DATA MINING

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

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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 (Computer 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

iv

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

Conte nts v

vi Contents

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

Conte nts 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 • 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

Conte nts ix

x Contents

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

Conte nts xi

xii Contents

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

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