IT445 SEU Decision Support Systems Worksheet

  

Description

College of Computing and Informatics
Assignment 2
Deadline: Day 21/2/2021 @ 23:59
[Total Mark for this Assignment is 5]
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Question One
Pg. 01
Learning
Outcome(s):
Develop student’s
Knowledge on
how EDW can
contribute to
Business
Intelligence
Question One
1 Marks
Discuss, analytically yet briefly, the role of an enterprise data warehouse (EDW) in the
decision-making process. (0.5 marks) Relate your discussion to operational databases
and the type of decisions they support as well as their insufficiency for effective
business intelligence strategy compared to EDW. (0.5 marks)
Question Two
Pg. 02
Learning
Outcome(s):
Develop student’s
knowledge on
performance
measurement and
management
systems
Question Two
1 Marks
What does a Performance Measurement System consist of? (0.3 marks) Which
Performance Management System is considered a management as well as
measurement methodology? (0.3 marks) What is its role as a performance measure and
as a management tool separately. (0.4 marks)
Question Three
Pg. 03
Learning
Outcome(s):
Develop students’
knowledge for
design principles
of Information
Visualization
Question Three
1.5 Marks
Decision makers in XYZ company need to analyze the organization’s financial
performance in order to determine whether departments’ expenditures are over or
under budget, thus, deriving actionable decisions. Table 1 provides information
extracted from the organization’s data warehouse on the budgeted expenses against
actual expenses of the company’s departments over one year period of time.
Dept.
Distribution
Facilities
HR
IS
Q1
Budget
390,000
675,000
350,000
950,000
Q1
Q2
Q2
Q3
Q3
Actual
Budget
Actual
Budget
Actual
375,000 395,000 382,000 400,000 390,000
693,000 800,000 837,000 750,000 713,000
346,000 350,000 342,000 350,000 340,000
925,000 850,000 890,000 875,000 976,000
Table 1: Departments’ yearly performance
Q4
Budget
410,000
750,000
350,000
900,000
Q4
Actual
408,000
790,000
367,000
930,000
A) Use the most appropriate basic visual chart covered in class to show, for each
department, the percentage variance between the budgeted and actual expences
over each of the four quarters. (0.5 marks)
Variance% = ((budgeted expense – actual expense) / actual expense) * 100
(a positive value indicates that the department is spending less than the
budgeted amount, whereas negative values indicate that expenses are more than
anticipated).
B)
Use the most appropriate basic visual chart covered in class to visualize the
difference between the departments’ overall budget values against overall
actual values over the four quarters. (0.5 marks)
C) As a decision maker, draw one informative conclusion from each chart (A) and
(B) using your own words. (0.5 marks)
Question Four
Pg. 04
Learning
Outcome(s):
Develop student’s
knowledge on
data warehouse
OLAP operations
Question Four
1.5 Marks
Suppose that a data warehouse consists of the three dimensions, supplier[sup_name,
type] customer[cust_name, email, mobile], location[street, city, country], and the two
measures units_sold, and sales_amount (in SAR).
A) Draw a Star schema diagram for the above data warehouse. Include the
appropriate attributes for dimension and factor tables (DT and FT). (0.5 marks)
B) Explain how Snowflake schema can provide better representation over Star
schema. In the given 3D warehouse above, determine which DT will be
affected when implementing the Snowflake model? (0.5 marks)
C) What specific OLAP operations (roll-up, drill down, dice, slice) should be
performed (based on the above schema (A)) in order to list the total sales of
each supplier in Saudi Arabia? (0.5 marks)
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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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 Applications
304
302
298
xi
xii
Contents
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
Commercial Software Tools
Free Software Tools
317
317
..,. APPLICATION CASE 7.6 A Potpourri ofText Mining Case Synopses
7.7
Sentiment Analysis Overview
318
319
..,. APPLICATION CASE 7.7 Whirlpool Achieves Customer Loyalty and
Product Success with Text Analytics 321
7.8
Sentiment Analysis Applications
7.9
Sentiment Analysis Process
325
Methods for Polarity Identification
Using a Lexicon
323
326
327
Using a Collection of Training Documents
328
Identifying Semantic Orientation of Sentences and Phrases
Identifying Semantic Orientation of Document
328
7.10 Sentiment Analysis and Speech Analytics
How Is It Done?
328
329
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
References
335
336
Chapter 8 Web Analytics, Web Mining, and Social Analytics
338
8.1
Opening Vignette: Security First Insurance Deepens
Connection with Policyholders 339
8.2
Web Mining Overview
8.3
Web Content and Web Structure Mining
341
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
1. Development Cycle
348
Web Crawler 348
Document Indexer
348
347
Conte nts
2. Response Cycle
Query Analyzer
349
349
Document Matcher/Ranker
How Does Google Do It?
