1. Briefly describe five techniques (or algorithms) that are used for classification modeling.2. How

  

1. Briefly describe five techniques (or algorithms) that are used for classification modeling.2. How spreadsheets can be used for analytical modeling and solution? 3. How can we use what-if analysis? 4. How learning happens in ANN? 5. What is the so-called “black-box” syndrome? 7. How does sensitivity analysis work? 8. Compared to ANN, what are the advantages of SVM? 9. What are the common applications of ANN? 10. What are the critical success factors for a kNN implementation? 11. What are the reasons for the recent emergence of visual analytics?
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BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
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Where those designations appear in this book, and the publisher was aware of a trademark claim, the
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Library of Congress Cataloging-in-Publication Data
Turban, Efraim.
[Decision support and expert system,)
Business intelligence and analytics: systems for decision support/Ramesh Sharda, Oklahoma State University,
Dursun Delen, Oklahoma State University, Efraim Turban, University of Hawaii; With contributions
by J. E. Aronson, The University of Georgia, Ting-Peng Liang, National Sun Yat-sen University,
David King, JOA Software Group, Inc.-Tenth edition.
pages cm
ISBN-13: 978-0-13-305090-5
ISBN-10: 0-13-305090-4
1. Management-Data processing. 2. Decision support systems. 3. Expert systems (Compute r science)
4. Business intelligence. I. Title.
HD30.2.T87 2014
658.4’03801 l-dc23
2013028826
10 9 8 7 6 5 4 3 2 1
PEARSON
ISBN 10: 0-13-305090-4
ISBN 13: 978-0-13-305090-5
BRIEF CONTENTS
Preface xxi
About the Authors xxix
PART I
Decision Making and Analytics: An Overview
PART II
1
Chapter 1
An Overview of Business Intelligence, Analytics,
and Decision Support 2
Chapter 2
Foundations and Technologies for Decision Making
Descriptive Analytics
77
Chapter 3
Data Warehousing
Chapter 4
Business Reporting, Visual Analytics, and Business
Performance Management 135
PART Ill Predictive Analytics
78
185
Chapter 5
Data Mining
Chapter 6
Techniques for Predictive Modeling
Chapter 7
Text Analytics, Text Mining, and Sentiment Analysis
Chapter 8
Web Analytics, Web Mining, and Social Analytics
186
PART IV Prescriptive Analytics
Chapter 9
37
243
288
338
391
Model-Based Decision Making: Optimization and MultiCriteria Systems 392
Chapter 10 Modeling and Analysis: Heuristic Search Methods and
Simulation 435
Chapter 11
Automated Decision Systems and Expert Systems
469
Chapter 12
Knowledge Management and Collaborative Systems
507
PART V Big Data and Future Directions for Business
Analytics 541
Chapter 13 Big Data and Analytics
542
Chapter 14 Business Analytics: Emerging Trends and Future
Impacts 592
Glossary
Index
634
648
iii
CONTENTS
Preface
xxi
About the Authors xxix
Part I
Decision Making and Analytics: An Overview
1
Chapter 1 An Overview of Business Intelligence, Analytics, and
Decision Support 2
1.1
Opening Vignette: Magpie Sensing Employs Analytics to
Manage a Vaccine Supply Chain Effectively and Safely 3
1.2
Changing Business Environments and Computerized
Decision Support 5
The Business Pressures-Responses-Support Model
1.3
Managerial Decision Making
The Nature of Managers’ Work
The Decision-Making Process
5
7
7
8
1.4
Information Systems Support for Decision Making
1.5
An Early Framework for Computerized Decision
Support 11
The Gorry and Scott-Morton Classical Framework
Computer Support for Structured Decisions
Computer Support for Semistructured Problems
13
13
The Concept of Decision Support Systems (DSS)
DSS as an Umbrella Term
14
A Framework for Business Intelligence (Bl)
Definitions of Bl
14
14
A Brief History of Bl
14
The Architecture of Bl
Styles of Bl
13
13
Evolution of DSS into Business Intelligence
1.7
11
12
Computer Support for Unstructured Decisions
1.6
9
15
15
The Origins and Drivers of Bl
16
A Multimedia Exercise in Business Intelligence 16
~ APPLICATION CASE 1.1 Sabre Helps Its Clients Through Dashboards
and Analytics 17
The DSS-BI Connection
1.8
18
Business Analytics Overview
Descriptive Analytics
~
20
APPLICATION CASE 1.2 Eliminating Inefficiencies at Seattle
Children’s Hospital
~
21
APPLICATION CASE 1.3 Analysis at the Speed of Thought
Predictive Analytics
iv
19
22
22
Conte nts
~
APPLICATION CASE 1.4 Moneybal/: Analytics in Sports and Movies
~
APPLICATION CASE 1.5 Analyzing Athletic Injuries
Prescriptive Analytics
23
24
24
~ APPLICATION CASE 1.6 Industrial and Commercial Bank of China
(ICBC) Employs Models to Reconfigure Its Branch Network
1.9
Analytics Applied to Different Domains 26
Analytics or Data Science? 26
Brief Introduction to Big Data Analytics
What Is Big Data? 27
~
25
27
APPLICATION CASE 1.7 Gilt Groupe’s Flash Sales Streamlined by Big
Data Analytics 29
1.10 Plan of the Book 29
Part I: Business Analytics: An Overview
Part II: Descriptive Analytics 30
29
Part Ill: Predictive Analytics 30
Part IV: Prescriptive Analytics 31
Part V: Big Data and Future Directions for Business Analytics 31
1.11 Resources, Links, and the Teradata University Network
Connection 31
Resources and Links 31
Vendors, Products, and Demos 31
Periodicals 31
The Teradata University Network Connection
The Book’s Web Site 32
Chapter Highlights
32
Questions for Discussion
~

