BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DURSUN DELEN EFRAIM TURBAN TENTH EDITION PDF

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BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DURSUN DELEN EFRAIM TURBAN TENTH EDITION .• TENTH EDITION BUSINESS INTELLIGENCE AND ANALYTICS: SYSTEMS FOR DECISION SUPPORT Ramesh Sharda Oklahoma State University Dursun Delen Oklahoma State University Efraim Turban University of Hawaii With contrib...

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BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DURSUN DELEN EFRAIM TURBAN

TENTH EDITION

.•

TENTH EDITION

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 Tbe University of Georgia

Ting-Peng Liang National Sun Yat-sen University

David King ]DA Software Group, Inc.

PEARSON Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo

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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 2013028826 10 9 8 7 6 5 4 3 2 1

PEARSON

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

PART II

1

Chapter 1

An Overview of Business Intelligence, Analytics, and Decision Support 2

Chapter 2

Foundations and Technologies for Decision Making

Descriptive Analytics

77

Chapter 3

Data Warehousing

Chapter 4

Business Reporting, Visual Analytics, and Business Performance Management 135

PART Ill Predictive Analytics

78

185

Chapter 5

Data Mining

Chapter 6

Techniques for Predictive Modeling

Chapter 7

Text Analytics, Text Mining, and Sentiment Analysis

Chapter 8

Web Analytics, Web Mining, and Social Analytics

186

PART IV Prescriptive Analytics Chapter 9

37

243 288

338

391

Model-Based Decision Making: Optimization and MultiCriteria 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 Index

634

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

1.3

Managerial Decision Making The Nature of Managers' Work The Decision-Making Process

5

7

7 8

1.4

Information Systems Support for Decision Making

1.5

An Early Framework for Computerized Decision Support 11 The Gorry and Scott-Morton Classical Framework Computer Support for Structured Decisions

Computer Support for Semistructured Problems

13 13

The Concept of Decision Support Systems (DSS) DSS as an Umbrella Term

14

A Framework for Business Intelligence (Bl) Definitions of Bl

14

14

A Brief History of Bl

14

The Architecture of Bl Styles of Bl

13

13

Evolution of DSS into Business Intelligence 1.7

11

12

Computer Support for Unstructured Decisions 1.6

9

15

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 1.8

18

Business Analytics Overview 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

Predictive Analytics iv

19

22

22

Conte nts ~

APPLICATION CASE 1.4 Moneybal/: Analytics in Sports and Movies

~

APPLICATION CASE 1.5 Analyzing Athletic Injuries

Prescriptive Analytics

23

24

24

~ APPLICATION CASE 1.6 Industrial and Commercial Bank of China

(ICBC) Employs Models to Reconfigure Its Branch Network

1.9

Analytics Applied to Different Domains 26 Analytics or Data Science? 26 Brief Introduction to Big Data Analytics What Is Big Data? 27 ~

25

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 Part II: Descriptive Analytics 30

29

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 The Book's Web Site 32 Chapter Highlights

32

Questions for Discussion ~

•

Key Terms 33

•

32 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 2.1 2.2

Opening Vignette: Decision Modeling at HP Using Spreadsheets 38 Decision Making: Introduction and Definitions 40 Characteristics of Decision Making 40 A Working Definition of Decision Making Decision-Making Disciplines 41

2.3

2.4

41

Decision Style and Decision Makers 41 Phases of the Decision-Making Process 42 Decision Making: The Intelligence Phase 44 Problem (or Opportunity) Identification 45 ~

APPLICATION CASE 2.1 Making Elevators Go Faster!

