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TENTH EDITIONBUSINESS INTELLIGENCEAND ANALYTICS:SYSTEMS FOR DECISION SUPPORTGlobal EditionRamesh ShardaOklahoma State UniversityDursun DelenOklahoma State UniversityEfraim TurbanUniversity of HawaiiWitb contributions byJ. E. AronsonThe University of GeorgiaTing-Peng LiangNational Sun Yat-sen UniversityDavid KingJDA Software Group, Inc.PEARSONBoston Columbus Indianapolis New York San Francisco Upper Saddle RiverAmsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal TorontoDelhi Mexico City Säo Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo

CONTENTSPreface 21About the Authors 29Part IDecision Making and Analytics: An Overview31Chapter 1 An Overview of Business Intelligence, Analytics, andDecision Support 321.1Opening Vignette: Magpie Sensing Employs Analytics toManage a Vaccine Supply Chain Effectively and Safely 331.2Changing Business Environments and ComputerizedDecision Support 35The Business Pressures-Responses-Support Model1.3Managerial Decision MakingThe Nature of Managers' WorkThe Decision-Making Process353737381.4Information Systems Support for Decision Making1.5An Early Framework for Computerized DecisionSupport 4139The Gorry and Scott-Morton Classical Framework 41Computer Support for Structured Decisions 42Computer Support for Unstructured Decisions 43Computer Support for Semistructured Problems 431.6The Concept of Decision Support Systems (DSS)DSS as an Umbrella Term43Evolution of DSS into Business Intelligence1.744A Framework for Business Intelligence (Bl)Definitions of Bl4444A Brief History of Bl44The Architecture of BlStyles of Bl434545The Origins and Drivers of Bl 46A Multimedia Exercise in Business Intelligence 46 APPLICATION CASE 1.1 Sabre Helps Its Clients Through Dashboardsand Analytics 47The DSS—Bl Connection1.848Business Analytics Overview 49Descriptive Analytics 50 APPLICATION CASE 1.2 Eliminating Inefficiencies at SeattleChildren's Hospital 51 APPLICATION CASE 1.3 Analysis at the Speed of Thought 52Predictive Analytics 52

Contents APPLICATION GASE 1.4 Moneyball: Analytics in Sports and Movies 53» APPLICATION CASE 1.5 Analyzing Athletic Injuries 54Prescriptive Analytics 54» APPLICATION CASE 1.6 Industrial and Commercial Bank of China(ICBC) Employs Models to Reconfigure Its Branch Network 55Analytics Applied to Different Domains 56Analytics or Data Science? 561.9 Brief Introduction to Big Data Analytics 57What Is Big Data? 57» APPLICATION CASE 1.7 Gilt Groupe's Flash Sales Streamlined by BigData Analytics 591.10 Plan ofthe Book 59Part I: Business Analytics: An Overview 59Part II: Descriptive Analytics 60Part III: Predictive Analytics 60Part IV: Prescriptive Analytics 61Part V: Big Data and Future Directions for Business Analytics 611.11 Resources, Links, and the Teradata University NetworkConnection 61Resources and Links 61Vendors, Products, and Demos 61Periodicals 61The Teradata University Network Connection 62The Book's Web Site 62Chapter Highlights 62 Key Terms 63Questions for Discussion 63 Exercises 63 END-OF-CHAPTER APPLICATION CASE Nationwide Insurance Used Blto Enhance Customer Service 64References 65Chapter 2Foundations and Technologies for Decision Making2.12.22.32.4Opening Vignette: Decision Modeling at HP UsingSpreadsheets 68Decision Making: Introduction and Definitions 70Characteristics of Decision Making 70A Working Definition of Decision Making 71Decision-Making Disciplines 71Decision Style and Decision Makers 71Phases of the Decision-Making Process 72Decision Making: The Intelligence Phase 74Problem (or Opportunity) Identification 75» APPLICATION CASE 2.1 Making Elevators Go Faster! 75Problem Classification 76Problem Decomposition 76Problem Ownership 7667

