Cover......Page 1
Title Page......Page 4
Contents......Page 8
Preface ......Page 18
About the Authors......Page 24
Chapter 1 An Overview of Business......Page 28
1.1 Opening Vignette: Magpie Sensing......Page 29
The Business PressuresโResponsesโSupport Model......Page 31
Definitions of BI......Page 33
A Brief History of BI......Page 34
The Origins and Drivers of BI......Page 35
Application Case 1.1 Sabre Helps Its Clients......Page 36
A Multimedia Exercise in Business Intelligence......Page 37
A Cyclical Process of Intelligence Creation and Use......Page 38
Intelligence and Espionage......Page 39
1.5 Transaction Processing Versus Analytic Processing......Page 40
Appropriate Planning and Alignment with the Business......Page 41
Real-Time, On-Demand BI Is Attainable......Page 42
Integration of Systems and Applications......Page 43
1.7 Analytics Overview......Page 44
Predictive Analytics......Page 45
Application Case 1.2 Eliminating Inefficiencies......Page 46
Prescriptive Analytics......Page 47
Application Case 1.4 Moneyball: Analytics in Sports......Page 48
Analytics Applied to Different Domains......Page 49
Application Case 1.6 Industrial and Commercial Bank of China......Page 50
Analytics or Data Science?......Page 51
What Is Big Data?......Page 52
Application Case 1.7 Gilt Groupeโs Flash Sales......Page 53
1.9 Plan of the Book......Page 54
The Bookโs Web Site......Page 56
Exercises......Page 57
End-of-Chapter Application Case......Page 58
References......Page 60
Chapter 2 Data Warehousing......Page 62
2.1 Opening Vignette: Isle of Capri Casinos......Page 63
A Historical Perspective to Data Warehousing......Page 65
Characteristics of Data Warehousing......Page 67
Operational Data Stores......Page 68
Application Case 2.1 A Better Data Plan: Well-Established......Page 69
Metadata......Page 70
2.3 Data Warehousing Process Overview......Page 71
Application Case 2.2 Data Warehousing Helps......Page 72
2.4 Data Warehousing Architectures......Page 74
Alternative Data Warehousing Architectures......Page 77
Which Architecture Is the Best?......Page 80
2.5 Data Integration and the Extraction, Transformation......Page 81
Application Case 2.3 BP Lubricants Achieves BIGS Success......Page 82
Extraction, Transformation, and Load......Page 84
2.6 Data Warehouse Development......Page 86
Application Case 2.4 Things Go Better with Cokeโs......Page 87
Data Warehouse Development Approaches......Page 89
Application Case 2.5 Starwood Hotels & Resorts Manages......Page 90
Additional Data Warehouse Development Considerations......Page 92
Representation of Data in Data Warehouse......Page 93
OLAP Versus OLTP......Page 94
OLAP Operations......Page 95
2.7 Data Warehousing Implementation Issues......Page 98
Application Case 2.6 EDW Helps Connect State......Page 100
Massive Data Warehouses and Scalability......Page 101
2.8 Real-Time Data Warehousing......Page 102
Application Case 2.7 Egg Plc Fries the Competition......Page 103
2.9 Data Warehouse Administration, Security......Page 106
The Future of Data Warehousing......Page 108
Cases......Page 111
The Teradata University Network (TUN) Connection......Page 112
Questions for Discussion......Page 113
Exercises......Page 114
End-of-Chapter Application Case......Page 115
References......Page 117
Chapter 3 Business Reporting, Visual Analytics, and Business......Page 120
3.1 Opening Vignette: Self-Service Reporting......Page 121
3.2 Business Reporting Definitions and Concepts......Page 124
What Is a Business Report?......Page 125
Application Case 3.1 Delta Lloyd Group Ensures Accuracy......Page 127
Components of Business Reporting Systems......Page 128
Application Case 3.2 Flood of Paper......Page 129
3.3 Data and Information Visualization......Page 130
Application Case 3.3 Tableau Saves Blastrac......Page 131
A Brief History of Data Visualization......Page 132
Application Case 3.4 TIBCO Spotfire Provides......Page 134
Basic Charts and Graphs......Page 135
Specialized Charts and Graphs......Page 136
