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Business Intelligence and Analytics: Systems for Decision

Support

(10th Edition)

Chapter 1: An Overview of Business

Intelligence, Analytics, and Decision Support

Business Intelligence and Analytics: Systems for Decision

Support

(10th Edition)

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

 ... (Continued…)

Learning Objectives  Learn the conceptual foundations of

the DSS methodology  Describe the BI methodology and

concepts and relate them to DSS  Understand the various types of

analytics  List the major tools of computerized

decision support

Opening Vignette…

Magpie Sensing Employs Analytics to Manage a Vaccine Supply Chain Effectively and Safely

 Company background  Problem  Proposed solution and results  Answer & discuss the case questions...

Opening Vignette…

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?

Changing Business Environment & Computerized Decision Support

 Companies are moving aggressively to computerized support of their operations  Business Intelligence

 Business Pressures–Responses– Support Model  Business pressures result of today's

competitive business climate  Responses to counter the pressures  Support to better facilitate the process

Business Pressures– Responses–Support Model

The Business Environment  The environment in which

organizations operate today is becoming more and more complex, creating  opportunities, and  problems.  Example: globalization.

 Business environment factors:  markets, consumer demands, technology,

and societal…

Business Environment Factors FACTOR DESCRIPTION Markets Strong competition

Expanding global markets Blooming electronic markets on the Internet Innovative marketing methods Opportunities for outsourcing with IT support

Need for real-time, on-demand transactions Consumer Desire for customization demand Desire for quality, diversity of products, and speed of delivery Customers getting powerful and less loyal Technology More innovations, new products, and new services

Increasing obsolescence rate Increasing information overload

Social networking, Web 2.0 and beyond Societal 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

Organizational Responses  Be Reactive, Anticipative, Adaptive,

and Proactive  Managers may take actions, such as

 Employ strategic planning.  Use new and innovative business models.  Restructure business processes.  Participate in business alliances.  Improve corporate information systems.  … more [in your book]

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 organization and its desired performance, as expressed in its mission, objectives, and goals, and the strategy to achieve them.

Managerial Decision Making  Management is a process by which

organizational goals are achieved by using resources.  Inputs: resources  Output: attainment of goals  Measure of success: outputs / inputs

 Management  Decision Making  Decision making: selecting the best

solution from two or more alternatives

The Nature of Managers’ Work Mintzberg's 10 Managerial Roles

Interpersonal 1. Figurehead 2. Leader 3. Liaison

Informational 4. Monitor 5. Disseminator 6. Spokesperson

Decisional 7. Entrepreneur 8. Disturbance handler 9. Resource allocator 10. Negotiator

Decision-Making Process  Managers usually make decisions

by following a four-step process (a.k.a. the scientific approach) 1. Define the problem (or 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.

Information Systems Support for Decision Making  Group communication and

collaboration  Improved data management  Managing data warehouses and Big

Data  Analytical support  Overcoming cognitive limits in

processing and storing information  Knowledge management  Anywhere, anytime support

An Early Decision Support Framework (by Gory and Scott-Morten, 1971)

An Early Decision Support Framework  Degree of Structuredness (Simon,

1977)  Decisions are classified as

 Highly structured (a.k.a. programmed)  Semi-structured  Highly unstructured (i.e.,

nonprogrammed)

 Types of Control (Anthony, 1965)  Strategic planning (top-level, long-

range)  Management control (tactical

planning)  Operational control

The Concept of DSS  DSS - interactive computer-based

systems, which help decision makers utilize data and models to solve unstructured problems

(Gorry and Scott-Morton, 1971)  Decision support systems couple the

intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions.

 DS as an Umbrella Term  Evolution of DS into Business Intelligence

A Framework for Business Intelligence (BI)

 BI is an evolution of decision support concepts over time

 Then: Executive Information System  Now: Everybody’s Information System

(BI)  BI systems are enhanced with

additional visualizations, alerts, and performance measurement capabilities

 The term BI emerged from industry

Definition of BI  BI is an umbrella term that combines

architectures, tools, databases, analytical tools, applications, and methodologies

 BI is a content-free expression, so it means different things to different people

 BI's major objective is to enable easy access to data (and models) to provide business managers with the ability to conduct analysis

 BI helps transform data, to information (and knowledge), to decisions, and finally to action

A Brief History of BI  The term BI was coined by the

Gartner Group in the mid-1990s  However, the concept is much older

 1970s - MIS reporting - static/periodic reports  1980s - Executive Information Systems (EIS)  1990s - OLAP, dynamic, multidimensional, ad-

hoc reporting -> coining of the term “BI”  2010s - Inclusion of AI and Data/Text Mining

capabilities; Web-based Portals/Dashboards, Big Data, Social Media, Analytics

 2020s - yet to be seen

The Evolution of BI Capabilities

The Architecture of BI  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 performance management (BPM) for monitoring and analyzing performance

 a user interface (e.g., dashboard)

A High-Level Architecture of BI

Data Warehouse

Technical staff

Data Warehouse Environment

Data Sources

Business Analytics Environment

Performance and Strategy

Business users Managers / executives

Built the data warehouse Access

Manipulation Results

BPM strategyü Organizing ü Summarizing ü Standardizing

Future component intelligent systems

User Interface - browser - portal - dashboard

Business Value of BI Analytical Applications

 Customer segmentation  Propensity to buy  Customer profitability  Fraud detection  Customer attrition  Channel optimization

Application Case 1.1

Sabre Helps Its Clients Through Dashboards and Analytics

Questions for Discussion 1. What is traditional reporting? How is it

used in the organization? 2. How can analytics be used to transform

the traditional reporting? 3. How can interactive reporting assist

organizations in decision making?