~
8.5
349
351
APPLICATION CASE 8.2 IGN Increases Search Traffic by 1500 Percent
Search Engine Optimization
354
Methods for Search Engine Optimization
~
8.6
APPLICATION CASE 8.3 Understanding Why Customers Abandon
Shopping Carts Results in $10 Million Sales Increase 357
Web Analytics Technologies
358
359
APPLICATION CASE 8.4 Allegro Boosts Online Click-Through Rates by
500 Percent with Web Analysis 360
Web Analytics Metrics
Web Site Usability
362
362
Traffic Sources
363
Visitor Profiles
364
Conversion Statistics
8.7
355
Web Usage Mining (Web Analytics)
~
353
364
Web Analytics Maturity Model and Web Analytics Tools
Web Analytics Tools
366
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
Social Network Analysis
374
Social Network Analysis Metrics
~
375
APPLICATION CASE 8.5 Social Network Analysis Helps
Telecommunication Firms 375
Connections
376
Distributions
376
Segmentation
8.9
377
Social Media Definitions and Concepts
How Do People Use Social Media?
~
377
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
~
373
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
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
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
9.2
Decision Support Systems Modeling
~
APPLICATION CASE 9.1 Optimal Transport for ExxonMobil
Downstream Through a DSS 395
Current Modeling Issues
~
394
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
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
~
9.5
9.6
APPLICATION CASE 9.3 American Airlines Uses
Should-Cost Modeling to Assess the Uncertainty of Bids
for Shipment Routes 403
Decision Modeling with Spreadsheets
~
404
APPLICATION CASE 9.4 Showcase Scheduling at Fred Astaire East
Side Dance Studio 404
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
Sensitivity Analysis 417
What-If Analysis 418
Goal Seeking 418
9.8
Decision Analysis with Decision Tables and Decision
Trees 420
Decision Tables
Decision Trees
9.9
399
420
422
Multi-Criteria Decision Making With Pairwise
Comparisons 423
The Analytic Hierarchy Process
423
Conte nts
~ 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-HIPRE 425
Chapter Highlights 429 • Key Terms 430
Questions for Discussion 430 • Exercises 430
~ END-OF-CHAPTER APPLICATION CASE Pre-Positioning of Emergency
433
Items for CARE International
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
Analytical Techniques
Algorithms
438
Blind Searching
439
Heuristic Searching
~
437
438
439
APPLICATION CASE 10.1 Chilean Government Uses Heuristics to
Make Decisions on School Lunch Providers
10.3
Example: The Vector Game
443
How Do Genetic Algorithms Work?
Limitations of Genetic Algorithms
Genetic Algorithm Applications
Simulation
441
441
Terminology of Genetic Algorithms
10.4
439
Genetic Algorithms and Developing GA Applications
443
445
445
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
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
xv
xvi
Contents
10.6
10.7
System Dynamics Modeling
Agent-Based Modeling
~
458
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: InterContinental Hotel Group Uses
11.2
Decision Rules for Optimal Hotel Room Rates
Automated Decision Systems 471
~
11.3
11.4
480
APPLICATION CASE 11.3 Expert System Aids in Identification of
Chemical, Biological, and Radiological Agents 481
Classical Applications of ES 481
Newer Applications of ES 482
Areas for ES Applications 483
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
~
11.7
APPLICATION CASE 11.2 Expert System Helps in Identifying Sport
Talents 480
Applications of Expert Systems
~
11.6
APPLICATION CASE 11.1 Giant Food Stores Prices the Entire
Store 472
The Artificial Intelligence Field 475
Basic Concepts of Expert Systems 477
Experts 477
Expertise 478
Features of ES 478
~
11.5
470
APPLICATION CASE 11.4 Diagnosing Heart Diseases by Signal
Processing 486
Knowledge Engineering 487
Knowledge Acquisition 488
Knowledge Verification and Validation 490
Knowledge Representation
Inferencing 491
Explanation and Justification
490
496
Conte nts
11.8
11.9
Problem Areas Suitable for Expert Systems
Development of Expert Systems 498
Defining the Nature and Scope of the Problem 499
497
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 System for Tendon
Injuries 501
11.10 Concluding Remarks
Chapter Highlights
503
Questions for Discussion
502
•
504
Key Terms
•
503
Exercises
504
…. END·OF·CHAPTER APPLICATION CASE Tax Collections Optimization
for New York State 504
References
505
Chapter 12 Knowledge Management and Collaborative Systems 507
12.1 Opening Vignette: Expertise Transfer System to Train
12.2
Future Army Personnel 508
Introduction to Knowledge Management
Knowledge Management Concepts and Definitions
Knowledge 513
12.3
512
513
Explicit and Tacit Knowledge 515
Approaches to Knowledge Management 516
The Process Approach to Knowledge Management 517
The Practice Approach to Knowledge Management 51 7
Hybrid Approaches to Knowledge Management 51 8
12.4
Knowledge Repositories 518
Information Technology (IT) in Knowledge
Management 520
The KMS Cyde 520
Components of KMS 521
Technologies That Support Knowledge Management
12.5
The Group Decision-Making Process 524
The Benefits and Limitations of Groupwork
