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THIRD EDITIONBUSINESS INTELLIGENCEA MANAGERIAL PERSPECTIVE ON ANALYTICSRamesh ShardaOklahoma State UniversityDursun DelenOklahoma State UniversityEfraim TurbanUniversity of HawaiiWith contributions byJ. E. AronsonThe University of GeorgiaTing-Peng LiangNational Sun Yat-sen UniversityDavid KingJDA Software Group, Inc.Global Edition contributions byLoveleen GaurAmity University, NoidaPEARSONBoston 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

CONTENTSPreface17Aböut the Authors 23Charter 1 An Overview of Business Intelligence,Analytics, and Decision Support 271.1 Opening Vignette: Magpie Sensing Employs Analytics to Managea Vaccine Supply Chain Effectively and Safely 281.2 Changing Business Environments and ComputerizedDecision Support 30The Business Pressures-Responses-Support Model 301.3 A Framework for Business Intelligence (Bl) 32DefmitionsofBl 32A Brief History of Bl 33TheArchitectureofBl 34The Origins and Drivers of Bl 34 APPLICATION CASE 1.1 Sabre Helps Its Clients Through Dashboardsand Analytics 35A Multimedia Exercise in Business Intelligence 361.4 Intelligence Creation, Use, and Bl Governance 37A Cydical Process of Intelligence Creation and Use 37Intelligence and Espionage 381.5 Transaction Processing Versus Analytic Processing 391.6 Successful Bl ImplementationThe Typical Bl User Community4040Appropriate Flanning and Alignment with the Business Strategy 40Real-Time, On-Demand Bl Is Attainable 41Developing or Acquiring Bl Systems 42Justification and Cost-Benefit Analysis 42Security and Protection of Privacy 42Integration of Systems and Applications 421.7 Analytics Overview 43Descriptive Analytics 44Predidive Analytics 44» APPLICATION CASE 1.2 Eliminating Inefficiencies at SeattleChildren's Hospital 45» APPLICATION CASE 1.3 Analysis at the Speed of Thought 46Prescriptive Analytics 46» APPLICATION CASE 1.4 Moneyball: Analytics in Sportsand Movies 47 APPLICATION CASE 1.5 Analyzing Athletic Injuries 48Analytics Applied to Different Domains 48

» APPLICATION CASE 1.6 Industrial and Commercial Bank of China(ICBC) Employs Models to Reconfigure Its Branch Network 49Analytics or Data Science? 501.8 Brief Introduction to Big Data Analytics 51What Is Big Data? 51 APPLICATION CASE 1.7 Gilt Groupe's Flash Sales Streamlined byBig Data Analytics 521.9 Plan ofthe Book 531.10 Resources, Links, and the Teradata University NetworkConnection 55Resources and Links 55Vendors, Products, and Demos 55Periodicals 55The Teradata University Network Connection 55The Book's Web Site 55Key Terms 56Questions for Discussion 56Exercises 56End-of-Chapter Application Oase 57References 59Chapter 2 Data Warehousing612.1 Opening Vignette: Isle of Capri Casinos Is Winning withEnterprise Data Warehouse 622.2 Data Warehousing Definitions and Concepts 64What Is a Data Warehouse? 64A Historical Perspective to Data Warehousing 64Characteristics of Data Warehousing 66Data Marts 67Operational Data Stores 67Enterprise Data Warehouses (EDW) 68 APPLICATION CASE 2.1 A Better Data Plan: Well-Established TELCOsLeverage Data Warehousing and Analytics to Stay on Top in aCompetitive Industry 68Metadata 692.3 Data Warehousing Process Overview 70 APPLICATION CASE 2.2 Data Warehousing Helps MultiCare SaveMore Lives 712.4 Data Warehousing Architectures 73Alternative Data Warehousing Architectures 76Which Architecture Is the Best? 792.5 Data Integration and the Extraction, Transformation, and Load(ETL) Processes 80Data Integration 81 APPLICATION CASE 2.3 BP Lubricants Achieves BIGS Success 81Extraction, Transformation, and Load 83