3.5 The Emergence of Data Visualization and Visual Analytics......Page 139
High-Powered Visual Analytics Environments......Page 141
3.6 Performance Dashboards......Page 144
Application Case 3.5 Dallas Cowboys Score Big......Page 146
Application Case 3.6 Saudi Telecom Company Excels......Page 147
Best Practices in Dashboard Design......Page 149
Present Information in Three Different Levels......Page 150
Closed-Loop BPM Cycle......Page 151
Application Case 3.7 IBM Cognos Express Helps......Page 154
Key Performance Indicator (KPI)......Page 155
Performance Measurement System......Page 156
The Four Perspectives......Page 157
The Meaning of Balance in BSC......Page 159
3.10 Six Sigma as a Performance Measurement System......Page 160
Effective Performance Measurement......Page 161
Application Case 3.8 Expedia.comโs Customer Satisfaction......Page 163
Key Terms......Page 165
Exercises......Page 166
End-of-Chapter Application Case......Page 167
References......Page 169
Chapter 4 Data Mining......Page 170
4.1 Opening Vignette: Cabelaโs Reels in More......Page 171
4.2 Data Mining Concepts and Applications......Page 173
Definitions, Characteristics, and Benefits......Page 174
Application Case 4.1 Smarter Insurance: Infinity......Page 175
Application Case 4.2 Harnessing Analytics to Combat......Page 180
4.3 Data Mining Applications......Page 184
Application Case 4.3 A Mine on Terrorist Funding......Page 187
Step 1: Business Understanding......Page 188
Step 3: Data Preparation......Page 189
Step 4: Model Building......Page 191
Step 6: Deployment......Page 193
Application Case 4.4 Data Mining in Cancer Research......Page 194
Other Data Mining Standardized Processes......Page 195
Classification......Page 197
Estimating the True Accuracy of Classification Models......Page 198
Application Case 4.5 2degrees Gets a 1275 Percent......Page 204
Cluster Analysis for Data Mining......Page 205
Association Rule Mining......Page 207
4.6 Data Mining Software Tools......Page 211
Application Case 4.6 Data Mining Goes to Hollywood:......Page 214
Data Mining and Privacy Issues......Page 217
Application Case 4.7 Predicting Customer Buying......Page 218
Data Mining Myths and Blunders......Page 219
Questions for Discussion......Page 221
Exercises......Page 222
References......Page 224
Chapter 5 Text and Web Analytics......Page 226
5.1 Opening Vignette: Machine Versus Men......Page 227
5.2 Text Analytics and Text Mining Overview......Page 230
Application Case 5.1 Text Mining for Patent Analysis......Page 233
5.3 Natural Language Processing......Page 234
Application Case 5.2 Text Mining Improves Hong......Page 236
5.4 Text Mining Applications......Page 238
Security Applications......Page 239
Application Case 5.3 Mining for Lies......Page 240
Biomedical Applications......Page 242
Academic Applications......Page 243
Application Case 5.4 Text mining and Sentiment......Page 244
5.5 Text Mining Process......Page 245
Task 1: Establish the Corpus......Page 246
Task 2: Create the TermโDocument Matrix......Page 247
Task 3: Extract the Knowledge......Page 249
Application Case 5.5 Research Literature Survey......Page 251
5.6 Sentiment Analysis......Page 254
Application Case 5.6 Whirlpool Achieves Customer......Page 256
Sentiment Analysis Applications......Page 257
Sentiment Analysis Process......Page 259
Methods for Polarity Identification......Page 260
Using a Lexicon......Page 261
Identifying Semantic Orientation of Document......Page 262
5.7 Web Mining Overview......Page 263
Web Content and Web Structure Mining......Page 265
Development Cycle......Page 268
Document Indexer......Page 269
Document Matcher/Ranker......Page 270
Search Engine Optimization......Page 271
Methods for Search Engine Optimization......Page 272
Application Case 5.7 Understanding Why Customers......Page 273
Web Analytics Technologies......Page 275
Application Case 5.8 Allegro Boosts Online Click-Through......Page 276
Web Site Usability......Page 278
Traffic Sources......Page 279