12.6
524
Supporting Groupwork with Computerized Systems
An Overview of Group Support Systems (GSS) 526
Groupware
527
Time/Place Framework
12.7
521
Making Decisions in Groups: Characteristics, Process,
Benefits, and Dysfunctions 523
Characteristics of Groupwork 523
527
Tools for Indirect Support of Decision Making
Groupware Tools
528
528
526
xvii
xviii Contents
Groupware
12.8
530
Collaborative Workflow 530
Web 2.0 530
Wikis 531
Collaborative Networks 531
Direct Computerized Support for Decision Making:
From Group Decision Support Systems to Group Support
Systems 532
Group Decision Support Systems (GOSS) 532
Group Support Systems 533
How GOSS (or GSS) Improve Groupwork 533
Facilities for GOSS 534
Chapter Highlights
535
Questions for Discussion
~
•
Key Terms
536
•
536
Exercises
536
END-OF-CHAPTER APPLICATION CASE Solving Crimes by Sharing
Digital Forensic Knowledge
References
537
539
Part V Big Data and Future Directions for Business
Analytics 541
Chapter 13 Big Data and Analytics 542
13.1 Opening Vignette: Big Data Meets Big Science at CERN
13.2 Definition of Big Data 546
The Vs That Define Big Data
13.3
547
~ APPLICATION CASE 13.1 Big Data Analytics Helps Luxottica Improve
Its Marketing Effectiveness 550
Fundamentals of Big Data Analytics 551
Business Problems Addressed by Big Data Analytics 554
~ APPLICATION CASE 13.2 Top 5 Investment Bank Achieves Single
Source of Truth
13.4
555
Big Data Technologies
MapReduce 557
556
Why Use Map Reduce? 558
Hadoop 558
How Does Hadoop Work? 558
Hadoop Technical Components 559
Hadoop: The Pros and Cons 560
NoSQL
~
13.5
562
APPLICATION CASE 13.3 eBay’s Big Data Solution
Data Scientist 565
Where Do Data Scientists Come From?
~
13.6
543
563
565
APPLICATION CASE 13.4 Big Data and Analytics in Politics
Big Data and Data Warehousing
Use Case(s) for Hadoop 570
Use Case(s) for Data Warehousing 571
569
568
Conte nts
13.7
13.8
13.9
The Gray Areas (Any One of the Two Would Do the Job)
Coexistence of Hadoop and Data Warehouse 572
Big Data Vendors 574
572
~
APPLICATION CASE 13.5 Dublin City Council Is Leveraging Big Data
to Reduce Traffic Congestion 575
~
APPLICATION CASE 13.6 Creditreform Boosts Credit Rating Quality
with Big Data Visual Analytics 580
Big Data and Stream Analytics 581
Stream Analytics Versus Perpetual Analytics 582
Critical Event Processing 582
Data Stream Mining 583
Applications of Stream Analytics 584
e-commerce 584
Telecommunications 584
~
APPLICATION CASE 13.7 Turning Machine-Generated Streaming Data
into Valuable Business Insights 585
Law Enforcement and Cyber Security
Power Industry 587
Financial Services 587
Health Sciences 587
Government 587
586
Chapter Highlights 588 • Key Terms 588
Questions for Discussion 588 • Exercises 589
~ END-OF-CHAPTER APPLICATION CASE Discovery Health Turns Big
Data into Better Healthcare
References
589
591
Chapter 14 Business Analytics: Emerging Trends and Future
Impacts 592
14.1 Opening Vignette: Oklahoma Gas and Electric Employs
14.2
Analytics to Promote Smart Energy Use 593
Location-Based Analytics for Organizations 594
Geospatial Analytics 594
~
APPLICATION CASE 14.1 Great Clips Employs Spatial Analytics to
Shave Time in Location Decisions 596
A Multimedia Exercise in Analytics Employing Geospatial Analytics
Real-Time Location Intelligence 598
~ APPLICATION CASE 14.2 Quiznos Targets Customers for Its
Sandwiches
14.3
Analytics Applications for Consumers
~
14.4
14.5
599
600
APPLICATION CASE 14.3 A Life Coach in Your Pocket
Recommendation Engines 603
Web 2.0 and Online Social Networking 604
Representative Characteristics of Web 2.0 605
Social Networking 605
A Definition and Basic Information 606
Implications of Business and Enterprise Social Networks 606
601
597
xix
xx
Contents
14.6
14.7
14.8
14.9
Cloud Computing and Bl 607
Service-Oriented DSS 608
Data-as-a-Service (DaaS) 608
Information-as-a-Service (Information on Demand) (laaS) 611
Analytics-as-a-Service (AaaS) 611
Impacts of Analytics in Organizations: An Overview 613
New Organizational Units 613
Restructuring Business Processes and Virtual Teams 614
The Impacts of ADS Systems 614
Job Satisfaction 614
Job Stress and Anxiety 614
Analytics’ Impact on Managers’ Activities and Their Performance 615
Issues of Legality, Privacy, and Ethics 616
Legal Issues 616
Privacy 617
Recent Technology Issues in Privacy and Analytics 618
Ethics in Decision Making and Support 619
An Overview of the Analytics Ecosystem 620
Analytics Industry Clusters 620
Data Infrastructure Providers 620
Data Warehouse Industry 621
Middleware Industry 622
Data Aggregators/Distributors 622
Analytics-Focused Software Developers 622
Reporting/Analytics 622
Predictive Analytics 623
Prescriptive Analytics 623
Application Developers or System Integrators: Industry Specific or General
Analytics User Organizations 625
Analytics Industry Analysts and Influencers 627
Academic Providers and Certification Agencies 628
624
Chapter Highlights 629 • Key Terms 629
Questions for Discussion 629 • Exercises 630
~ END·OF·CHAPTER APPLICATION CASE Southern States Cooperative
Optimizes Its Catalog Campaign 630
References 632
Glossary
Index
634
648
PREFACE
Analytics has become the technology driver of this decade. Companies such as IBM,