2.6 Data Warehouse Development 85 APPLICATION CASE 2.4 Things Go Better with Coke'sData Warehouse 86Data Warehouse Development Approaches 88 APPLICATION CASE 2.5 Starwood Hotels & Resorts Manages HotelProfitability with Data Warehousing 89Additional Data Warehouse Development Considerations 91Representation of Data in Data Warehouse 92Analysis of Data in Data Warehouse 93OLAP Versus OLTP 93OLAP Operations 942.7 Data Warehousing Implementation Issues 97 APPLICATION CASE 2.6 EDW Helps Connect State Agencies inMichigan 99Massive Data Warehouses and Scalability 1002.8 Real-Time Data Warehousing 101 APPLICATION CASE 2.7 Egg Plc Fries the Competitionin Near Real Time 1022.9 Data Warehouse Administration, Security Issues,and Future Trends 105The Future of Data Warehousing 1072.10 Resources, Links, and the Teradata University NetworkConnection 110Resources and Links 110Cases 110Vendors, Products, and Demos 111Periodicals 111Additional References 111The Teradata University Network (TUN) ConnectionKey Terms 112Questions for Discussion 112Exercises 113End-of-Chapter Application Case 114References 116111Chapter 3 Business Reporting, Visual Analytics, and BusinessPerformance Management 1193.1 Opening Vignette: Self-Service Reporting Environment SavesMillions for Corporate Customers 1203.2 Business Reporting Definitions and Concepts 123What Is a Business Report? 124 APPLICATION CASE 3.1 Delta Lloyd Group Ensures Accuracyand Efficiency in Financial Reporting 126Components of Business Reporting Systems 12 7 APPLICATION CASE 3.2 Flood of PaperEnds at FEMA 128

3.3 Data and Information Visualization 129 APPLICATION CASE 3.3 Tableau Saves Blastrac Thousands of Dollarswith Simplified Information Sharing 130A Brief History of Data Visualization 131 APPLICATION CASE 3.4 TIBCO Spotfire Provides Dana-Farber CancerInstitute with Unprecedented Insight into Cancer Vaccine ClinicalTrials 1333.4 DifferentTypesof ChartsandGraphs 134Basic Charts and Graphs 134Specialized Charts and Graphs 1353.5 The Emergence of Data Visualization and Visual Analytics 138Visual Analytics 140High-Powered Visual Analytics Environments 1403.6 Performance Dashboards 143Dashboard Design 145 APPLICATION CASE 3.5 Dallas Cowboys Score Big with Tableauand Teknion 145 APPLICATION CASE 3.6 Saudi Telecom Company fxcels withInformation Visualization 146What to Look For in a Dashboard 148Best Practices in Dashboard Design 148Benchmark Key Performance Indicators with Indjustry Standards 149Wrap the Dashboard Metrics with Contextual Metadata 149Validate the Dashboard Design byaUsability Specialist 149Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard 149Enrich Dashboard with Business-User Comments 149Present Information in Three Different Levels 149Pick the Right Visual Construct Using Dashboard Design Prindples 150Provide for Guided Analytics 1503.7 Business Performance Management 150Closed-Loop BPM Cyde 150 APPLICATION CASE 3.7 IBM Cognos Express Helps Mace for Fasterand Better Business Reporting 1533.8 Performance Measurement 154Key Performance Indicator (KPI) 154Performance Measurement System 1553.9 Balanced Scorecards 156The Four Perspectives 156The Meaning of Balance in BSC 158Dashboards Versus Scorecards 1593.10 SixSigmaasa Performance Measurement System 159The DMAIC Performance Model 160Balanced Scorecard Versus Six Sigma 160Effective Performance Measurement 160