Visitor Profiles......Page 280
Conversion Statistics......Page 281
5.10 Social Analytics......Page 282
Social Network Analysis......Page 283
Application Case 5.9 Social Network Analysis Helps......Page 284
Connections......Page 285
Social Media Analytics......Page 286
How Do People Use Social Media?......Page 287
Application Case 5.10 Measuring the Impact of Social......Page 288
Measuring the Social Media Impact......Page 289
Best Practices in Social Media Analytics......Page 290
Application Case 5.11 eHarmony Uses Social......Page 291
Exercises......Page 294
End-of-Chapter Application Case......Page 295
References......Page 297
Chapter 6 Big Data and Analytics......Page 300
6.1 Opening Vignette: Big Data Meets Big Science at CERN......Page 301
6.2 Definition of Big Data......Page 304
The Vs That Define Big Data......Page 305
Application Case 6.1 BigData Analytics Helps......Page 308
6.3 Fundamentals of Big Data Analytics......Page 309
Business Problems Addressed by Big Data Analytics......Page 312
Application Case 6.2 Top 5 Investment Bank Achieves......Page 313
MapReduce......Page 314
How Does Hadoop Work?......Page 316
Hadoop Technical Components......Page 317
Hadoop: The Pros and Cons......Page 318
NoSQL......Page 320
Application Case 6.3 eBayโs Big Data Solution......Page 321
6.5 Data Scientist......Page 322
Where Do Data Scientists Come From?......Page 323
Application Case 6.4 Big Data and Analytics in Politics......Page 326
6.6 Big Data and Data Warehousing......Page 327
Use Cases for Hadoop......Page 328
Use Cases for Data Warehousing......Page 329
Coexistence of Hadoop and Data Warehouse......Page 330
6.7 Big Data Vendors......Page 332
Application Case 6.5 Dublin City Council Is Leveraging......Page 334
Application Case 6.6 Creditreform Boosts......Page 338
6.8 Big Data And Stream Analytics......Page 339
Stream Analytics Versus Perpetual Analytics......Page 340
Critical Event Processing......Page 341
e-Commerce......Page 342
Application Case 6.7......Page 343
Health Sciences......Page 345
Government......Page 346
Exercises......Page 347
End-of-Chapter Application Case......Page 348
References......Page 349
Chapter 7 Business Analytics: Emerging Trends and Future Impacts......Page 352
7.1 Opening Vignette: Oklahoma Gas and Electric......Page 353
Geospatial Analytics......Page 354
Application Case 7.1 Great Clips Employs Spatial......Page 356
Real-Time Location Intelligence......Page 358
Application Case 7.2 Quiznos Targets Customers......Page 359
7.3 Analytics Applications for Consumers......Page 360
Application Case 7.3 A Life Coach in Your Pocket......Page 361
7.4 Recommendation Engines......Page 363
7.5 The Web 2.0 Revolution and Online Social Networking......Page 364
Social Networking......Page 365
Implications of Business and Enterprise Social......Page 366
7.6 Cloud Computing and BI......Page 367
Service-Oriented DSS......Page 368
Data-as-a-Service (DaaS)......Page 370
Information-as-a-Service (Information on Demand)......Page 371
Analytics-as-a-Service (AaaS)]......Page 372
7.7 Impacts of Analytics In Organizations: An Overview......Page 373
Restructuring Business Processes and Virtual Teams......Page 374
Analyticsโ Impact on Managersโ Activities and Their Performance......Page 375
Privacy......Page 377
Recent Technology Issues in Privacy and Analytics......Page 379
7.9 An Overview of the Analytics Ecosystem......Page 380
Data Infrastructure Providers......Page 381
Middleware/BI Platform Industry......Page 382
Predictive Analytics......Page 383
Application Developers or System Integrators:......Page 384
Analytics Industry Analysts and Influencers......Page 386
Academic Providers and Certification Agencies......Page 388
Exercises......Page 390
End-of-Chapter Application Case......Page 391
References......Page 392
Glossary......Page 394
B......Page 402
C......Page 403
D......Page 404
E......Page 405
I......Page 406
N......Page 407
P......Page 408
S......Page 409
T......Page 410
Z......Page 411