Key Terms
33

32
33
Exercises
33
END-OF-CHAPTER APPLICATION CASE Nationwide Insurance Used Bl
to Enhance Customer Service 34
References
35
Chapter 2 Foundations and Technologies for Decision Making
2.1
2.2
Opening Vignette: Decision Modeling at HP Using
Spreadsheets 38
Decision Making: Introduction and Definitions 40
Characteristics of Decision Making 40
A Working Definition of Decision Making
Decision-Making Disciplines 41
2.3
2.4
41
Decision Style and Decision Makers 41
Phases of the Decision-Making Process 42
Decision Making: The Intelligence Phase 44
Problem (or Opportunity) Identification 45
~
APPLICATION CASE 2.1 Making Elevators Go Faster!
Problem Classification
46
Problem Decomposition
Problem Ownership
46
46
45
37
v
vi
Contents
2.5
Decision Making: The Design Phase
Models
47
Mathematical (Quantitative) Models
The Benefits of Models
Normative Models
Suboptimization
47
47
Selection of a Principle of Choice
48
49
49
Descriptive Models
50
Good Enough, or Satisficing
51
Developing (Generating) Alternatives
Measuring Outcomes
Risk
47
52
53
53
Scenarios
54
Possible Scenarios
54
Errors in Decision Making
54
2.6
Decision Making: The Choice Phase
2.7
Decision Making: The Implementation Phase
2.8
How Decisions Are Supported
Support for the Intelligence Phase
Support for the Design Phase
57
Support for the Choice Phase
58
56
58
Decision Support Systems: Capabilities
A DSS Application
55
56
Support for the Implementation Phase
2.9
55
59
59
2.10 DSS Classifications
61
The AIS SIGDSS Classification for DSS
Other DSS Categories
61
63
Custom-Made Systems Versus Ready-Made Systems
63
2.11 Components of Decision Support Systems
The Data Management Subsystem
64
65
The Model Management Subsystem 65
~ APPLICATION CASE 2.2 Station Casinos Wins by Building Customer
Relationships Using Its Data
~
66
APPLICATION CASE 2.3 SNAP DSS Helps OneNet Make
Telecommunications Rate Decisions 68
The User Interface Subsystem
68
The Knowledge-Based Management Subsystem 69
~ APPLICATION CASE 2.4 From a Game Winner to a Doctor!
Chapter Highlights
72
Questions for Discussion
~

