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Chapter 4 – discussion question #1-5 & exercise 1. Note: For exercise 1,the link provided in the textbook is not working, use this alternative link: 




Ramesh Sharda
Oklahoma State University

Dursun Delen
Oklahoma State University

Efraim Turban
University of Hawaii

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Preface xxv

About the Authors xxxiv

PART I Introduction to Analytics and AI 1
Chapter 1 Overview of Business Intelligence, Analytics,

Data Science, and Artificial Intelligence: Systems
for Decision Support 2

Chapter 2 Artificial Intelligence: Concepts, Drivers, Major
Technologies, and Business Applications 73

Chapter 3 Nature of Data, Statistical Modeling, and
Visualization 117

PART II Predictive Analytics/Machine Learning 193
Chapter 4 Data Mining Process, Methods, and Algorithms 194

Chapter 5 Machine-Learning Techniques for Predictive
Analytics 251

Chapter 6 Deep Learning and Cognitive Computing 315

Chapter 7 Text Mining, Sentiment Analysis, and Social
Analytics 388

PART III Prescriptive Analytics and Big Data 459
Chapter 8 Prescriptive Analytics: Optimization and

Simulation 460

Chapter 9 Big Data, Cloud Computing, and Location Analytics:
Concepts and Tools 509

PART IV Robotics, Social Networks, AI and IoT 579
Chapter 10 Robotics: Industrial and Consumer Applications 580

Chapter 11 Group Decision Making, Collaborative Systems, and
AI Support 610

Chapter 12 Knowledge Systems: Expert Systems, Recommenders,
Chatbots, Virtual Personal Assistants, and Robo
Advisors 648

Chapter 13 The Internet of Things as a Platform for Intelligent
Applications 687

PART V Caveats of Analytics and AI 725
Chapter 14 Implementation Issues: From Ethics and Privacy to

Organizational and Societal Impacts 726

Glossary 770

Index 785




Preface xxv

About the Authors xxxiv

PART I Introduction to Analytics and AI 1

Chapter 1 Overview of Business Intelligence, Analytics, Data
Science, and Artificial Intelligence: Systems for Decision
Support 2
1.1 Opening Vignette: How Intelligent Systems Work for

KONE Elevators and Escalators Company 3

1.2 Changing Business Environments and Evolving Needs for
Decision Support and Analytics 5

Decision-Making Process 6

The Influence of the External and Internal Environments on the Process 6

Data and Its Analysis in Decision Making 7

Technologies for Data Analysis and Decision Support 7

1.3 Decision-Making Processes and Computerized Decision
Support Framework 9

Simon’s Process: Intelligence, Design, and Choice 9

The Intelligence Phase: Problem (or Opportunity) Identification 10
0 APPLICATION CASE 1.1 Making Elevators Go Faster! 11

The Design Phase 12

The Choice Phase 13

The Implementation Phase 13

The Classical Decision Support System Framework 14

A DSS Application 16

Components of a Decision Support System 18

The Data Management Subsystem 18

The Model Management Subsystem 19

Telecommunications Rate Decisions 20

The User Interface Subsystem 20

The Knowledge-Based Management Subsystem 21

1.4 Evolution of Computerized Decision Support to Business
Intelligence/Analytics/Data Science 22

A Framework for Business Intelligence 25

The Architecture of BI 25

The Origins and Drivers of BI 26

Data Warehouse as a Foundation for Business Intelligence 27

Transaction Processing versus Analytic Processing 27

A Multimedia Exercise in Business Intelligence 28

Contents v

1.5 Analytics Overview 30

Descriptive Analytics 32
0 APPLICATION CASE 1.3 Silvaris Increases Business with Visual

Analysis and Real-Time Reporting Capabilities 32
0 APPLICATION CASE 1.4 Siemens Reduces Cost with the Use of Data

Visualization 33

Predictive Analytics 33
0 APPLICATION CASE 1.5 Analyzing Athletic Injuries 34

Prescriptive Analytics 34
0 APPLICATION CASE 1.6 A Specialty Steel Bar Company Uses Analytics

to Determine Available-to-Promise Dates 35

1.6 Analytics Examples in Selected Domains 38

Sports Analytics—An Exciting Frontier for Learning and Understanding
Applications of Analytics 38

Analytics Applications in Healthcare—Humana Examples 43
0 APPLICATION CASE 1.7 Image Analysis Helps Estimate Plant Cover 50

1.7 Artificial Intelligence Overview 52

What Is Artificial Intelligence? 52

The Major Benefits of AI 52

The Landscape of AI 52
0 APPLICATION CASE 1.8 AI Increases Passengers’ Comfort and

