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 Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo Editor in Chief: Stephanie Wall Executive Editor: Bob Horan Program Manager Team Lead: Ashley Santora Program Manager: Denise Vaughn Executive Marketing Manager: Anne Fahlgren Project Manager Team Lead: Judy Leale Project Manager: Tom Benfatti Operations Specialist: Michelle Klein Creative Director: Jayne Conte Cover Designer: Suzanne Behnke Digital Production Project Manager: Lisa Rinaldi Full-Service Project Management: George Jacob, Integra Software Solution s. Printer/Binder: Edwards Brothers Malloy-Jackson Road Cover Printer: Lehigh/Phoenix-Hagerstown Text Font: Garamond Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook appear on the appropriate page within text. Microsoft and/ or its respective suppliers make no representations about the suitability of the information contained in the documents and related graphics published as part of the services for any purpose. All such documents and related graphics are provided "as is" without warranty of any kind. Microsoft and/or its respective suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for any special, indirect or consequential damages or any damages whatsoever resulting from loss of use , data or profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection with the use or performance of information available from the services. The documents and related graphics contained herein could include technical inaccuracies or typographical errors. Changes are periodically added to the information here in. Microsoft and/or its respective suppliers may make improvements and/or changes in the product(s) and/ or the program(s) described herein at any time. Partial screen shots may be viewed in full within the software version specified. Microsoft® Windows®, and Microsoft Office® are registered trademarks of the Microsoft Corporation in the U.S.A. and other countries. This book is not .

1. 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

2. 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
Boston Columbus Indianapolis New York San Francisco Upper
Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich
Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul
Singapore Taipei Tokyo
Editor in Chief: Stephanie Wall
Executive Editor: Bob Horan
Program Manager Team Lead: Ashley Santora
Program Manager: Denise Vaughn
Executive Marketing Manager: Anne Fahlgren
Project Manager Team Lead: Judy Leale
Project Manager: Tom Benfatti

3. Operations Specialist: Michelle Klein
Creative Director: Jayne Conte
Cover Designer: Suzanne Behnke
Digital Production Project Manager: Lisa
Rinaldi
Full-Service Project Management: George Jacob,
Integra Software
Solution
s.
Printer/Binder: Edwards Brothers Malloy-Jackson
Road
Cover Printer: Lehigh/Phoenix-Hagerstown
Text Font: Garamond
Credits and acknowledgments borrowed from other sources and
reproduced, with permission, in this textbook
appear on the appropriate page within text.
Microsoft and/ or its respective suppliers make no
representations about the suitability of the information
contained in the documents and related graphics published as

4. part of the services for any purpose. All such
documents and related graphics are provided "as is" without
warranty of any kind. Microsoft and/or its
respective suppliers hereby disclaim all warranties and
conditions with regard to this information, including
all warranties and conditions of merchantability, whether
express, implied or statutory, fitness for a particular
purpose, title and non-infringement. In no event shall Microsoft
and/or its respective suppliers be liable for
any special, indirect or consequential damages or any damages
whatsoever resulting from loss of use , data or
profits, whether in an action of contract, negligence or other
tortious action, arising out of or in connection
with the use or performance of information available from the
services.
The documents and related graphics contained herein could
include technical inaccuracies or typographical
errors. Changes are periodically added to the information here
in. Microsoft and/or its respective suppliers may
make improvements and/or changes in the product(s) and/ or the
program(s) described herein at any time.
Partial screen shots may be viewed in full within the software
version specified.

5. Microsoft® Windows®, and Microsoft Office® are registered
trademarks of the Microsoft Corporation in the U.S.A.
and other countries. This book is not sponsored or endorsed by
or affiliated with the Microsoft Corporation.
Copyright© 2015, 2011, 2007 by Pearson Education, Inc., One
Lake Street, Upper Saddle River,
New Jersey 07458. All rights reserved. Manufactured in the
United States of America. This publication
is protected by Copyright, and permission should be obtained
from the publisher prior to any prohibited
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please submit a written request to Pearson Education, Inc.,
Permissions Department, One Lake Street,
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request to 201-236-3290.
Many of the designations by manufacturers and sellers to
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Where those designations appear in this book, and the publisher
was aware of a trademark claim, the
designations have been printe d in initial caps or all caps.