Key Terms
73

70
73
Exercises
74
END-OF-CHAPTER APPLICATION CASE Logistics Optimization in a
Major Shipping Company (CSAV)
References
75
74
Conte nts
Part II Descriptive Analytics
Chapter 3 Data Warehousing
77
78
3.1
Opening Vignette: Isle of Capri Casinos Is Winning with
Enterprise Data Warehouse 79
3.2
Data Warehousing Definitions and Concepts
What Is a Data Warehouse?
81
A Historical Perspective to Data Warehousing
Characteristics of Data Warehousing
Data Marts
85
APPLICATION CASE 3.1 A Better Data Plan: Well-Established TELCOs
Leverage Data Warehousing and Analytics to Stay on Top in a
Competitive Industry 85
Data Warehousing Process Overview
~
3.4
83
84
Enterprise Data Warehouses (EDW)
Metadata 85
3.3
81
84
Operational Data Stores
~
Data Warehousing Architectures
Which Architecture Is the Best?
90
93
96
Data Integration and the Extraction, Transformation, and
Load (ETL) Processes 97
Data Integration
~
98
APPLICATION CASE 3.3 BP Lubricants Achieves BIGS Success
Extraction, Transfonnation, and Load
3.6
87
APPLICATION CASE 3.2 Data Warehousing Helps MultiCare Save
More Lives 88
Alternative Data Warehousing Architectures
3.5
102
APPLICATION CASE 3.4 Things Go Better with Coke’s Data
Warehouse
103
Data Warehouse Development Approaches
~
103
APPLICATION CASE 3.5 Starwood Hotels & Resorts Manages Hotel
Profitability with Data Warehousing 106
Additional Data Warehouse Development Considerations
Representation of Data in Data Warehouse
Analysis of Data in the Data Warehouse
OLAP Versus OLTP
OLAP Operations
109
110
11 0
Real-Time Data Warehousing
~
113
APPLICATION CASE 3.6 EDW Helps Connect State Agencies in
Michigan 115
Massive Data Warehouses and Scalability
3.8
107
108
Data Warehousing Implementation Issues
~
98
100
Data Warehouse Development
~
3.7
81
116
117
APPLICATION CASE 3.7 Egg Pie Fries the Competition in Near Real
Time 118
vii
viii
Contents
3.9
Data Warehouse Administration, Security Issues, and Future
Trends 121
The Future of Data Warehousing
123
3.10 Resources, Links, and the Teradata University Network
Connection 126
Resources and Links 126
Cases 126
Vendors, Products, and Demos 127
Periodicals 127
Additional References 127
The Teradata University Network (TUN) Connection 127
Chapter Highlights
128

Questions for Discussion
Key Terms
128

128
Exercises
129
…. END-OF-CHAPTER APPLICATION CASE Continental Airlines Flies High
with Its Real-Time Data Warehouse
References
131
132
Chapter 4 Business Reporting, Visual Analytics, and Business
Performance Management 135
4.1
Opening Vignette:Self-Service Reporting Environment
Saves Millions for Corporate Customers 136
4.2
Business Reporting Definitions and Concepts
What Is a Business Report?
139
140
..,. APPLICATION CASE 4.1 Delta Lloyd Group Ensures Accuracy and
Efficiency in Financial Reporting
141
Components of the Business Reporting System
143
…. APPLICATION CASE 4.2 Flood of Paper Ends at FEMA
4.3
Data and Information Visualization
144
145
..,. APPLICATION CASE 4.3 Tableau Saves Blastrac Thousands of Dollars
with Simplified Information Sharing
A Brief History of Data Visualization
146
147
…. APPLICATION CASE 4.4 TIBCO Spotfire Provides Dana-Farber Cancer
Institute with Unprecedented Insight into Cancer Vaccine Clinical
Trials 149
4.4
Different Types of Charts and Graphs
Basic Charts and Graphs
Specialized Charts and Graphs
4.5
151
The Emergence of Data Visualization and Visual
Analytics 154
Visual Analytics
156
High-Powered Visual Analytics Environments
4.6
150
150
Performance Dashboards
158
160
…. APPLICATION CASE 4.5 Dallas Cowboys Score Big with Tableau and
Teknion
161
Conte nts
Dashboard Design
~
162
APPLICATION CASE 4.6 Saudi Telecom Company Excels with
Information Visualization 163
What to Look For in a Dashboard
164
Best Practices in Dashboard Design
165
Benchmark Key Performance Indicators with Industry Standards
Wrap the Dashboard Metrics with Contextual Metadata
165
Validate the Dashboard Design by a Usability Specialist
165
Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard
Enrich Dashboard with Business Users’ Comments
Present Information in Three Different Levels
4.7
166
~
4.8
166
167
APPLICATION CASE 4.7 IBM Cognos Express Helps Mace for Faster
and Better Business Reporting 169
Performance Measurement
Key Performance Indicator (KPI)
170
171
Performance Measurement System
4.9
166
166
Business Performance Management
Closed-Loop BPM Cycle
165
165
Pick the Right Visual Construct Using Dashboard Design Principles
Provide for Guided Analytics
165
Balanced Scorecards
The Four Perspectives
172
172
173
The Meaning of Balance in BSC
17 4
Dashboards Versus Scorecards
174
4.10 Six Sigma as a Performance Measurement System
The DMAIC Performance Model
175
176
Balanced Scorecard Versus Six Sigma
176
Effective Performance Measurement 177
~ APPLICATION CASE 4.8 Expedia.com’s Customer Satisfaction
Scorecard
178
Chapter Highlights
179
Questions for Discussion
~

180
Exercises
181
184
Part Ill Predictive Analytics
Chapter 5 Data Mining
5.2
181
Key Terms
END-OF-CHAPTER APPLICATION CASE Smart Business Reporting
Helps Healthcare Providers Deliver Better Care 182
References
5.1

