BUSINESS INTELLIGENCE

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1 SECOND EDITION BUSINESS INTELLIGENCE A MANAGERIAL APPROACH INTERNATIONAL EDITION Efraim Turban University of Hawaii Ramesh Sharda Oklahoma State University Dursun Deleii Oklahoma State University David King JDA Software Group, Inc. With contributions by Janine E. Aronson The University of Georgia Prentice Hall Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo

2 CONTENTS Preface 15 Chapter 1 Introduction to Business Intelligence 23 Opening Vignette: Norfolk Southern Uses Business Intelligence for Decision Support to Reach Success Changing Business Environments and Computerized Decision Support 26 The Business Pressures-Responses-Support Model, A Framework for Business Intelligence (Bl) 28 Definitions of Bl 28 A Brief History of Bl 29 The Architecture of Bl 30 Styles of Bl 32 The Benefits of BI 32 Event-Driven Alerts Intelligence Creation and Use and Bl Governance 36 A Cyclical Process of Intelligence Creation and Use 36 Intelligence and Espionage Transaction Processing versus Analytic Processing Successful Bl Implementation 38 The'Typical Bl User Community 38 Appropriate Planning and Alignment with the Business Strategy 39 Real-Time, 6n-Demand Bl Is Attainable 40 Developing or Acquiring Bl Systems 40 Justification and Cost-Benefit Analysis 40 Security and Protection of Privacy 41 Integration of Systems and Applications Major Tools and Techniques of Business Intelligence 41 The Tools and Techniques 41 Selected Bl Vendors Plan of the Book Resources, Links, and theteradata University Network Connection 43 Resources and Links 43 Cases 43 Vendors, Products, and Demos 44 Periodicals 44 The Teradata University Network Connection 44 The Book's Web Site 44

3 10 Contents Chapter Highlights 44 Key Terms 45 Questions for Discussion 45 Exercises 45 End of Chapter Application Case 46 References 47 Chapter 2 Data Warehousing 49 Opening Vignette: DirecTV Thrives with Active Data Warehousing Data Warehousing Definitions and Concepts 52 What Is a Data Warehouse? 52 Characteristics of Data Warehousing 52 Data Marts 53 Operational Data Stores (ODS) 53 Enterprise Data Warehouses (EDWs) 54 Metadata Data Warehousing Process Overview Data Warehousing Architectures 58 Alternative Data Warehousing Architectures 61 Which Architecture Is the Best? Data Integration and the Extraction, Transformation, and Load (ETL) Processes 65 Data Integration 65. Extraction, Transformation, and Load (ETL) Data Warehouse Development 69 Data Warehouse Vendors 72 Data Warehouse Development Approaches 72 Additional Data Warehouse Development Considerations. 74 Representation of Data in Data Warehouse 75 Analysis of Data in Data Warehouse 76 OLAP versus OLTP 77 OLAP Operations Data Warehousing Implementation Issues 80 Massive Data Warehouses and Scalability Real-Time Data Warehousing Data Warehouse Administration, Security Issues, and Future Trends 90 The Future of Data Warehousing Resources, Links, and the Teradata University Network Connection 93 Resources and Links 93 Cases 93 Vendors, Products, and Demos 93

4 Contents 11 Periodicals 93 Additional References 94 The Teradata University Network (TUN) Connection 94 Chapter Highlights 94 Key Terms 95 Questions for Discussion 95 Exercises 95 End of Chapter Application Case 97 References 98 Chapter 3 Business Performance Management 101 Opening Vignette: Double Down at Harrah's Business Performance Management (BPM) Overview 105 BPM Defined 105 BPM and Bl Compared Strategize: Where Do We Want to Go? 107 * Strategic Planning 107 The Strategy Gap Plan: How Do We Get There? 109 Operational Planning 109 Financial Planning and Budgeting Monitor: How Are We Doing? 111. Diagnostic Control Systems 111 Pitfalls of Variance Analysis Act and Adjust: What Do We Need to Do Differently? Performance Measurement 117 KPIs and Operational Metrics 117 Problems with Existing Performance Measurement Systems 118 Effective Performance Measurement BPM Methodologies 123 Balanced Scorecard (BSC) 123 Six Sigma BPM Technologies and Applications 132 BPM Architecture 132 Commercial BPM Suites 134 BPM Market versus the Bl Platform Market Performance Dashboards and Scorecards 137 Dashboards versus Scorecards 137 Dashboard Design 138 What to Look for in a Dashboard 139 Data Visualization 139 Chapter Highlights 142 Key Terms 143