6. 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
10 9 8 7 6 5 4 3 2 1

7. PEARSON
2013028826
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 1
Chapter 1 An Overview of Business Intelligence, Analytics,
and Decision Support 2
Chapter 2 Foundations and Technologies for Decision Making
37
PART II Descriptive Analytics 77

8. Chapter 3 Data Warehousing 78
Chapter 4 Business Reporting, Visual Analytics, and Business
Performance Management 135
PART Ill Predictive Analytics 185
Chapter 5 Data Mining 186
Chapter 6 Techniques for Predictive Modeling 243
Chapter 7 Text Analytics, Text Mining, and Sentiment Analysis
288
Chapter 8 Web Analytics, Web Mining, and Social Analytics
338
PART IV Prescriptive Analytics 391
Chapter 9 Model-Based Decision Making: Optimization and
Multi-
Criteria Systems 392
Chapter 10 Modeling and Analysis: Heuristic Search Methods

9. 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 634
Index 648
iii

10. iv
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 5
1.3 Managerial Decision Making 7
The Nature of Managers' Work 7

11. The Decision-Making Process 8
1.4 Information Systems Support for Decision Making 9
1.5 An Early Framework for Computerized Decision
Support 11
The Gorry and Scott-Morton Classical Framework 11
Computer Support for Structured Decisions 12
Computer Support for Unstructured Decisions 13
Computer Support for Semistructured Problems 13
1.6 The Concept of Decision Support Systems (DSS) 13
DSS as an Umbrella Term 13
Evolution of DSS into Business Intelligence 14
1.7 A Framework for Business Intelligence (Bl) 14
Definitions of Bl 14

12. A Brief History of Bl 14
The Architecture of Bl 15
Styles of Bl 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 18
1.8 Business Analytics Overview 19
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

13. 22
Predictive Analytics 22
~ APPLICATION CASE 1.4 Moneybal/: Analytics in Sports and
Movies 23
~ APPLICATION CASE 1.5 Analyzing Athletic Injuries 24
Prescriptive Analytics 24
~ APPLICATION CASE 1.6 Industrial and Commercial Bank of
China
(ICBC) Employs Models to Reconfigure Its Branch Network 25
Analytics Applied to Different Domains 26
Analytics or Data Science? 26
1.9 Brief Introduction to Big Data Analytics 27
What Is Big Data? 27
~ APPLICATION CASE 1.7 Gilt Groupe's Flash Sales

14. Streamlined by Big
Data Analytics 29
1.10 Plan of the Book 29
Part I: Business Analytics: An Overview 29
Part II: Descriptive Analytics 30
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

15. The Teradata University Network Connection 32
The Book's Web Site 32
Chapter Highlights 32 • Key Terms 33
Questions for Discussion 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
37
2.1 Opening Vignette: Decision Modeling at HP Using
Spreadsheets 38
2.2 Decision Making: Introduction and Definitions 40
Characteristics of Decision Making 40
A Working Definition of Decision Making 41

16. Decision-Making Disciplines 41
Decision Style and Decision Makers 41
2.3 Phases of the Decision-Making Process 42
2.4 Decision Making: The Intelligence Phase 44
Problem (or Opportunity) Identification 45
~ APPLICATION CASE 2.1 Making Elevators Go Faster! 45
Problem Classification 46
Problem Decomposition 46
Problem Ownership 46
Conte nts v
vi Contents
2.5 Decision Making: The Design Phase 47
Models 47

17. Mathematical (Quantitative) Models 47
The Benefits of Models 4 7
Selection of a Principle of Choice 48
Normative Models 49
Suboptimization 49
Descriptive Models 50
Good Enough, or Satisficing 51
Developing (Generating) Alternatives 52
Measuring Outcomes 53
Risk 53
Scenarios 54
Possible Scenarios 54
Errors in Decision Making 54

18. 2.6 Decision Making: The Choice Phase 55
2.7 Decision Making: The Implementation Phase 55
2.8 How Decisions Are Supported 56
Support for the Intelligence Phase 56
Support for the Design Phase 5 7
Support for the Choice Phase 58
Support for the Implementation Phase 58
2.9 Decision Support Systems: Capabilities 59
A DSS Application 59
2.10 DSS Classifications 61
The AIS SIGDSS Classification for DSS 61
Other DSS Categories 63
Custom-Made Systems Versus Ready-Made Systems 63

19. 2.11 Components of Decision Support Systems 64
The Data Management Subsystem 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!
70
Chapter Highlights 72 • Key Terms 73
Questions for Discussion 73 • Exercises 74
~ END-OF-CHAPTER APPLICATION CASE Logistics
Optimization in a