A Multimedia Exercise in Business Intelligence  Teradata University Network (TUN) www.teradatauniversitynetwork.com

 BSI Videos (Business Scenario Investigations)

www.youtube.com/watch?v=NXEL5F4_aKA

 Also look for other BSI Videos at TUN

http://www.teradatauniversitynetwork.com/

http://www.youtube.com/watch?v=NXEL5F4_aKA

DSS-BI Connections  Similarities and differences?

 Similar architectures, data focus, …  Direct vs. indirect support  Different target audiences  Commercially available systems

versus in-house development of solutions

 Origination – Industry vs. Academia  So, is DSS = BI ?

Analytics Overview  Analytics?

 Something new or just a new name for …  A Simple Taxonomy of Analytics

(proposed by INFORMS)  Descriptive Analytics  Predictive Analytics  Prescriptive Analytics

 Analytics or Data Science?

Analytics Overview

Application Case 1.2

Eliminating Inefficiencies at Seattle Children’s Hospital 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?

Application Case 1.3

Analysis at the Speed of Thought

Questions for Discussion 1. What are the desired

functionalities of a reporting tool? 2. What advantages were derived by

using a reporting tool in the case?

Application Case 1.4

Moneyball: Analytics in Sports and Movies

Questions for Discussion 1. How is predictive analytics applied

in Moneyball? 2. What is the difference between

objective and subjective approaches in decision making?

Application Case 1.5

Analyzing Athletic Injuries

Questions for Discussion 1. What types of analytics are applied in the

injury analysis? 2. How do visualizations aid in understanding

the data and delivering insights into the data?

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

sequence analysis?

Application Case 1.6

Industrial and Commercial Bank of China (ICBC) Employs Models to Reconfigure Its Branch Networks

Questions for Discussion 1. How can analytical techniques help organizations to

retain competitive advantage? 2. How can descriptive and predictive analytics help in

pursuing prescriptive analytics? 3. What kind of prescriptive analytic techniques are

employed in the case study? 4. Are the prescriptive models once built good forever?

Introduction to Big Data Analytics

 Big Data?  Not just big!  Volume  Variety  Velocity

 More of Big Data and related analytics tools and techniques are covered in Chapter 13.

Application Case 1.7

Gilt Groupe’s Flash Sales Streamlined by Big Data Analytics

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?

End-of-Chapter Application Case

Nationwide Insurance Used BI to Enhance Customer Service

Questions for Discussion 1. Why did Nationwide need an enterprise-wide data

warehouse? 2. How did integrated data drive the business value? 3. What forms of analytics are employed at

Nationwide? 4. With integrated data available in an enterprise data

warehouse, what other applications could Nationwide potentially develop?

Plan of the Book  Part I - Decision Making and Analytics: An Overview

 (Chapters 1 & 2)  Part II - Descriptive Analytics

 (Chapters 3 & 4)  Part III - Predictive Analytics

 Chapters 5 - 8  Part IV - Prescriptive Analytics

 Chapter 9 - 12  Part V - Big Data and Future Directions for Business

Analytics  Chapters 13 & 14

 PLUS - Online Supplements  …

End of the Chapter

 Questions / Comments…

any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United

States of America.

Slide 1
Learning Objectives
Learning Objectives
Opening Vignette…
Opening Vignette…
Changing Business Environment & Computerized Decision Support
Business Pressures–Responses–Support Model
The Business Environment
Business Environment Factors
Organizational Responses
Closing the Strategy Gap
Managerial Decision Making
The Nature of Managers’ Work Mintzberg's 10 Managerial Roles
Decision-Making Process
Information Systems Support for Decision Making
Slide 16
An Early Decision Support Framework
The Concept of DSS
A Framework for Business Intelligence (BI)
Definition of BI
A Brief History of BI
The Evolution of BI Capabilities
The Architecture of BI
A High-Level Architecture of BI
Business Value of BI Analytical Applications
Application Case 1.1
A Multimedia Exercise in Business Intelligence
DSS-BI Connections
Analytics Overview
Analytics Overview
Application Case 1.2
Application Case 1.3
Application Case 1.4
Application Case 1.5
Application Case 1.6
Introduction to Big Data Analytics
Application Case 1.7
End-of-Chapter Application Case
Plan of the Book
End of the Chapter
Slide 41