Oracle, Microsoft, and others are creating new organizational units focused on analytics
that help businesses become more effective and efficient in their operations. Decision
makers are using more computerized tools to support their work. Even consumers are
using analytics tools directly or indirectly to make decisions on routine activities such as
shopping, healthcare, and entertainment. The field of decision support systems (DSS)/
business intelligence (BI) is evolving rapidly to become more focused on innovative applications of data streams that were not even captured some time back, much less analyzed
in any significant way. New applications turn up daily in healthcare, sports, entertainment, supply chain management, utilities, and virtually every industry imaginable.
The theme of this revised edition is BI and analytics for enterprise decision support.
In addition to traditional decision support applications, this edition expands the reader’s
understanding of the various types of analytics by providing examples, products, services,
and exercises by discussing Web-related issues throughout the text. We highlight Web
intelligence/Web analytics, which parallel Bl/business analytics (BA) for e-commerce and
other Web applications. The book is supported by a Web site (pearsonhighered.com/
sharda) and also by an independent site at dssbibook.com. We will also provide links
to software tutorials through a special section of the Web site.
The purpose of this book is to introduce the reader to these technologies that are
generally called analytics but have been known by other names. The core technology
consists of DSS, BI, and various decision-making techniques. We use these terms interchangeably. This book presents the fundamentals of the techniques and the manner in
which these systems are constructed and used. We follow an EEE approach to introducing these topics: Exposure, Experience, and Explore. The book primarily provides
exposure to various analytics techniques and their applications. The idea is that a student
will be inspired to learn from how other organizations have employed analytics to make
decisions or to gain a competitive edge. We believe that such exposure to what is being
done with analytics and how it can be achieved is the key component of learning about
analytics. In describing the techniques, we also introduce specific software tools that can
be used for developing such applications. The book is not limited to any one software
tool , so the students can experience these techniques using any number of available
software tools. Specific suggestions are given in each chapter, but the student and the
professor are able to use this book with many different software tools. Our book’s companion Web site will include specific software guides, but students can gain experience
with these techniques in many different ways. Finally, we hope that this exposure and
experience enable and motivate readers to explore the potential of these techniques in
their own domain. To facilitate such exploration, we include exercises that direct them
to Teradata University Network and other sites as well that include team-oriented exercises where appropriate. We will also highlight new and innovative applications that we
learn about on the book’s companion Web sites.
Most of the specific improvements made in this tenth edition concentrate on three
areas: reorganization, content update, and a sharper focus. Despite the many changes, we
have preserved the comprehensiveness and user friendliness that have made the text a
market leader. We have also reduced the book’s size by eliminating older and redundant
material and by combining material that was not used by a majority of professors. At the
same time, we have kept several of the classical references intact. Finally, we present
accurate and updated material that is not available in any other text. We next describe the
changes in the tenth edition.
xxi
xxii
Preface
WHAT’S NEW IN THE TENTH EDITION?
With the goal of improving the text, this edition marks a major reorganization of the text
to reflect the focus on analytics. The last two editions transformed the book from the
traditional DSS to BI and fostered a tight linkage with the Teradata University Network
(TUN). This edition is now organized around three major types of analytics. The new
edition has many timely additions , and the dated content has been deleted. The following
major specific changes have been made:
• New organization. The book is now organized around three types of analytics:
descriptive, predictive, and prescriptive, a classification promoted by INFORMS. After
introducing the topics of DSS/ BI and analytics in Chapter 1 and covering the foundations of decision making and decision support in Chapter 2, the book begins with an
overview of data warehousing and data foundations in Chapter 3. This part then covers descriptive or reporting analytics, specifically, visualization and business performance measurement. Chapters 5-8 cover predictive analytics. Chapters 9-12 cover
prescriptive and decision analytics as well as other decision support systems topics.