20. Major Shipping Company (CSAV) 74
References 75
Part II Descriptive Analytics 77
Chapter 3 Data Warehousing 78
3.1 Opening Vignette: Isle of Capri Casinos Is Winning with
Enterprise Data Warehouse 79
3.2 Data Warehousing Definitions and Concepts 81
What Is a Data Warehouse? 81
A Historical Perspective to Data Warehousing 81
Characteristics of Data Warehousing 83
Data Marts 84
Operational Data Stores 84

21. Enterprise Data Warehouses (EDW) 85
Metadata 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
3.3 Data Warehousing Process Overview 87
~ APPLICATION CASE 3.2 Data Warehousing Helps MultiCare
Save
More Lives 88
3.4 Data Warehousing Architectures 90
Alternative Data Warehousing Architectures 93
Which Architecture Is the Best? 96
3.5 Data Integration and the Extraction, Transformation, and
Load (ETL) Processes 97
Data Integration 98

22. ~ APPLICATION CASE 3.3 BP Lubricants Achieves BIGS
Success 98
Extraction, Transfonnation, and Load 100
3.6 Data Warehouse Development 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 107
Representation of Data in Data Warehouse 108
Analysis of Data in the Data Warehouse 109
OLAP Versus OLTP 110
OLAP Operations 11 0

23. 3.7 Data Warehousing Implementation Issues 113
~ APPLICATION CASE 3.6 EDW Helps Connect State
Agencies in
Michigan 115
Massive Data Warehouses and Scalability 116
3.8 Real-Time Data Warehousing 117
~ APPLICATION CASE 3.7 Egg Pie Fries the Competition in
Near Real
Time 118
Conte nts vii
viii Conte nts
3.9 Data Warehouse Administration, Security Issues, and Future
Trends 121
The Future of Data Warehousing 123

24. 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 • Key Terms 128
Questions for Discussion 128 • Exercises 129
.... END-OF-CHAPTER APPLICATION CASE Continental
Airlines Flies High
with Its Real-Time Data Warehouse 131
References 132

25. 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 139
What Is a Business Report? 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
144
4.3 Data and Information Visualization 145
..,. APPLICATION CASE 4.3 Tableau Saves Blastrac
Thousands of Dollars
with Simplified Information Sharing 146

26. A Brief History of Data Visualization 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 150
Basic Charts and Graphs 150
Specialized Charts and Graphs 151
4.5 The Emergence of Data Visualization and Visual
Analytics 154
Visual Analytics 156
High-Powered Visual Analytics Environments 158
4.6 Performance Dashboards 160
.... APPLICATION CASE 4.5 Dallas Cowboys Score Big with
Tableau and

27. Teknion 161
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
165
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 165

28. Enrich Dashboard with Business Users' Comments 165
Present Information in Three Different Levels 166
Pick the Right Visual Construct Using Dashboard Design
Principles 166
Provide for Guided Analytics 166
4.7 Business Performance Management 166
Closed-Loop BPM Cycle 167
~ APPLICATION CASE 4.7 IBM Cognos Express Helps Mace
for Faster
and Better Business Reporting 169
4.8 Performance Measurement 170
Key Performance Indicator (KPI) 171
Performance Measurement System 172
4.9 Balanced Scorecards 172

29. The Four Perspectives 173
The Meaning of Balance in BSC 17 4
Dashboards Versus Scorecards 174
4.10 Six Sigma as a Performance Measurement System 175
The DMAIC Performance Model 176
Balanced Scorecard Versus Six Sigma 176
Effective Performance Measurement 1 77
~ APPLICATION CASE 4.8 Expedia.com's Customer
Satisfaction
Scorecard 178
Chapter Highlights 179 • Key Terms 180
Questions for Discussion 181 • Exercises 181
~ END-OF-CHAPTER APPLICATION CASE Smart Business
Reporting
Helps Healthcare Providers Deliver Better Care 182

30. References 184
Part Ill Predictive Analytics 185
Chapter 5 Data Mining 186
5.1 Opening Vignette: Cabela's Reels in More Customers with
Advanced Analytics and Data Mining 187
5.2 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
Conte nts ix
x Conte nts
Definitions, Characteristics, and Benefits 192
..,. APPLICATION CASE 5.2 Harnessing Analytics to Combat