5 12 Contents Questions for Discussion 143 Exercises 144 End of Chapter Application Case 145 References 147 Chapter 4 Data Mining for Business Intelligence 151 Opening Vignette: Data Mining Goes to Hollywood! Data Mining Concepts and Definitions 155 Definitions, Characteristics, and Benefits 157 How Data Mining Works Data Mining Applications Data Mining Process 168 Step 1: Business Understanding 169 Step 2: Data Understanding 170 Step 3: Data Preparation 170 Step 4: Modeling Building 172 Step 5: Testing and Evaluation 174 Step 6: Deployment 174 Other Data Mining Standardized Processes and Methodologies Data Mining Methods 177 Classification 178 Estimating the True Accuracy of Classification Models 178 Cluster Analysis for Data Mining 184 Association Rule Mining Artificial Neural Networks for Data Mining 189 Elements of ANN 190 Applications of ANN Data Mining Software Tools Data Mining Myths and Blunders 199 Chapter Highlights 200 Key Terms 201 Questions for Discussion 202 Exercises 202 End of Chapter Application Case 205 References 206 Chapter 5 Text and Web Mining 209 Opening Vignette: Mining Text for Security and Counterterrorism Text Mining Concepts and Definitions Natural Language Processing Text Mining Applications 220 Marketing Applications 220 Security Applications 220 Biomedical Applications 223 Academic Applications 225

6 Contents Text Mining Process 226 Task 1: Establish the Corpus 227 Task 2: Create the Term-Document Matrix 228 Task 3: Extract the Knowledge Text Mining Tools 235 Commercial Software Tools 235 Free Software Tools Web Mining Overview Web Content Mining and Web Structure Mining Web Usage Mining Web Mining Success Stories 242 Chapter Highlights 246 Key Terms 246 Questions for Discussion 247 Exercises 247 End of Chapter Application Case 248 References Chapter 6 Business Intelligence Implementation: Integration and Emerging Trends 251 Opening Vignette: Bl Eastern Mountain Sports Increases Collaboration and Productivity Implementing Bl: An Overview 255 Bl Implementations Factors 255 Managerial Issues Related to Bl Implementation Bl and Integration Implementation 258 Types of Integration 258 Why Integrate? 258 Levels of Bl Integration 259 Embedded Intelligent Systems Connecting Bl Systems to Databases and Other Enterprise Systems 260 Connecting to Databases 260 Integrating Bl Applications and Back-End Systems 260 Middleware On-Demand Bl 262 The Limitations of Traditional Bl 263 The On-demand Alternative Issues of Legality, Privacy, and Ethics 265 Legal Issues 265 Privacy 265 Ethics in Decision Making and Support Emerging Topics in Bl: An Overview 268 The Future of Business Intelligence 268

7 14 Contents Glossary 295 Index The Web 2.0 Revolution 268 Representative Characteristics of Web Web 2.0 Companies and New Business Models Online Social Networking: Basics and Examples 270 A Definition and Basic Information 270 Mobile Social Networking 271 Major Social Network Services: Facebook and Orkut 271 Implications of Business and Enterprise Social Networks Virtual Worlds Social Networks and Bl: Collaborative Decision Making 279 The Rise of Collaborative Decision Making 280 Collaboration in Virtual Teams' Decision Making RFID and New Bl Application Opportunities Reality Mining 286 Key Terms 289 Questions for Discussion 289 Exercises 289 End of Chapter Application Case 291 References 291

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