Some of the coverage from Chapter 3-4 in previous editions will now be found in
the new Chapters 9 and 10. Chapter 11 covers expert systems as well as the new
rule-based systems that are commonly built for implementing analytics. Chapter 12
combines two topics that were key chapters in earlier editions-knowledge management and collaborative systems. Chapter 13 is a new chapter that introduces big data
and analytics. Chapter 14 concludes the book with discussion of emerging trends
and topics in business analytics, including location intelligence, mobile computing,
cloud-based analytics, and privacy/ethical considerations in analytics. This chapter
also includes an overview of the analytics ecosystem to help the user explore all of
the different ways one can participate and grow in the analytics environment. Thus,
the book marks a significant departure from the earlier editions in organization. Of
course, it is still possible to teach a course with a traditional DSS focus with this book
by covering Chapters 1-4, Chapters 9-12, and possibly Chapter 14.
• New chapters.
The following chapters have been added:
Chapter 8, “Web Analytics, Web Mining, and Social Analytics.” This chapter
covers the popular topics of Web analytics and social media analytics. It is an
almost entirely new chapter (95% new material).
Chapter 13, “Big Data and Analytics.” This chapter introduces the hot topics of
Big Data and analytics. It covers the basics of major components of Big Data techniques and charcteristics. It is also a new chapter (99% new material) .
Chapter 14, “Business Analytics: Emerging Trends and Future Impacts.”
This chapter examines several new phenomena that are already changing or are
likely to change analytics. It includes coverage of geospatial in analytics, locationbased analytics applications, consumer-oriented analytical applications, mobile platforms , and cloud-based analytics. It also updates some coverage from the previous
edition on ethical and privacy considerations. It concludes with a major discussion
of the analytics ecosystem (90% new material).
• Streamlined coverage. We have made the book shorter by keeping the most
commonly used content. We also mostly eliminated the preformatted online content. Instead, we will use a Web site to provide updated content and links on a
regular basis. We also reduced the number of references in each chapter.
• Revamped author team. Building upon the excellent content that has been
prepared by the authors of the previous editions (Turban, Aronson, Liang, King,
Sharda, and Delen), this edition was revised by Ramesh Sharda and Dursun Delen.
Preface xxiii
Both Ramesh and Dursun have worked extensively in DSS and analytics and have
industry as well as research experience.
• A live-update Web site. Adopters of the textbook will have access to a Web site that
will include links to news stories, software, tutorials, and even YouTube videos related
to topics covered in the book. This site will be accessible at http://dssbibook.com.
• Revised and updated content. Almost all of the chapters have new opening
vignettes and closing cases that are based on recent stories and events. In addition,
application cases throughout the book have been updated to include recent examples of applications of a specific technique/model. These application case stories
now include suggested questions for discussion to encourage class discussion as
well as further exploration of the specific case and related materials . New Web site
links have been added throughout the book. We also deleted many older product
links and references. Finally, most chapters have new exercises, Internet assignments, and discussion questions throughout.
Specific changes made in chapters that have been retained from the previous editions are summarized next:
Chapter 1, “An Overview of Business Intelligence, Analytics, and Decision
Support,” introduces the three types of analytics as proposed by INFORMS: descriptive,
predictive, and prescriptive analytics. A noted earlier, this classification is used in guiding
the complete reorganization of the book itself. It includes about 50 percent new material.
All of the case stories are new.
Chapter 2, “Foundations and Technologies for Decision Making,” combines material from earlier Chapters 1, 2, and 3 to provide a basic foundation for decision making in
general and computer-supported decision making in particular. It eliminates some duplication that was present in Chapters 1-3 of the previous editions. It includes 35 percent
new material. Most of the cases are new.
Chapter 3, “Data Warehousing”
• 30 percent new material, including the cases
• New opening case
• Mostly new cases throughout
• NEW: A historic perspective to data warehousing-how did we get here?
• Better coverage of multidimensional modeling (star schema and snowflake schema)
• An updated coverage on the future of data warehousing
Chapter 4, “Business Reporting, Visual Analytics, and Business Performance
Management”
• 60 percent of the material is new-especially in visual analytics and reporting
• Most of the cases are new
Chapter 5, “Data Mining”
• 25 percent of the material is new
• Most of the cases are new
Chapter 6, “Techniques for Predictive Modeling”
• 55 percent of the material is new
• Most of the cases are new
• New sections on SVM and kNN
Chapter 7, “Text Analytics, Text Mining, and Sentiment Analysis”
• 50 percent of the material is new
• Most of the cases are new
• New section (1/ 3 of the chapter) on sentiment analysis
xxiv Preface
Chapter 8, “Web Analytics, Web Mining, and Social Analytics” (New Chapter)
• 95 percent of the material is new
Chapter 9, “Model-Based Decision Making: Optimization and Multi-Criteria Systems”
• All new cases
• Expanded coverage of analytic hierarchy process
• New examples of mixed-integer programming applications and exercises
• About 50 percent new material
In addition, all the Microsoft Excel-related coverage has been updated to work with
Microsoft Excel 2010.