Security in Airports and Borders 54

The Three Flavors of AI Decisions 55

Autonomous AI 55

Societal Impacts 56
0 APPLICATION CASE 1.9 Robots Took the Job of Camel-Racing Jockeys

for Societal Benefits 58

1.8 Convergence of Analytics and AI 59

Major Differences between Analytics and AI 59

Why Combine Intelligent Systems? 60

How Convergence Can Help? 60

Big Data Is Empowering AI Technologies 60

The Convergence of AI and the IoT 61

The Convergence with Blockchain and Other Technologies 62
0 APPLICATION CASE 1.10 Amazon Go Is Open for Business 62

IBM and Microsoft Support for Intelligent Systems Convergence 63

1.9 Overview of the Analytics Ecosystem 63

1.10 Plan of the Book 65

1.11 Resources, Links, and the Teradata University Network
Connection 66

Resources and Links 66

Vendors, Products, and Demos 66

Periodicals 67

The Teradata University Network Connection 67

vi Contents

The Book’s Web Site 67
Chapter Highlights 67 • Key Terms 68

Questions for Discussion 68 • Exercises 69

References 70

Chapter 2 Artificial Intelligence: Concepts, Drivers, Major
Technologies, and Business Applications 73
2.1 Opening Vignette: INRIX Solves Transportation

Problems 74

2.2 Introduction to Artificial Intelligence 76

Definitions 76

Major Characteristics of AI Machines 77

Major Elements of AI 77

AI Applications 78

Major Goals of AI 78

Drivers of AI 79

Benefits of AI 79

Some Limitations of AI Machines 81

Three Flavors of AI Decisions 81

Artificial Brain 82

2.3 Human and Computer Intelligence 83

What Is Intelligence? 83

How Intelligent Is AI? 84

Measuring AI 85
0 APPLICATION CASE 2.1 How Smart Can a Vacuum Cleaner Be? 86

2.4 Major AI Technologies and Some Derivatives 87

Intelligent Agents 87

Machine Learning 88
0 APPLICATION CASE 2.2 How Machine Learning Is Improving Work

in Business 89

Machine and Computer Vision 90

Robotic Systems 91

Natural Language Processing 92

Knowledge and Expert Systems and Recommenders 93

Chatbots 94

Emerging AI Technologies 94

2.5 AI Support for Decision Making 95

Some Issues and Factors in Using AI in Decision Making 96

AI Support of the Decision-Making Process 96

Automated Decision Making 97
0 APPLICATION CASE 2.3 How Companies Solve Real-World Problems

Using Google’s Machine-Learning Tools 97

Conclusion 98

Contents vii

2.6 AI Applications in Accounting 99

AI in Accounting: An Overview 99

AI in Big Accounting Companies 100

Accounting Applications in Small Firms 100
0 APPLICATION CASE 2.4 How EY, Deloitte, and PwC Are Using AI 100

Job of Accountants 101

2.7 AI Applications in Financial Services 101

AI Activities in Financial Services 101

AI in Banking: An Overview 101

Illustrative AI Applications in Banking 102

Insurance Services 103
0 APPLICATION CASE 2.5 US Bank Customer Recognition and

Services 104

2.8 AI in Human Resource Management (HRM) 105

AI in HRM: An Overview 105

AI in Onboarding 105
0 APPLICATION CASE 2.6 How Alexander Mann Solutions (AMS) Is

Using AI to Support the Recruiting Process 106

Introducing AI to HRM Operations 106

2.9 AI in Marketing, Advertising, and CRM 107

Overview of Major Applications 107

AI Marketing Assistants in Action 108

Customer Experiences and CRM 108
0 APPLICATION CASE 2.7 Kraft Foods Uses AI for Marketing

and CRM 109

Other Uses of AI in Marketing 110

2.10 AI Applications in Production-Operation
Management (POM) 110

AI in Manufacturing 110

Implementation Model 111

Intelligent Factories 111

Logistics and Transportation 112
Chapter Highlights 112 • Key Terms 113

Questions for Discussion 113 • Exercises 114

References 114

Chapter 3 Nature of Data, Statistical Modeling, and Visualization 117
3.1 Opening Vignette: SiriusXM Attracts and Engages a

New Generation of Radio Consumers with Data-Driven
Marketing 118

3.2 Nature of Data 121

3.3 Simple Taxonomy of Data 125
0 APPLICATION CASE 3.1 Verizon Answers the Call for Innovation: The

Nation’s Largest Network Provider uses Advanced Analytics to Bring
the Future to its Customers 127

viii Contents

3.4 Art and Science of Data Preprocessing 129
0 APPLICATION CASE 3.2 Improving Student Retention with

Data-Driven Analytics 133

3.5 Statistical Modeling for Business Analytics 139

Descriptive Statistics for Descriptive Analytics 140

Measures of Centrality Tendency (Also Called Measures of Location or
Centrality) 140

