicates a high level of unstructuredness in the problems, thus making DSS quite useful in the intelligence phase.

The Internet and advanced database technologies have created a glut of data and information available to decision makers-so much that it can detract from the quality and speed of decision making. It is important to recognize some issues in using data and analytics tools for decision making. First, to paraphrase baseball great Vin Scully, "data should be used the way a drunk uses a lamppost. For support, not for illumination." It is especially true when the focus is on understanding the problem. We should recognize that not all the data that may help understand the problem is available. To quote Einstein, "Not everything that counts can be counted, and not everything that can be counted counts. " There might be other issues that have to be recognized as well.

Support for the Design Phase

The design phase involves generating alternative courses of action, discussing the criteria for choices and their relative importance, and forecasting the future consequences of using various alternatives. Several of these activities can use standard models provided by a DSS (e.g., financial and forecasting models, available as applets) . Alternatives for struc- tured problems can be generated through the use of e ither standard or special models.

58 Pan I • Decision Making and Analytics: An Overview

However, the genera tion of alternatives for complex problems requires expertise that can be provided o nly by a human, brainstorming software, or an ES. OLAP and data mining softwa re are quite useful in identifying relatio nsh ips that can be u sed in m odels. Most DSS h ave quantitative a nalysis cap abilities, and an internal ES can assist with qualitative meth- ods as well as with the expertise required in selecting quantitative an alysis and forecasting models. A KMS should certainly be consulted to determine whether such a problem h as been en counte red before o r whether there are experts on h and w h o can provide quick unde rstanding and a nswers. CRM syste ms, revenue ma nageme nt systems, ERP , a nd SCM systems software are u seful in that they provide models of business processes that can test assumptio ns and scen arios. If a problem requires brainstorming to help identify important issu es and options, a GSS may prove helpful. Tools that provide cognitive mapping can also help. Cohen et al. (2001) described several Web-based tools that p rovide decisio n suppo rt, mainly in the design phase, by providing models a nd reporting of alternative results . Each of the ir cases has saved millio n s o f dollars annually by utilizing these tools. Such DSS are helping en gineers in product d esign as well as decision makers solving business problems.

Support for the Choice Phase

In additio n to providing models that rapidly ide ntify a best or good-en ough alternative, a DSS can support the choice phase throug h what-if and goal-seeking a nalyses. Different scenarios can be tested for the selected option to reinforce the final decision. Again, a KMS helps identify similar p ast experie n ces; CRM, ERP , and SCM systems are u sed to test the impacts of decisio ns in establishing their value, leading to an intelligent choice. An ES can be u sed to assess the desirability of certain solutions as well as to recommend an appropri- ate solutio n . If a group ma kes a decision, a GSS can provide support to lead to consensus.

Support for the Implementation Phase

This is w h ere "making the decision h app e n " occurs . The DSS benefits p rovided during imple me ntatio n may be as impo rta nt as o r even more importa nt than th ose in the earlier phases. DSS can be u sed in implementation activities such as decision communication, explanatio n, a nd justificatio n .

Implementation-phase DSS benefits are partly due to the vividness and d etail of analyses and reports. For example , one chief executive officer (CEO) gives employees and external parties n o t o nly the aggregate financial goals and cash needs for the n ear term, but also the calculation s, interme diate results, and statistics u sed in d etermining the aggregate figures. In additio n to communicating the financial goals unambiguously, the CEO sign als oth e r messages. Employees know that the CEO has thought through the assumptions behind the financial goals and is serious about their importance and attain- ability. Bankers a nd directors are shown that the CEO was personally involved in a n a- lyzing cash need s and is aware o f a nd respons ible fo r the implications of the fina ncing requests prepa red by the finance department. Each o f these messages improves decisio n imple me ntatio n in some way.

As mentioned earlier, reporting systems a nd other tools variou sly labeled as BAM, BPM, KMS , EIS, ERP, CRM, and SCM are all useful in tracking how well an implementation is working. GSS is useful for a tea m to collaborate in establishing implementation effec- tiveness . For example, a d ecisio n might be made to get rid of unprofitable customers. An effective CRM can identify classes of customers to get rid of, identify the impact of doing so, and then verify that it really worked that way.