Problem Classification

46

Problem Decomposition Problem Ownership

46

46

45

37

v

vi

Contents

2.5

Decision Making: The Design Phase Models

47

Mathematical (Quantitative) Models The Benefits of Models Normative Models Suboptimization

47

47

Selection of a Principle of Choice

48

49 49

Descriptive Models

50

Good Enough, or Satisficing

51

Developing (Generating) Alternatives Measuring Outcomes Risk

47

52

53

53

Scenarios

54

Possible Scenarios

54

Errors in Decision Making

54

2.6

Decision Making: The Choice Phase

2.7

Decision Making: The Implementation Phase

2.8

How Decisions Are Supported Support for the Intelligence Phase Support for the Design Phase

57

Support for the Choice Phase

58

56

58

Decision Support Systems: Capabilities A DSS Application

55

56

Support for the Implementation Phase 2.9

55

59

59

2.10 DSS Classifications

61

The AIS SIGDSS Classification for DSS Other DSS Categories

61

63

Custom-Made Systems Versus Ready-Made Systems

63

2.11 Components of Decision Support Systems

The Data Management Subsystem

64

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! Chapter Highlights

72

Questions for Discussion ~

•

Key Terms 73

•

70

73

Exercises

74

END-OF-CHAPTER APPLICATION CASE Logistics Optimization in a

Major Shipping Company (CSAV) References

75

74

Conte nts

Part II Descriptive Analytics Chapter 3 Data Warehousing

77 78

3.1

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

3.2

Data Warehousing Definitions and Concepts What Is a Data Warehouse?

81

A Historical Perspective to Data Warehousing Characteristics of Data Warehousing Data Marts

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

Data Warehousing Process Overview ~

3.4

83

84

Enterprise Data Warehouses (EDW) Metadata 85

3.3

81

84

Operational Data Stores

~

Data Warehousing Architectures Which Architecture Is the Best?

90 93

96

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

98

APPLICATION CASE 3.3 BP Lubricants Achieves BIGS Success

Extraction, Transfonnation, and Load 3.6

87

APPLICATION CASE 3.2 Data Warehousing Helps MultiCare Save More Lives 88

Alternative Data Warehousing Architectures

3.5

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 Representation of Data in Data Warehouse Analysis of Data in the Data Warehouse OLAP Versus OLTP OLAP Operations

109

110 11 0

Real-Time Data Warehousing ~

113

APPLICATION CASE 3.6 EDW Helps Connect State Agencies in Michigan 115

Massive Data Warehouses and Scalability 3.8

107

108

Data Warehousing Implementation Issues ~

98

100

Data Warehouse Development ~

3.7

81

116

117

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

vii

viii

Contents

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

•

Questions for Discussion

Key Terms

128

•

128

Exercises

129

.... END-OF-CHAPTER APPLICATION CASE Continental Airlines Flies High with Its Real-Time Data Warehouse

References

131

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 What Is a Business Report?

139

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

4.3

Data and Information Visualization

144

145

..,. APPLICATION CASE 4.3 Tableau Saves Blastrac Thousands of Dollars with Simplified Information Sharing

A Brief History of Data Visualization

146

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 Basic Charts and Graphs

Specialized Charts and Graphs 4.5

151

The Emergence of Data Visualization and Visual Analytics 154 Visual Analytics

156

High-Powered Visual Analytics Environments 4.6

150

150

Performance Dashboards

158

160

.... APPLICATION CASE 4.5 Dallas Cowboys Score Big with Tableau and Teknion

161

Conte nts

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 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 Enrich Dashboard with Business Users' Comments Present Information in Three Different Levels

4.7

166

~

4.8

166

167

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

Performance Measurement Key Performance Indicator (KPI)

170

171

Performance Measurement System 4.9

166

166

Business Performance Management Closed-Loop BPM Cycle

165

165

Pick the Right Visual Construct Using Dashboard Design Principles Provide for Guided Analytics

165

Balanced Scorecards The Four Perspectives

172

172

173

The Meaning of Balance in BSC

17 4

Dashboards Versus Scorecards

174

4.10 Six Sigma as a Performance Measurement System

The DMAIC Performance Model

175

176

Balanced Sco...