Arithmetic Mean 140

Median 141

Mode 141

Measures of Dispersion (Also Called Measures of Spread or
Decentrality) 142

Range 142

Variance 142

Standard Deviation 143

Mean Absolute Deviation 143

Quartiles and Interquartile Range 143

Box-and-Whiskers Plot 143

Shape of a Distribution 145
0 APPLICATION CASE 3.3 Town of Cary Uses Analytics to Analyze Data

from Sensors, Assess Demand, and Detect Problems 150

3.6 Regression Modeling for Inferential Statistics 151

How Do We Develop the Linear Regression Model? 152

How Do We Know If the Model Is Good Enough? 153

What Are the Most Important Assumptions in Linear Regression? 154

Logistic Regression 155

Time-Series Forecasting 156
0 APPLICATION CASE 3.4 Predicting NCAA Bowl Game
Outcomes 157

3.7 Business Reporting 163
0 APPLICATION CASE 3.5 Flood of Paper Ends at FEMA 165

3.8 Data Visualization 166

Brief History of Data Visualization 167
0 APPLICATION CASE 3.6 Macfarlan Smith Improves Operational

Performance Insight with Tableau Online 169

3.9 Different Types of Charts and Graphs 171

Basic Charts and Graphs 171

Specialized Charts and Graphs 172

Which Chart or Graph Should You Use? 174

3.10 Emergence of Visual Analytics 176

Visual Analytics 178

High-Powered Visual Analytics Environments 180

3.11 Information Dashboards 182

Contents ix

0 APPLICATION CASE 3.7 Dallas Cowboys Score Big with Tableau
and Teknion 184

Dashboard Design 184
0 APPLICATION CASE 3.8 Visual Analytics Helps Energy Supplier Make

Better Connections 185

What to Look for in a Dashboard 186

Best Practices in Dashboard Design 187

Benchmark Key Performance Indicators with Industry Standards 187

Wrap the Dashboard Metrics with Contextual Metadata 187

Validate the Dashboard Design by a Usability Specialist 187

Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard 188

Enrich the Dashboard with Business-User Comments 188

Present Information in Three Different Levels 188

Pick the Right Visual Construct Using Dashboard Design Principles 188

Provide for Guided Analytics 188
Chapter Highlights 188 • Key Terms 189

Questions for Discussion 190 • Exercises 190

References 192

PART II Predictive Analytics/Machine Learning 193

Chapter 4 Data Mining Process, Methods, and Algorithms 194
4.1 Opening Vignette: Miami-Dade Police Department Is Using

Predictive Analytics to Foresee and Fight Crime 195

4.2 Data Mining Concepts 198
0 APPLICATION CASE 4.1 Visa Is Enhancing the Customer

Experience while Reducing Fraud with Predictive Analytics
and Data Mining 199

Definitions, Characteristics, and Benefits 201

How Data Mining Works 202
0 APPLICATION CASE 4.2 American Honda Uses Advanced Analytics to

Improve Warranty Claims 203

Data Mining Versus Statistics 208

4.3 Data Mining Applications 208
0 APPLICATION CASE 4.3 Predictive Analytic and Data Mining Help

Stop Terrorist Funding 210

4.4 Data Mining Process 211

Step 1: Business Understanding 212

Step 2: Data Understanding 212

Step 3: Data Preparation 213

Step 4: Model Building 214
0 APPLICATION CASE 4.4 Data Mining Helps in
Cancer Research 214

Step 5: Testing and Evaluation 217

x Contents

Step 6: Deployment 217

Other Data Mining Standardized Processes and Methodologies 217

4.5 Data Mining Methods 220

Classification 220

Estimating the True Accuracy of Classification Models 221

Estimating the Relative Importance of Predictor Variables 224

Cluster Analysis for Data Mining 228
0 APPLICATION CASE 4.5 Influence Health Uses Advanced Predictive

Analytics to Focus on the Factors That Really Influence People’s
Healthcare Decisions 229

Association Rule Mining 232

4.6 Data Mining Software Tools 236
0 APPLICATION CASE 4.6 Data Mining goes to Hollywood: Predicting

Financial Success of Movies 239

4.7 Data Mining Privacy Issues, Myths, and Blunders 242
0 APPLICATION CASE 4.7 Predicting Customer Buying Patterns—

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