31. Crime:
Predictive Analytics Helps Memphis Police Department
Pinpoint Crime
and Focus Police Resources 196
How Data Mining Works 197
Data Mining Versus Statistics 200
5.3 Data Mining Applications 201
.... APPLICATION CASE 5.3 A Mine on Terrorist Funding 203
5.4 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
210
Step 5: Testing and Evaluation 211

32. Step 6: Deployment 211
Other Data Mining Standardized Processes and Methodologies
212
5.5 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
5.6 Data Mining Software Tools 228
.... APPLICATION CASE 5.6 Data Mining Goes to Hollywood:
Predicting
Financial Success of Movies 231

33. 5.7 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 241
References 241
Chapter 6 Techniques for Predictive Modeling 243
6.1 Opening Vignette: Predictive Modeling Helps Better
Understand and Manage Complex Medical
Procedures 244

34. 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
Network Information Processing 2 52
Neural Network Architectures 254
~ APPLICATION CASE 6.2 Predictive Modeling Is Powering
the Power
Generators 256
6.3 Developing Neural Network-Based Systems 258
The General ANN Learning Process 259
Backpropagation 260

35. 6.4 Illuminating the Black Box of ANN with Sensitivity
Analysis 262
~ APPLICATION CASE 6.3 Sensitivity Analysis Reveals Injury
Severity
Factors in Traffic Accidents 264
6.5 Support Vector Machines 265
~ APPLICATION CASE 6.4 Managing Student Retention with
Predictive
Modeling 266
Mathematical Formulation of SVMs 270
Primal Form 271
Dual Form 271
Soft Margin 271
Nonlinear Classification 272
Kernel Trick 272

36. 6.6 A Process-Based Approach to the Use of SVM 273
Support Vector Machines Versus Artificial Neural Networks
274
6.7 Nearest Neighbor Method for Prediction 275
Similarity Measure: The Distance Metric 276
Parameter Selection 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 284
References 285
Chapter 7 Text Analytics, Text Mining, and Sentiment Analysis
288

37. 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
~ APPLICATION CASE 7.1 Text Mining for Patent Analysis
295
7.3 Natural Language Processing 296
~ APPLICATION CASE 7.2 Text Mining Improves Hong Kong
Government's Ability to Anticipate and Address Public
Complaints 298
7.4 Text Mining Applications 300
Marketing Applications 301
Security Applications 301
~ APPLICATION CASE 7.3 Mining for Lies 302
Biomedical Applications 304
Conte nts xi

38. xii Conte nts
Academic Applications 305
.... APPLICATION CASE 7.4 Text Mining and Sentiment
Analysis Help
Improve Customer Service Performance 306
7.5 Text Mining Process 307
Task 1: Establish the Corpus 308
Task 2: Create the Term-Document Matrix 309
Task 3: Extract the Knowledge 312
..,. APPLICATION CASE 7.5 Research Literature Survey with
Text
Mining 314
7.6 Text Mining Tools 317

39. Commercial Software Tools 317
Free Software Tools 317
..,. APPLICATION CASE 7.6 A Potpourri ofText Mining Case
Synopses 318
7.7 Sentiment Analysis Overview 319
..,. APPLICATION CASE 7.7 Whirlpool Achieves Customer
Loyalty and
Product Success with Text Analytics 321
7.8 Sentiment Analysis Applications 323
7.9 Sentiment Analysis Process 325
Methods for Polarity Identification 326
Using a Lexicon 327
Using a Collection of Training Documents 328
Identifying Semantic Orientation of Sentences and Phrases 328
Identifying Semantic Orientation of Document 328

40. 7.10 Sentiment Analysis and Speech Analytics 329
How Is It Done? 329
..,. APPLICATION CASE 7.8 Cutting Through the Confusion:
Blue Cross
Blue Shield of North Carolina Uses Nexidia's Speech Analytics
to Ease
Member Experience in Healthcare 331
Chapter Highlights 333 • Key Terms 333
Questions for Discussion 334 • Exercises 334
.... END-OF-CHAPTER APPLICATION CASE BBVA
Seamlessly Monitors
and Improves Its Online Reputation 335
References 336
Chapter 8 Web Analytics, Web Mining, and Social Analytics
338
8.1 Opening Vignette: Security First Insurance Deepens

