Business intelligence : the savvy manager's guide, getting onboard with emerging IT

목차 (Table of Contents)

• CONTENTS
• Foreward = xi
• Preface = xiii
• CHAPTER 1 Business Intelligence and Information Exploitation = 1
• Why Business Intelligence? = 2
• The Information Asset = 3
• Exploiting Information = 4
• Business Intelligence and Program Success = 5
• What Is Business Intelligence? = 6
• Actionable Knowledge = 8
• Selected Readings = 8
• CHAPTER 2 The Value of Business Intelligence = 11
• The Information Asset and Data Valuation = 12
• Actionable Knowledge-Return on Investment = 16
• Business Intelligence Applications = 17
• The Intelligence Dashboard = 24
• Business Intelligence Adds Value = 25
• CHAPTER 3 Planning for Success = 27
• Initiating a Program = 28
• Business／Information Technology Partnership = 31
• Business Intelligence Success Factors = 33
• Team Building = 38
• Strategic versus Tactical Planning = 40
• Summary = 42
• CHAPTER 4 The Business Intelligence Environment = 45
• The Business Case = 47
• The Business Intelligence Process = 47
• System Infrastructure = 49
• Information Access, Delivery, and Analysis = 52
• Services = 55
• Management Issues = 57
• Seek to Learn More = 58
• CHAPTER 5 Business Models and Information Flow = 59
• The Business Case = 60
• Information Processing and Information Flow = 63
• The Information Flow Model = 66
• Usage in Practice = 68
• Modeling Frameworks = 70
• Management Issues = 72
• Learning More = 73
• CHAPTER 6 Data Warehouses, Online Analytical Processing, and Metadata = 75
• The Business Case = 75
• Data Models = 76
• The Data Warehouse = 82
• The Data Mart = 82
• Online Analytical Processing = 83
• Metadata = 84
• Management Issues = 88
• Summary = 89
• CHAPTER 7 Business Rules = 91
• The Business Case = 92
• The Business Rules Approach = 95
• What Is a Business Rule? = 96
• What Is a Business Rule System? = 100
• Sources of Business Rules = 103
• Management Issues = 106
• To Learn More = 107
• CHAPTER 8 Data Profiling = 109
• The Business Case = 109
• Data Profiling Activities = 110
• Data Model Inference = 111
• Attribute Analysis = 113
• Relationship Analysis = 120
• Management Issues = 125
• The Last Word = 125
• CHAPTER 9 Data Quality and Information Compliance = 127
• The Business Case = 128
• More Than Just Names and Addresses = 128
• Types of Errors = 131
• Data Cleansing = 135
• Business Rule-Based Information Compliance = 140
• Management Issues = 143
• To Learn More = 144
• CHAPTER 10 Information Intergration = 145
• The Business Case = 145
• ETL : Extract, Transform, Load = 146
• Enterprise Application Intergration and Web Services = 150
• Record Linkage and Consolidation = 152
• Management Issues = 158
• To Learn More = 160
• CHAPTER 11 The Value of Parallelism = 161
• The Business Case = 162
• Parallelism and Granularity = 163
• Parallel Processing Systems = 166
• Dependence = 169
• Parallelism and Business Intelligence = 171
• Management Issues = 173
• To Learn More = 174
• CHAPTER 12 Alternate Information Contexts = 175
• The Business Case = 175
• Psychographics and Demographics = 176
• Geographic Data = 177
• Web Behavior Intelligence = 182
• Management Issues = 184
• To Learn More = 185
• CHAPTER 13 Data Enhancement = 187
• The Business Case = 188
• Types of Data Enhancement = 190
• Incremental Enhancements = 192
• Batch Enhancements = 193
• Standardization = 194
• Example : Address Standardization = 197
• Enhancement Methodologies = 199
• Management Issues = 203
• To Learn More = 203
• CHAPTER 14 Knowledge Discovery and Data Mining = 205
• The Business Case = 206
• Data Mining and the Data Warehouse = 206
• The Virtuous Cycle = 207
• Directed versus Undirected Knowledge Discovery = 208
• Six Basic Tasks of Data Mining = 209
• Data Mining Techniques = 211
• Management Issues = 216
• To Learn More = 218
• CHAPTER 15 Using Publicly Available Data = 219
• The Business Case = 220
• Management Issues = 220
• Public Data = 221
• Data Resources = 225
• Semistructured Data = 226
• The Myth of Privacy = 227
• To Learn More = 231
• CHAPTER 16 Quick Reference Guide = 233
• Business Analytics = 233
• Business Intelligence = 234
• Business Rules = 235
• Data Cleansing = 236
• Data Enhancement = 238
• Data Integration = 239
• Data Mart = 240
• Data Mining = 240
• Data Model = 241
• Data Profiling = 242
• Data Quality = 243
• Data Warehouse = 244
• Dimensional Modeling = 245
• Extract／Transform／Load = 246
• Geographical Information System = 247
• Metadata = 248
• Online Analytical Processing = 249
• Parallelism = 250
• Query and Reporting = 251
• Index = 253