All phases of the decision-making process can be suppo rted by improved communica- tion through collaborative computing via GSS and KMS. Computerized systems can facilitate communicatio n by he lping people explain and justify the ir suggestio ns and opinio ns.

Chapter 2 • Foundations and Technologies for Decision Making 59

Decision implementation can also be supported by ES. An ES can be used as an advi- sory system regarding implementation problems (such as handling resistance to change). Finally, an ES can provide training that may smooth the course of implementation.

Impacts along the value chain, though reported by an EIS through a Web-based enterprise information portal, are typically identified by BAM, BPM, SCM, and ERP systems. CRM systems report and update internal records, based on the impacts of the implementa- tion. These inputs are then used to identify new problems and opportunities- a return to the intelligence phase.

SECTION 2.8 REVIEW QUESTIONS

1. Describe how DSS/BI technologies and tools can aid in each phase of decision making. 2. Describe how new technologies can provide decision-making support.

Now that we have studied how technology can assist in decision making, we study some details of decision support systems (DSS) in the next two sections.

2.9 DECISION SUPPORT SYSTEMS: CAPABILITIES The early definitions of a DSS identified it as a system intended to support managerial decision makers in semistructured and unstructured decision situations. DSS were meant to be adjuncts to decision makers , extending their capabilities but not replacing their judg- ment. They were aimed at decisions that required judgment or at decisions that could not be completely supported by algorithms . Not specifically stated but implied in the early definitions was the notion that the system would be computer based, would operate inter- actively online, and preferably would have graphical output capabilities, now simplified via browsers and mobile devices.

A DSS Application

A DSS is typically built to support the solution of a certain problem or to evaluate an opportunity. This is a key difference between DSS and BI applications. In a very strict sense, business intelligence (BI) systems monitor situations and identify problems and/ or opportunities, using analytic methods. Reporting plays a major role in BI; the user generally must identify whether a particular situation warrants attention, and then analyti- cal methods can be applied. Again, although models and data access (generally through a data warehouse) are included in BI, DSS typically have their own databases and are developed to solve a specific problem or set of problems. They are therefore called DSS applications.

Formally, a DSS is an approach (or methodology) for supporting decision making . It uses an interactive, flexible, adaptable computer-based information system (CBIS) especia lly developed for supporting the solution to a specific unstructured manage- ment problem. It uses data, provides an easy user interface, and can incorporate the decision maker's own insights. In addition, a DSS includes models and is developed (possibly by end users) through an interactive and iterative process. It can support a ll phases of decision making and may include a knowledge component. Finally, a DSS can be used by a single user or can be Web based for use by many people at several locations.

Because there is no consensus on exactly what a DSS is, there is obviously no agree- ment on the standard characteristics and capabilities of DSS. The capabilities in Figure 2.3 constitute an ideal set, some members of which are described in the definitions of DSS and illustrated in the application cases.

The key characteristics and capabilities of DSS (as s hown in Figure 2.3) are:

60 Pan I • Decision Making and Analytics: An Overview

Data access

Modeling and analysis

Ease of development by end users

the process

14 Stand-alone,

integration, and Web-based

1 Semistructured or unstructured

problems

2 Support

managers at all levels

3 Support

individuals and groups

Interdependent or sequential

decisions

Support intelligence

design, choice , and implementation

Support variety of decision

processes and styles Effectiveness and efficiency

Interactive , ease of use

Adaptable and f lexible

FIGURE 2.3 Key Characteristics and Capabilities of DSS.

1. Support for decision makers, mainly in semistructured and unstructured situ ations, by bringing together human judgment and computerized information. Such prob- lems cannot be solved (or cannot be solved conveniently) by oth er computerized systems or through use of sta ndard quantitative methods or tools. Gen erally , these problems gain structure as the DSS is developed. Even some structured problems have been solved by DSS.

2 . Support for a ll managerial levels, ranging from top executives to line managers. 3. Support for individuals as well as groups . Less-structured problems often require the

involvement of individuals from d ifferent departments and organizational levels or even from d ifferent o rganizations. DSS support virtual teams through collaborative Web tools. DSS have been developed to support individual and group work, as well as to support individual decisio n making and groups of decision makers working somewhat independently.