41. Connection with Policyholders 339
8.2 Web Mining Overview 341
8.3 Web Content and Web Structure Mining 344
.... APPLICATION CASE 8.1 Identifying Extremist Groups
with Web Link
and Content Analysis 346
8.4 Search Engines 347
Anatomy of a Search Engine 347
1. Development Cycle 348
Web Crawler 348
Document Indexer 348
2. Response Cycle 349
Query Analyzer 349

42. Document Matcher/Ranker 349
How Does Google Do It? 351
~ APPLICATION CASE 8.2 IGN Increases Search Traffic by
1500 Percent 353
8.5 Search Engine Optimization 354
Methods for Search Engine Optimization 355
~ APPLICATION CASE 8.3 Understanding Why Customers
Abandon
Shopping Carts Results in $10 Million Sales Increase 357
8.6 Web Usage Mining (Web Analytics) 358
Web Analytics Technologies 359
~ APPLICATION CASE 8.4 Allegro Boosts Online Click-
Through Rates by
500 Percent with Web Analysis 360
Web Analytics Metrics 362
Web Site Usability 362

43. Traffic Sources 363
Visitor Profiles 364
Conversion Statistics 364
8.7 Web Analytics Maturity Model and Web Analytics Tools
366
Web Analytics Tools 368
Putting It All Together-A Web Site Optimization Ecosystem 370
A Framework for Voice of the Customer Strategy 372
8.8 Social Analytics and Social Network Analysis 373
Social Network Analysis 374
Social Network Analysis Metrics 375
~ APPLICATION CASE 8.5 Social Network Analysis Helps
Telecommunication Firms 375

44. Connections 376
Distributions 376
Segmentation 377
8.9 Social Media Definitions and Concepts 377
How Do People Use Social Media? 378
~ APPLICATION CASE 8.6 Measuring the Impact of Social
Media at
Lollapalooza 379
8.10 Social Media Analytics 380
Measuring the Social Media Impact 381
Best Practices in Social Media Analytics 381
~ APPLICATION CASE 8.7 eHarmony Uses Social Media to
Help Take the
Mystery Out of Online Dating 383
Social Media Analytics Tools and Vendors 384

45. Chapter Highlights 386 • Key Terms 387
Questions for Discussion 387 • Exercises 388
~ END-OF-CHAPTER APPLICATION CASE Keeping Students
on Track with
Web and Predictive Analytics 388
References 390
Conte nts xiii
xiv Contents
Part IV Prescriptive Analytics 391
Chapter 9 Model-Based Decision Making: Optimization and
Multi-Criteria Systems 392
9.1 Opening Vignette: Midwest ISO Saves Billions by Better
Planning of Power Plant Operations and Capacity
Planning 393

46. 9.2 Decision Support Systems Modeling 394
~ APPLICATION CASE 9.1 Optimal Transport for ExxonMobil
Downstream Through a DSS 395
Current Modeling Issues 396
~ APPLICATION CASE 9.2 Forecasting/Predictive Analytics
Proves to Be
a Good Gamble for Harrah's Cherokee Casino and Hotel 397
9.3 Structure of Mathematical Models for Decision Support 399
The Components of Decision Support Mathematical Models 399
The Structure of Mathematical Models 401
9.4 Certainty, Uncertainty, and Risk 401
Decision Making Under Certainty 402
Decision Making Under Uncertainty 402
Decision Making Under Risk (Risk Analysis) 402
~ APPLICATION CASE 9.3 American Airlines Uses
Should-Cost Modeling to Assess the Uncertainty of Bids

47. for Shipment Routes 403
9.5 Decision Modeling with Spreadsheets 404
~ APPLICATION CASE 9.4 Showcase Scheduling at Fred
Astaire East
Side Dance Studio 404
9.6 Mathematical Programming Optimization 407
~ APPLICATION CASE 9.5 Spreadsheet Model Helps Assign
Medical
Residents 407
Mathematical Programming 408
Linear Programming 408
Modeling in LP: An Example 409
Implementation 414
9.7 Multiple Goals, Sensitivity Analysis, What-If Analysis,
and Goal Seeking 416
Multiple Goals 416

48. Sensitivity Analysis 417
What-If Analysis 418
Goal Seeking 418
9.8 Decision Analysis with Decision Tables and Decision
Trees 420
Decision Tables 420
Decision Trees 422
9.9 Multi-Criteria Decision Making With Pairwise
Comparisons 423
The Analytic Hierarchy Process 423
~ APPLICATION CASE 9.6 U.S. HUD Saves the House by
Using
AHP for Selecting IT Projects 423
Tutorial on Applying Analytic Hierarchy Process Using Web-