Contents APPLICATION CASE 3.8 Expedia.com's Customer SatisfactionScorecard 162Key Terms 164Questions for Discussion 165Exercises 165End-of-Chapter Application Case 166References 168Chapter 4 Data Mining 1694.1 Opening Vignette: Cabela's Reeis in More Customers withAdvanced Analytics and Data Mining 1704.2 Data Mining Concepts and Applications 172Definition;, Characteristics, and Benefits, 173 APPLICATION CASE 4.1 Smarter Insurance: Infinity P&C ImprovesCustomer Service and Combats Fraud with PredictiveAnalytics 174How Data Mining Works 179 APPLICATION CASE 4.2 Harnessing Analytics to Combat Crime:Predictive Analytics Helps Memphis Police Department PinpointCrime and Focus Police Resources 179Data Mining Versus Statistics 1834.3 Data Mining Applications 183 APPLICATION CASE 4.3 A Mine on Terrorist Funding 1864.4 Data Mining Process 187Step 1: Business Understanding 187Step 2: Data Understanding 188Step 3: Data Preparation 188Step 4: Model Building 190Step 5: Testing and Evaluation 192Step 6: Deployment 192» APPLICATION CASE 4.4 Data Mining in Cancer Research 193Other Data Mining Standardized Processes and Methodologies 1944.5 Data Mining Methods 196Classification 196Estimating the True Accuracy of Classification Models 197» APPLICATION CASE 4.5 2degrees Gets a 1275 Percent Boost inChurn Identification 203Cluster Analysis for Data Mining 204Association Rule Mining 2064.6 Data Mining Software Tools 210 APPLICATION CASE 4.6 Data Mining Goes to Hollywood: PredictingFinancial Success of Movies 2134.7 Data Mining Privacy Issues, Myths, and Blunders 216Data Mining and Privacy Issues 216» APPLICATION CASE 4.7 Predicting Customer Buying Patterns—The Target Story 21711

Data Mining Myths and Blunders 218Key Terms 220Questions for Discussion 220Exercises 221End-of-Chapter Application Case 223References 223Chapter 5 Text and Web Analytics 2255.1 Opening Vignette: Machine Versus Men on Jeopardy!: The StoryofWatson 2265.2 Text Analytics and Text Mining Overview 229 APPLICATION CASE 5.1 Text Mining for Patent Analysis 2325.3 Natural Language Processing 233 APPLICATION CASE 5.2 Text Mining Improves Hong KongGovernment's Ability to Anticipate and Address PublicComplaints 2355.4 Text Mining Applications 237Marketing Applications 238Securtty Applications 238 APPLICATION CASE 5.3 Mining for Lies 239Biomedical Applications 241Academic Applications 242 APPLICATION CASE 5.4 Text mining and Sentiment Analysis HelpImprove Customer Service Performance 2435.5 Text Mining Process 244Task 1: Establish the Corpus 245Task 2: Create the Term-Document Matrix 246Task 3: Extract the Knowledge 248 APPLICATION CASE 5.5 Research Literature Survey with TextMining 2505.6 Sentiment Analysis 253 APPLICATION CASE 5.6 Whirlpool Achieves Customer Loyalty andProduct Success with Text Analytics 255Sentiment Analysis Applications 256Sentiment Analysis Process 258Methods for Polarity Identification 259Using a Lexicon 260Using a Collection of Training Documents 261IdentKying Semantic Orientation of Sentences and Phrases 261Identifying Semantic Orientation of Document 2615.7 Web Mining Overview 262Wel Content and Web Structure Mining 2645.8 Search Engines 267Ahatpmy of a Search Engine 267Development Cyde 267

ContentsWeb Crawler 268Document Indexer 268Response Cycle 269Query Analyzer 269Document Matcher/Ranker 269Search Engine Optimization 270Methods for Search Engine Optimization 271» APPLICATION CASE 5.7 Understanding Why Customers AbandonShopping Carts Results in a 10 Million Sales Increase 2725.9 Web Usage Mining (Web Analytics) 274Web Analytics Technologies 274» APPLICATION CASE 5.8 Allegro Boosts Online Click-Through Ratesby 500 Percent with Web Analysis 275Web Analytics Metrics 277Web Site Usability 277Traffic Sources 278Visitor Profiles 229Conversion Statistics 2805.10 Social Analytics 281Social Network Analysis 282Social Network Analysis Metrics 283» APPLICATION CASE 5.9 Social Network Analysis HelpsTelecommunication Firms 283Connections 284Distributions 285Segmerrtatton 285Social Media Analytics 285How Do People Use Social Media? 286 APPLICATION CASE 5.10 Measuring the Impact of Social Media atLoMapaWa 287Measuring the Soaal Media Impact 288Best Practices in Social Media Analytics 289 APPLICATION CASE 5.11 eHarmony Uses Social Media to Help Takethe Mystery Out of Online Dating 290Key Terms 293Questions for Discussion 293Exercises 293End-of-Chapter Application Case 294References 296Chapter 6 Big Data and Analytics 2996.1 Opening Vignette: Big Data Meets Big Science at CERN 3006.2 Definition of Big Data 303The Vs That Define Big Data 304» APPUCATION CASE 6.1 BigData Analytics Helps LuxotticaImprovement its Marketing Effectiveness 307;/13