6Contents2.5Decision Making: The Design PhaseModels 77Mathematical (Quantitative) ModelsThe Benefits of Models 77Selection of a Principle of Choice777778Normative Models 79Suboptimization 79Descriptive Models 80Good Enough, or Satisficing 81Developing (Generating) Alternatives 822.62.72.8Measuring Outcomes 83Risk 83Scenarios 84Possible Scenarios 84Errors in Decision Making 84Decision Making: The Choice Phase 85Decision Making: The Implementation Phase 85How Decisions Are Supported 86Support for the Intelligence Phase 86Support for the Design Phase 87Support for the Choice Phase 88Support for the Implementation Phase2.988Decision Support Systems: Capabilities 89A DSS Application 892.10 DSS Classification 91The AIS SIGDSS Classification for DSS 91Other DSS Categories 93Custom-Made Systems Versus Ready-Made Systems 932.11 Components of Decision Support Systems 94The Data Management Subsystem 95The Model Management Subsystem 95 APPLICATION CASE 2.2 Station Casinos Wins by Building CustomerRelationships Using Its Data 96 APPLICATION CASE 2.3 SNAP DSS Helps OneNet MakeTelecommunications Rate Decisions 98The User Interface Subsystem 98The Knowledge-Based Management Subsystem 99 APPLICATION CASE 2.4 From a Game Winner to a Doctor! 100Chapter Highlights 102 Key Terms 103Questions for Discussiort 103 Exercises 104 END-OF-CHAPTER APPLICATION CASE Logistics Optimization in aMajor Shipping Company (CSAV) 104References 105

ContentsPart IIDescriptive Analytics107Chapter 3 Data Warehousing 1083.1 Opening Vignette: Isle of Capri Casinos Is Winning withEnterprise Data Warehouse 1093.2 Data Warehousing Definitions and Concepts 111What Is a Data Warehouse? 111A Historical Perspective to Data WarehousingCharacteristics of Data Warehousing111113Data Marts 114Operational Data Stores 114Enterprise Data Warehouses (EDW) 115Metadata 115 APPLICATION CASE 3.1 A Better Data Plan: Well-Established TELCOsLeverage Data Warehousing and Analytics to Stay on Top in aCompetitive Industry 1153.33.4Data Warehousing Process Overview 117 APPLICATION CASE 3.2 Data Warehousing Helps MultiCare SaveMoreLives 118Data Warehousing Architectures 120Alternative Data Warehousing Architectures 123Which Architecture Is the Best?3.5126Data Integration and the Extraction, Transformation, andLoad (ETL) Processes 127Data Integration 128 APPLICATION CASE 3.3 BP Lubricants Achieves BIGS SuccessExtraction, Transformation, and Load3.6128130Data Warehouse Development 132 APPLICATION CASE 3.4 Things Go Better with Coke's DataWarehouse 133Data Warehouse Development Approaches 133 APPLICATION CASE 3.5 Starwood Hotels & Resorts Manages HotelProfitability with Data Warehousing 136Additional Data Warehouse Development Considerations 137Representation of Data in Data Warehouse 138Analysis of Data in the Data Warehouse 139OLAP Versus OLTP 140OLAP Operations 1403.7Data Warehousing Implementation Issues 143 APPLICATION CASE 3.6 EDW Helps Connect State Agencies inMichigan 145Massive Data Warehouses and Scalability 1463.8Real-Time Data Warehousing 147 APPLICATION CASE 3.7 Egg Plc Fries the Competition in Near RealTime 148

8Contents3.9Data Warehouse Administration, Security Issues, and FutureTrends 151The Future of Data Warehousing 1533.10 Resources, Links, and the Teradata University NetworkConnection 156Resources and Links 156Gases 156Vendors, Products, and Demos157Periodicals 157Additional References 157The Teradata University Network (TUN) Connection 157Chapter Highlights 158 Key Terms 158Questions for Discussion 158 Exercises 159 END-OF-CHAPTER APPLICATION CASE Continental Airlines Flies Highwith Its Real-Time Data Warehouse 161References 162Chapter 4 Business Reporting, Visual Analytics, and BusinessPerformance Management 1654.1 Opening Vignette.Self-Service Reporting EnvironmentSaves Millions for Corporate Customers 1664.2Business Reporting Definitions and Concepts 169What Is a Business Report? 170 APPLICATION CASE 4.1 Delta Lloyd Group Ensures Accuracy andEfficiency in Financial Reporting 171Components of the Business Reporting System 173 APPLICATION CASE 4.2 Flood of Paper Ends at FEMA 1744.3 Data and Information Visualization 175 APPLICATION CASE 4.3 Tableau Saves Blastrac Thousands of Dollarswith Simplified Information Sharing 176A Brief History of Data Visualization 177 APPLICATION CASE 4.4 TIBCO Spotfire Provides Dana-Farber CancerInstitute with Unprecedented Insight into Cancer Vaccine ClinicalTrials 1794.4Different Types of Charts and GraphsBasic Charts and Graphs 180180Specialized Charts and Graphs 1814.5The Emergence of Data Visualization and VisualAnalytics 184Visual Analytics 186High-Powered Visual Analytics Environments 1884.6Performance Dashboards 190 APPLICATION CASE 4.5 Dallas Cowboys Score Big with Tableau andTeknion 191