Chapter 10, “Modeling and Analysis: Heuristic Search Methods and Simulation”
• This chapter now introduces genetic algorithms and various types of simulation
models
• It includes new coverage of other types of simulation modeling such as agent-based
modeling and system dynamics modeling
• New cases throughout
• About 60 percent new material
Chapter 11, “Automated Decision Systems and Expert Systems”
• Expanded coverage of automated decision systems including examples from the
airline industry
• New examples of expert systems
• New cases
• About 50 percent new material
Chapter 12, “Knowledge Management and Collaborative Systems”
• Significantly condensed coverage of these two topics combined into one chapter
• New examples of KM applications
• About 25 percent new material
Chapters 13 and 14 are mostly new chapters, as described earlier.
We have retained many of the enhancements made in the last editions and updated
the content. These are summarized next:
• Links to Teradata University Network (TUN). Most chapters include new links
to TUN (teradatauniversitynetwork.com). We encourage the instructors to register and join teradatauniversitynetwork.com and explore various content available
through the site. The cases, white papers, and software exercises available through
TUN will keep your class fresh and timely.
• Book title. As is already evident, the book’s title and focus have changed
substantially.
• Software support. The TUN Web site provides software support at no charge.
It also provides links to free data mining and other software. In addition, the site
provides exercises in the use of such software.
THE SUPPLEMENT PACKAGE: PEARSONHIGHERED.COM/SHARDA
A comprehensive and flexible technology-support package is available to enhance the
teaching and learning experience. The following instructor and student supplements are
available on the book’s Web site, pearsonhighered.com/sharda:
• Instructor’s Manual. The Instructor’s Manual includes learning objectives for the
entire course and for each chapter, answers to the questions and exercises at the end
of each chapter, and teaching suggestions (including instructions for projects). The
Instructor’s Manual is available on the secure faculty section of pearsonhighered
.com/sharda.
Preface
• Test Item File and TestGen Software. The Test Item File is a comprehensive
collection of true/false, multiple-choice, fill-in-the-blank, and essay questions. The
questions are rated by difficulty level, and the answers are referenced by book page
number. The Test Item File is available in Microsoft Word and in TestGen. Pearson
Education’s test-generating software is available from www.pearsonhighered.
com/ire. The software is PC/MAC compatible and preloaded with all of the Test
Item File questions. You can manually or randomly view test questions and dragand-drop to create a test. You can add or modify test-bank questions as needed. Our
TestGens are converted for use in BlackBoard, WebCT, Moodie, D2L, and Angel.
These conversions can be found on pearsonhighered.com/sharda. The TestGen
is also available in Respondus and can be found on www.respondus.com.
• PowerPoint slides. PowerPoint slides are available that illuminate and build
on key concepts in the text. Faculty can download the PowerPoint slides from
pearsonhighered.com/sharda.
ACKNOWLEDGMENTS
Many individuals have provided suggestions and criticisms since the publication of the
first edition of this book. Dozens of students participated in class testing of various chapters, software, and problems and assisted in collecting material. It is not possible to name
everyone who participated in this project, but our thanks go to all of them. Certain individuals made significant contributions, and they deserve special recognition.
