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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

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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

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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

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

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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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xiv Contents

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

Conte nts xvii

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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

Conte nts xix

xx Contents

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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xxii Preface

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

Preface xxiii

xxiv Preface

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

Preface XXV

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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xxviii Preface

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

xxix

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.

1

2

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 Responses

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, Englewood 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.