هوش تجاری: رویکرد مدیریتی

For courses on Business Intelligence or Decision Support Systems. A managerial approach to understanding business intelligence systems. To help future managers use and understand analytics, Business Intelligence provides students with a solid foundation of BI that is reinforced with hands-on practice. The second edition features updated information on data mining, text and web mining, and implementation and emerging technologies Cover......Page 1 Contents......Page 10 Preface......Page 16 Chapter 1 Introduction to Business Intelligence......Page 24 Opening Vignette: Norfolk Southern Uses Business Intelligence for Decision Support to Reach Success......Page 25 The Business Pressures–Responses–Support Model......Page 27 Definitions of BI......Page 29 A Brief History of BI......Page 30 The Architecture of BI......Page 31 The Benefits of BI......Page 33 Event-Driven Alerts......Page 36 A Cyclical Process of Intelligence Creation and Use......Page 37 1.4 Transaction Processing versus Analytic Processing......Page 38 The Typical BI User Community......Page 39 Appropriate Planning and Alignment with the Business Strategy......Page 40 Justification and Cost–Benefit Analysis......Page 41 Selected BI Vendors......Page 42 1.7 Plan of the Book......Page 43 Cases......Page 44 Chapter Highlights......Page 45 Exercises......Page 46 End of Chapter Application Case......Page 47 References......Page 48 Chapter 2 Data Warehousing......Page 50 Opening Vignette: DirecTV Thrives with Active Data Warehousing......Page 51 Characteristics of Data Warehousing......Page 53 Operational Data Stores (ODS)......Page 54 Enterprise Data Warehouses (EDWs)......Page 55 Metadata......Page 56 2.2 Data Warehousing Process Overview......Page 57 2.3 Data Warehousing Architectures......Page 59 Alternative Data Warehousing Architectures......Page 62 Which Architecture Is the Best?......Page 64 Data Integration......Page 66 Extraction, Transformation, and Load (ETL)......Page 68 2.5 Data Warehouse Development......Page 70 Data Warehouse Development Approaches......Page 73 Additional Data Warehouse Development Considerations......Page 75 Representation of Data in Data Warehouse......Page 76 Analysis of Data in Data Warehouse......Page 77 OLAP Operations......Page 78 2.6 Data Warehousing Implementation Issues......Page 81 Massive Data Warehouses and Scalability......Page 85 2.7 Real-Time Data Warehousing......Page 86 2.8 Data Warehouse Administration, Security Issues, and Future Trends......Page 91 The Future of Data Warehousing......Page 92 Periodicals......Page 94 Chapter Highlights......Page 95 Exercises......Page 96 End of Chapter Application Case......Page 98 References......Page 99 Chapter 3 Business Performance Management......Page 102 Opening Vignette: Double Down at Harrah’s......Page 103 BPM and BI Compared......Page 106 Strategic Planning......Page 108 The Strategy Gap......Page 109 Operational Planning......Page 110 Financial Planning and Budgeting......Page 111 Diagnostic Control Systems......Page 112 Pitfalls of Variance Analysis......Page 113 3.5 Act and Adjust: What Do We Need to Do Differently?......Page 116 KPIs and Operational Metrics......Page 118 Problems with Existing Performance Measurement Systems......Page 119 Effective Performance Measurement......Page 121 Balanced Scorecard (BSC)......Page 124 Six Sigma......Page 127 BPM Architecture......Page 133 Commercial BPM Suites......Page 135 BPM Market versus the BI Platform Market......Page 136 Dashboards versus Scorecards......Page 138 Dashboard Design......Page 139 Data Visualization......Page 140 Chapter Highlights......Page 143 Questions for Discussion......Page 144 Exercises......Page 145 End of Chapter Application Case......Page 146 References......Page 148 Chapter 4 Data Mining for Business Intelligence......Page 152 Opening Vignette: Data Mining Goes to Hollywood!......Page 153 4.1 Data Mining Concepts and Definitions......Page 156 Definitions, Characteristics, and Benefits......Page 158 How Data Mining Works......Page 162 4.2 Data Mining Applications......Page 166 4.3 Data Mining Process......Page 169 Step 1: Business Understanding......Page 170 Step 3: Data Preparation......Page 171 Step 4: Modeling Building......Page 173 Step 6: Deployment......Page 175 Other Data Mining Standardized Processes and Methodologies......Page 177 4.4 Data Mining Methods......Page 178 Estimating the True Accuracy of Classification Models......Page 179 Cluster Analysis for Data Mining......Page 185 Association Rule Mining......Page 187 4.5 Artificial Neural Networks for Data Mining......Page 190 Elements of ANN......Page 191 Applications of ANN......Page 193 4.6 Data Mining Software Tools......Page 195 4.7 Data Mining Myths and Blunders......Page 200 Chapter Highlights......Page 201 Key Terms......Page 202 Exercises......Page 203 End of Chapter Application Case......Page 206 References......Page 207 Chapter 5 Text and Web Mining......Page 210 Opening Vignette: Mining Text for Security and Counterterrorism......Page 211 5.1 Text Mining Concepts and Definitions......Page 213 5.2 Natural Language Processing......Page 216 Security Applications......Page 221 Biomedical Applications......Page 224 Academic Applications......Page 226 5.4 Text Mining Process......Page 227 Task 1: Establish the Corpus......Page 228 Task 2: Create the Term–Document Matrix......Page 229 Task 3: Extract the Knowledge......Page 231 Commercial Software Tools......Page 236 5.6 Web Mining Overview......Page 237 5.7 Web Content Mining and Web Structure Mining......Page 239 5.8 Web Usage Mining......Page 241 5.9 Web Mining Success Stories......Page 243 Key Terms......Page 247 Exercises......Page 248 End of Chapter Application Case......Page 249 References......Page 250 Chapter 6 Business Intelligence Implementation: Integration and Emerging Trends......Page 252 Opening Vignette: BI Eastern Mountain Sports Increases Collaboration and Productivity......Page 253 BI Implementations Factors......Page 256 Managerial Issues Related to BI Implementation......Page 257 Why Integrate?......Page 259 Embedded Intelligent Systems......Page 260 Integrating BI Applications and Back-End Systems......Page 261 Middleware......Page 262 6.4 On-Demand BI......Page 263 Key Characteristics and Benefits......Page 264 Privacy......Page 266 Ethics in Decision Making and Support......Page 268 6.7 The Web 2.0 Revolution......Page 269 Web 2.0 Companies and New Business Models......Page 270 A Definition and Basic Information......Page 271 Major Social Network Services: Facebook and Orkut......Page 272 Implications of Business and Enterprise Social Networks......Page 273 6.9 Virtual Worlds......Page 276 6.10 Social Networks and BI: Collaborative Decision Making......Page 280 Collaboration in Virtual Teams’ Decision Making......Page 281 6.11 RFID and New BI Application Opportunities......Page 283 6.12 Reality Mining......Page 287 Exercises......Page 290 References......Page 292 C......Page 296 D......Page 297 I......Page 298 O......Page 299 S......Page 300 T......Page 301 W......Page 302 B......Page 304 C......Page 305 D......Page 306 E......Page 307 I......Page 308 M......Page 309 P......Page 310 S......Page 311 T......Page 312 Z......Page 313

To help future managers use and understand analytics, Business Intelligence provides readers with a solid foundation of BI that is reinforced with hands-on practice.

Introduction to Business Intelligence; Data Warehousing; Business Performance Management; Data Mining for Business Intelligence; Text and Web Mining; Business Intelligence Implementation, Integration and Emerging Trends

For managers interested in understanding the foundations and applications of BI, knowledge management, data mining, and other intelligent systems.