4. Support fo r interdependent a nd/ or sequential decisions. The decisio ns may be made once, several times, or repeatedly .

5. Support in all phases of the decision-making process: intelligence, design, choice, and impleme ntation.

6. Support for a variety of decision-making processes and styles. 7. The decision m aker should be reactive, able to confront cha nging con ditions quickly,

an d able to adapt the DSS to meet these changes. DSS are flexible, so users can add, delete, combine, change, or rearrange basic elements. They are also flexible in that they can be readily modified to solve oth er, similar p roblems.

Chapter 2 • Foundations and Technologies for Decision Making 61

8 . User-friendliness, strong graphical capabilities, and a n atural language interactive human-machine interface can greatly increase the effectiveness of DSS. Most new DSS applications u se Web-based inte rfaces or mobile platform interfaces.

9 . Improvement of the effectiveness of decision making (e.g. , accuracy, timeliness, quality) rathe r tha n its efficie ncy (e.g., the cost of making decisio n s). When DSS are deployed , decision making often takes longer, but the decisions are better.

10. The decision maker h as comple te control over all step s of th e decisio n-making process in solving a proble m . A DSS sp ecifically aims to support, not to replace, the decision maker.

11. End u sers are able to develop a nd modify simple systems by them selves. Larger systems can be built with assistance from informatio n system (IS) specialists. Spreadsheet p ackages have been utilized in developing simpler systems. OLAP and data mining software, in conjunctio n w ith d ata warehouses, e nable u sers to build fairly large, complex DSS.

12. Models a re generally utilized to analyze decision-making situation s. The mod- e ling capability e n ables experim e ntation with different strategies under diffe re nt config urations .

13. Access is provided to a variety o f data sources, formats, a nd types, including GIS , multimedia , a nd object-o rie nted data.

14. The DSS can be employed as a stand-alone tool u sed by an individual decision maker in one locatio n or distributed throughout an o rganization and in several organizations along the supply chain. It can be integrated with other DSS and/ or applications, and it can be distributed internally and exte rnally, u sing networking and Web technologies.

These key DSS characteristics and cap abilities allow decision make rs to make better, more consistent decisions in a time ly m anne r, a nd they are provided by the major DSS compo nents, w hich we w ill describe after discussing various ways of classifying DSS (n ext) .

SECTION 2.9 REVIEW QUESTIONS

1. List the key characte ristics and capabilities of DSS. 2. Describe h ow providing support to a workgroup is different from providing support

to group work . Explain why it is impo rtant to differentiate these concepts. 3. What kinds of DSS can end users develop in spreadsheets? 4. Why is it so important to include a m odel in a DSS?

2.10 DSS CLASSIFICATIONS DSS application s have been classified in several different ways (see Power, 2002; Power and Sharda, 2009). The design process, as well as the operatio n a nd implementatio n of DSS, depends in many cases on the type of DSS involved . However, remember that not every DSS fits neatly into o n e category. Most fit into the classification provided by the Association for Information Syste ms Special Interest Group on Decision Support Systems (AIS SIGDSS). We discuss this classification but also point out a few other attempts at classifying DSS.

The AIS SIGDSS Classification for DSS

The AIS SIGDSS (ais.site-ym.com/group/SIGDSS) h as adopted a concise classification scheme for DSS that was proposed by Power (2002). It includes the following categories:

• Communicatio n s-driven and group DSS (GSS) • Data-driven DSS

62 Pan I • Decision Making and Analytics: An Overview

• Document-driven DSS • Knowledge-driven DSS, data mining, and management ES applications • Model-driven DSS

There may also be hybrids that combine two or more categories. These are called compound DSS. We discuss the major categories next.

COMMUNICATIONS-DRIVEN AND GROUP DSS Communications-driven and group DSS (GSS) include DSS tha t use computer, collaboration, and communication technologies to support groups in tasks that may or may not include decision making. Essentially, all DSS that support any kind of group work fall into this category. They include those tha