ContentsDashboard Design 192 APPLICATION CASE 4.6 Saudi Telecom Company Excels withInformation Visualization 193What to Look For in a Dashboard 194Best Practices in Dashboard Design195Benchmark Key Performance Indicators with Industry Standards 195Wrap the Dashboard Metrics with Contextual Metadata 195Validate the Dashboard Design by a Usability Specialist 195Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard 195Enrich Dashboard with Business Users' Comments 195Present Information in Three Different Levels196Pick the Right Visual Construct Using Dashboard Design Principles 196Provide for Guided Analytics 1964.7Business Performance Management4.8Closed-Loop BPM Cycle 197 APPLICATION CASE 4.7 IBM Cognos Express Helps Mace for Fasterand Better Business Reporting 199Performance Measurement 200Key Performance Indicator (KPI)4.9201Performance Measurement SystemBalanced Scorecards 202The Four Perspectives196202203The Meaning of Balance in BSC204Dashboards Versus Scorecards 2044.10 Six Sigma as a Performance Measurement SystemThe DMAIC Performance Model205206Balanced Scorecard Versus Six Sigma 206Effective Performance Measurement 207 APPLICATION CASE 4.8 Expedia.com'*. Customer SatisfactionScorecard 208Chapter Highlights 209 Key Terms 210Questions for Discussion 211 Exercises 211» END-OF-CHAPTER APPLICATION CASE Smart Business ReportingHelps Healthcare Providers Deliver Better Care 212References 214Part III Predictive Analytics215Chapter 5 Data Mining 2165.1 Opening Vignette: Cabela's Reels in More Customers withAdvanced Analytics and Data Mining 2175.2Data Mining Concepts and Applications 219» APPLICATION CASE 5.1 Smarter Insurance: Infinity P&C ImprovesCustomer Service and Combats Fraud with Predictive Analytics 2219

5.35.45.55.65.7Definitions, Characteristics, and Benefits 222 APPLICATION CASE 5.2 Harnessing Analytics to Combat Crime:Predictive Analytics Helps Memphis Police Department Pinpoint Crimeand Focus Police Resources 226How Data Mining Works 227Data Mining Versus Statistics 230Data Mining Applications 231 APPLICATION CASE 5.3 A Mine on Terrorist Funding 233Data Mining Process 234Step 1: Business Understanding 235Step 2: Data Understanding 235Step 3: Data Preparation 236Step 4: Model Building 238 APPLICATION CASE 5.4 Data Mining in Cancer Research 240Step 5: Testing and Evaluation 241Step 6: Deployment 241Other Data Mining Standardized Processes and Methodologies 242Data Mining Methods 244Classification 244Estimating the True Accuracy of Classification Models 245Cluster Analysis for Data Mining 2 50 APPLICATION CASE 5.5 2degrees Gets a 1275 Percent Boost in ChurnIdentification 251Association Rule Mining 254Data Mining Software Tools 258 APPLICATION CASE 5.6 Data Mining Goes to Hollywood: PredictingFinancial Success of Movies 261Data Mining Privacy Issues, Myths, and Blunders 264Data Mining and Privacy Issues 264 APPLICATION CASE 5.7 Predicting Customer Buying Patterns—TheTarget Story 265Data Mining Myths and Blunders 266Chapter Highlights 267 Key Terms 268Questions for Discussion 268 Exercises 269 END-OF-CHAPTER APPLICATION CASE Macys.com Enhances ItsCustomers' Shopping Experience with Analytics 271References 271Chapter 6 Techniques for Predictive Modeling2736.1Opening Vignette: Predictive Modeling Helps BetterUnderstand and Manage Complex MedicalProcedures 2746.2Basic Concepts of Neural Networks 277Biological and Artificial Neural Networks 278 APPLICATION CASE 6.1 Neural Networks Are Helping to Save Lives inthe Mining Industry 280Elements of ANN 281