49. HIPRE 425
Chapter Highlights 429 • Key Terms 430
Questions for Discussion 430 • Exercises 430
~ END-OF-CHAPTER APPLICATION CASE Pre-Positioning of
Emergency
Items for CARE International 433
References 434
Chapter 10 Modeling and Analysis: Heuristic Search Methods
and
Simulation 435
10.1 Opening Vignette: System Dynamics Allows Fluor
Corporation to Better Plan for Project and Change
Management 436
10.2 Problem-Solving Search Methods 437
Analytical Techniques 438
Algorithms 438
Blind Searching 439

50. Heuristic Searching 439
~ APPLICATION CASE 10.1 Chilean Government Uses
Heuristics to
Make Decisions on School Lunch Providers 439
10.3 Genetic Algorithms and Developing GA Applications 441
Example: The Vector Game 441
Terminology of Genetic Algorithms 443
How Do Genetic Algorithms Work? 443
Limitations of Genetic Algorithms 445
Genetic Algorithm Applications 445
10.4 Simulation 446
~ APPLICATION CASE 10.2 Improving Maintenance Decision
Making in
the Finnish Air Force Through Simulation 446
~ APPLICATION CASE 10.3 Simulating Effects of Hepatitis B
Interventions 447

51. Major Characteristics of Simulation 448
Advantages of Simulation 449
Disadvantages of Simulation 450
The Methodology of Simulation 450
Simulation Types 451
Monte Carlo Simulation 452
Discrete Event Simulation 453
10.5 Visual Interactive Simulation 453
Conventional Simulation Inadequacies 453
Visual Interactive Simulation 453
Visual Interactive Models and DSS 454
~ APPLICATION CASE 10.4 Improving Job-Shop Scheduling
Decisions
Through RFID: A Simulation-Based Assessment 454
Simulation Software 457
Conte nts xv

52. xvi Contents
10.6 System Dynamics Modeling 458
10.7 Agent-Based Modeling 461
~ APPLICATION CASE 10.5 Agent-Based Simulation Helps
Analyze
Spread of a Pandemic Outbreak 463
Chapter Highlights 464 • Key Terms 464
Questions for Discussion 465 • Exercises 465
~ END-OF-CHAPTER APPLICATION CASE HP Applies
Management
Science Modeling to Optimize Its Supply Chain and Wins a
Major
Award 465
References 467
Chapter 11 Automated Decision Systems and Expert Systems
469
11.1 Opening Vignette: I nterContinental Hotel Group Uses

53. Decision Rules for Optimal Hotel Room Rates 470
11.2 Automated Decision Systems 471
~ APPLICATION CASE 11.1 Giant Food Stores Prices the
Entire
Store 472
11.3 The Artificial Intelligence Field 475
11.4 Basic Concepts of Expert Systems 477
Experts 477
Expertise 478
Features of ES 478
~ APPLICATION CASE 11.2 Expert System Helps in
Identifying Sport
Talents 480
11.5 Applications of Expert Systems 480
~ APPLICATION CASE 11.3 Expert System Aids in
Identification of

54. Chemical, Biological, and Radiological Agents 481
Classical Applications of ES 481
Newer Applications of ES 482
Areas for ES Applications 483
11.6 Structure of Expert Systems 484
Knowledge Acquisition Subsystem 484
Knowledge Base 485
Inference Engine 485
User Interface 485
Blackboard (Workplace) 485
Explanation Subsystem (Justifier) 486
Knowledge-Refining System 486
~ APPLICATION CASE 11.4 Diagnosing Heart Diseases by
Signal
Processing 486
11.7 Knowledge Engineering 487
Knowledge Acquisition 488

55. Knowledge Verification and Validation 490
Knowledge Representation 490
Inferencing 491
Explanation and Justification 496
11.8 Problem Areas Suitable for Expert Systems 497
11.9 Development of Expert Systems 498
Defining the Nature and Scope of the Problem 499
Identifying Proper Experts 499
Acquiring Knowledge 499
Selecting the Building Tools 499
Coding the System 501
Evaluating the System 501
.... APPLICATION CASE 11.5 Clinical Decision Support

56. System for Tendon
Injuries 501
…
BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE

57. 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

58. National Sun Yat-sen University
David King
]DA Software Group, Inc.
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