First, we appreciate the efforts of those individuals who provided formal reviews of
the first through tenth editions (school affiliations as of the date of review):
Robert Blanning, Vanderbilt University
Ranjit Bose, University of New Mexico
Warren Briggs, Suffolk University
Lee Roy Bronner, Morgan State University
Charles Butler, Colorado State University
Sohail S. Chaudry, University of Wisconsin-La Crosse
Kathy Chudoba, Florida State University
Wingyan Chung, University of Texas
Woo Young Chung, University of Memphis
Paul “Buddy” Clark, South Carolina State University
Pi’Sheng Deng, California State University-Stanislaus
Joyce Elam, Florida International University
Kurt Engemann, Iona College
Gary Farrar, Jacksonville University
George Federman, Santa Clara City College
Jerry Fjermestad, New Jersey Institute of Technology
Joey George, Florida State University
Paul Gray, Claremont Graduate School
Orv Greynholds, Capital College (Laurel, Maryland)
Martin Grossman, Bridgewater State College
Ray Jacobs, Ashland University
Leonard Jessup , Indiana University
Jeffrey Johnson , Utah State University
Jahangir Karimi, University of Colorado Denver
Saul Kassicieh, University of New Mexico
Anand S. Kunnathur, University of Toledo
XXV
xxvi Preface
Shao-ju Lee, California State University at Northridge
Yair Levy, Nova Southeastern University
Hank Lucas, New York University
Jane Mackay, Texas Christian University
George M. Marakas, University of Maryland
Dick Mason, Southern Methodist University
Nick McGaughey, San Jose State University
Ido Millet, Pennsylvania State University-Erie
Benjamin Mittman, Northwestern University
Larry Moore, Virginia Polytechnic Institute and State University
Simitra Mukherjee, Nova Southeastern University
Marianne Murphy, Northeastern University
Peter Mykytyn, Southern Illinois University
Natalie Nazarenko, SUNY College at Fredonia
Souren Paul, Southern Illinois University
Joshua Pauli, Dakota State University
Roger Alan Pick, University of Missouri-St. Louis
W. “RP” Raghupaphi, California State University-Chico
Loren Rees, Virginia Polytechnic Institute and State University
David Russell, Western New England College
Steve Ruth, George Mason University
Vartan Safarian, Winona State University
Glenn Shephard, San Jose State University
Jung P. Shim, Mississippi State University
Meenu Singh, Murray State University
Randy Smith, University of Virginia
James T.C. Teng, University of South Carolina
John VanGigch, California State University at Sacramento
David Van Over, University of Idaho
Paul J.A. van Vliet, University of Nebraska at Omaha
B. S. Vijayaraman, University of Akron
Howard Charles Walton, Gettysburg College
Diane B. Walz, University of Texas at San Antonio
Paul R. Watkins, University of Southern California
Randy S. Weinberg, Saint Cloud State University
Jennifer Williams, University of Southern Indiana
Steve Zanakis, Florida International University
Fan Zhao, Florida Gulf Coast University
Several individuals contributed material to the text or the supporting material.
Susan Baxley and Dr. David Schrader of Teradata provided special help in identifying
new TUN content for the book and arranging permissions for the same. Peter Horner,
editor of OR/MS Today, allowed us to summarize new application stories from OR/
MS Today and Analytics Magazine. We also thank INFORMS for their permission to
highlight content from Inteifaces. Prof. Rick Wilson contributed some examples and
exercise questions for Chapter 9. Assistance from Natraj Ponna, Daniel Asamoah, Amir
Hassan-Zadeh, Kartik Dasika, Clara Gregory, and Amy Wallace (all of Oklahoma State
University) is gratefully acknowledged for this edition. We also acknowledge Narges
Kasiri (Ithaca College) for the write-up on system dynamics modeling and Jongswas
Chongwatpol (NIDA, Thailand) for the material on SIMIO software. For the previous edition, we acknowledge the contributions of Dave King QDA Software Group, Inc.) and
Preface
Jerry Wagner (University of Nebraska-Omaha). Major contributors for earlier editions
include Mike Gou! (Arizona State University) and Leila A. Halawi (Bethune-Cookman
College), who provided material for the chapter on data warehousing; Christy Cheung
(Hong Kong Baptist University), who contributed to the chapter on knowledge management; Linda Lai (Macau Polytechnic University of China); Dave King QDA Software
Group, Inc.); Lou Frenzel, an independent consultant whose books Crash Course in
Artificial Intelligence and Expert Systems and Understanding of Expert Systems (both
published by Howard W. Sams, New York, 1987) provided material for the early editions; Larry Medsker (American University), who contributed substantial material on neural networks; and Richard V. McCarthy (Quinnipiac University), who performed major
revisions in the seventh edition.
Previous editions of the book have also benefited greatly from the efforts of many
individuals who contributed advice and interesting material (such as problems), gave
feedback on material, or helped with class testing. These individuals are Warren Briggs
(Suffolk University), Frank DeBalough (University of Southern California), Mei-Ting
Cheung (University of Hong Kong), Alan Dennis (Indiana University), George Easton
(San Diego State University), Janet Fisher (California State University, Los Angeles),
David Friend (Pilot Software, Inc.), the late Paul Gray (Claremont Graduate School),
Mike Henry (OSU), Dustin Huntington (Exsys , Inc.), Subramanian Rama Iyer (Oklahoma
State University), Angie Jungermann (Oklahoma State University), Elena Karahanna
(The University of Georgia), Mike McAulliffe (The University of Georgia), Chad Peterson
(The University of Georgia), Neil Rabjohn (York University), Jim Ragusa (University of
Central Florida) , Alan Rowe (University of Southern California), Steve Ruth (George
Mason University), Linus Schrage (University of Chicago), Antonie Stam (University of
Missouri), Ron Swift (NCR Corp.), Merril Warkentin (then at Northeastern University),
Paul Watkins (The University of Southern California), Ben Mortagy (Claremont Graduate
School of Management), Dan Walsh (Bellcore), Richard Watson (The University of
Georgia), and the many other instructors and students who have provided feedback.
