Business Intelligence: A Managerial Approach

✍ Scribed by Turban, Efraim;Sharda, Ramesh;Delen, Dursun;King, David

• Publisher: [Pearson Education [distributor], Prentice Hall
• Year: 2010;2011
• Tongue: English
• Leaves: 313
• Edition: 2nd ed
• Category: Library

✦ Synopsis

A managerial approach to understanding business intelligence systems.
To help future managers use and understand analytics, "Business Intelligence" provides 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.

✦ Table of Contents

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