185
186
Opening Vignette: Cabela’s Reels in More Customers with
Advanced Analytics and Data Mining 187
Data Mining Concepts and Applications
~
189
APPLICATION CASE 5.1 Smarter Insurance: Infinity P&C Improves
Customer Service and Combats Fraud with Predictive Analytics
191
ix
x
Contents
Definitions, Characteristics, and Benefits
192
..,. APPLICATION CASE 5.2 Harnessing Analytics to Combat Crime:
Predictive Analytics Helps Memphis Police Department Pinpoint Crime
and Focus Police Resources 196
5.3
How Data Mining Works 197
Data Mining Versus Statistics 200
Data Mining Applications 201
…. APPLICATION CASE 5.3 A Mine on Terrorist Funding
5.4
203
Data Mining Process 204
Step 1: Business Understanding 205
Step 2: Data Understanding 205
Step 3: Data Preparation 206
Step 4: Model Building 208
…. APPLICATION CASE 5.4 Data Mining in Cancer Research
Step 5: Testing and Evaluation
5.5
5.6
5.7
210
211
Step 6: Deployment 211
Other Data Mining Standardized Processes and Methodologies 212
Data Mining Methods 214
Classification 214
Estimating the True Accuracy of Classification Models 215
Cluster Analysis for Data Mining 220
..,. APPLICATION CASE 5.5 2degrees Gets a 1275 Percent Boost in Churn
Identification 221
Association Rule Mining 224
Data Mining Software Tools 228
…. APPLICATION CASE 5.6 Data Mining Goes to Hollywood: Predicting
Financial Success of Movies 231
Data Mining Privacy Issues, Myths, and Blunders 234
Data Mining and Privacy Issues 234
…. APPLICATION CASE 5.7 Predicting Customer Buying Patterns-The
Target Story 235
Data Mining Myths and Blunders 236
Chapter Highlights
237

Key Terms
238
Questions for Discussion 238 • Exercises 239
…. END-OF-CHAPTER APPLICATION CASE Macys.com Enhances Its
Customers’ Shopping Experience with Analytics
References
241
241
Chapter 6 Techniques for Predictive Modeling
243
6.1
Opening Vignette: Predictive Modeling Helps Better
Understand and Manage Complex Medical
Procedures 244
6.2
Basic Concepts of Neural Networks 247
Biological and Artificial Neural Networks 248
..,. APPLICATION CASE 6.1 Neural Networks Are Helping to Save Lives in
the Mining Industry 250
Elements of ANN 251
Conte nts
Network Information Processing 252
Neural Network Architectures 254
~
APPLICATION CASE 6.2 Predictive Modeling Is Powering the Power
Generators 256
6.3
Developing Neural Network-Based Systems
The General ANN Learning Process 259
Backpropagation 260
6.4
Illuminating the Black Box of ANN with Sensitivity
Analysis 262
~
6.5
APPLICATION CASE 6.3 Sensitivity Analysis Reveals Injury Severity
Factors in Traffic Accidents 264
Support Vector Machines
~
265
APPLICATION CASE 6.4 Managing Student Retention with Predictive
Modeling 266
Mathematical Formulation of SVMs
Primal Form 271
Dual Form 271
Soft Margin 271
Nonlinear Classification
Kernel Trick 272
270
272
6.6
A Process-Based Approach to the Use of SVM
Support Vector Machines Versus Artificial Neural Networks
6.7
Nearest Neighbor Method for Prediction
Similarity Measure: The Distance Metric 276
Parameter Selection
~
258
273
274
275
277
APPLICATION CASE 6.5 Efficient Image Recognition and
Categorization with kNN 278
Chapter Highlights
280

Key Terms
280
Questions for Discussion 281 • Exercises 281
~ END-OF-CHAPTER APPLICATION CASE Coors Improves Beer Flavors
with Neural Networks
References
284
285
Chapter 7 Text Analytics, Text Mining, and Sentiment Analysis
288
7.1
Opening Vignette: Machine Versus Men on Jeopardy!: The
Story of Watson 289
7.2
Text Analytics and Text Mining Concepts and
Definitions 291
~
7.3
Natural Language Processing
~
7.4
APPLICATION CASE 7.1 Text Mining for Patent Analysis
296
APPLICATION CASE 7.2 Text Mining Improves Hong Kong
Government’s Ability to Anticipate and Address Public Complaints
Text Mining Applications
Marketing Applications
Security Applications
~
295
300
301
301
APPLICATION CASE 7.3 Mining for Lies
Biomedical App …
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