Several vendors cooperated by providing development and/or demonstration software: Expert Choice, Inc. (Pittsburgh, Pennsylvania), Nancy Clark of Exsys,
Inc. (Albuquerque, New Mexico), Jim Godsey of GroupSystems, Inc. (Broomfield,
Colorado), Raimo Hamalainen of Helsinki University of Technology, Gregory PiatetskyShapiro of KDNuggets .com, Logic Programming Associates (UK), Gary Lynn of
NeuroDimension Inc. (Gainesville, Florida), Palisade Software (Newfield, New York),
Jerry Wagner of Planners Lab (Omaha, Nebraska) , Promised Land Technologies (New
Haven, Connecticut), Salford Systems (La Jolla , California), Sense Networks (New York,
New York), Gary Miner of StatSoft, Inc. (Tulsa, Oklahoma), Ward Systems Group, Inc.
(Frederick, Maryland), Idea Fisher Systems, Inc. (Irving, California), and Wordtech
Systems (Orinda, California).
Special thanks to the Teradata University Network and especially to Hugh Watson,
Michael Gou!, and Susan Baxley, Program Director, for their encouragement to tie this
book with TUN and for providing useful material for the book.
Many individuals helped us with administrative matters and editing, proofreading,
and preparation. The project began with Jack Repcheck (a former Macmillan editor), who
initiated this project with the support of Hank Lucas (New York University). Judy Lang
collaborated with all of us, provided editing, and guided us during the entire project
through the eighth edition.
Finally, the Pearson team is to be commended: Executive Editor Bob Horan, who
orchestrated this project; Kitty Jarrett, who copyedited the manuscript; and the production team, Tom Benfatti at Pearson, George and staff at Integra Software Services, who
transformed the manuscript into a book.
xxvii
xxviii Preface
We would like to thank all these individuals and corporations. Without their help,
the creation of this book would not have been possible. Ramesh and Dursun want to
specifically acknowledge the contributions of previous coauthors Janine Aronson, David
King, and T. P. Liang, whose original contributions constitute significant components of
the book.
R.S.
D.D.
E.T
Note that Web site URLs are dynamic. As this book went to press, we verified that all the cited Web sites were
active and valid. Web sites to which we refer in the text sometimes change or are discontinued because companies change names, are bought or sold, merge, or fail. Sometimes Web sites are down for maintenance, repair,
or redesign. Most organizations have dropped the initial “www” designation for their sites, but some still use
it. If you have a problem connecting to a Web site that we mention, please be patient and simply run a Web
search to try to identify the new site. Most times, the new site can be found quickly. Some sites also require a
free registration before allowing you to see the content. We apologize in advance for this inconvenience.
ABOUT THE AUTHORS
Ramesh Sharda (M.B.A., Ph.D., University of Wisconsin-Madison) is director of the
Ph.D. in Business for Executives Program and Institute for Research in Information
Systems (IRIS), ConocoPhillips Chair of Management of Technology, and a Regents
Professor of Management Science and Information Systems in the Spears School of
Business at Oklahoma State University (OSU) . About 200 papers describing his research
have been published in major journals, including Operations Research, Management
Science, Information Systems Research, Decision Support Systems, and journal of MIS.
He cofounded the AIS SIG on Decision Support Systems and Knowledge Management
(SIGDSS). Dr. Sharda serves on several editorial boards, including those of INFORMS
journal on Computing, Decision Support Systems, and ACM Transactions on Management
Information Systems. He has authored and edited several textbooks and research books
and serves as the co-editor of several book series (Integrated Series in Information
Systems, Operations Research/ Computer Science Interfaces, and Annals of Information
Systems) with Springer. He is also currently serving as the executive director of the
Teradata University Network. His current research interests are in decision support systems, business analytics, and technologies for managing information overload.
Dursun Delen (Ph.D., Oklahoma State University) is the Spears and Patterson Chairs in
Business Analytics, Director of Research for the Center for Health Systems Innovation,
and Professor of Management Science and Information Systems in the Spears School of
Business at Oklahoma State University (OSU). Prior to his academic career, he worked
for a privately owned research and consultancy company, Knowledge Based Systems
Inc. , in College Station, Texas, as a research scientist for five years, during which he led
a number of decision support and other information systems-related research projects
funded by federal agencies such as DoD, NASA, NIST, and DOE. Dr. Delen’s research has
appeared in major journals including Decision Support Systems, Communications of the
ACM, Computers and Operations Research, Computers in Industry, journal of Production
Operations Management, Artificial Intelligence in Medicine, and Expert Systems with
Applications, among others. He recently published four textbooks: Advanced Data Mining
Techniques with Springer, 2008; Decision Support and Business Intelligence Systems with
Prentice Hall, 2010; Business Intelligence: A Managerial Approach , with Prentice Hall,
2010; and Practical Text Mining, with Elsevier, 2012. He is often invited to national and
international conferences for keynote addresses on topics related to data/ text mining,
business intelligence, decision support systems, and knowledge management. He served
as the general co-chair for the 4th International Conference on Network Computing and
Advanced Information Management (September 2-4, 2008, in Seoul, South Korea) and
regularly chairs tracks and mini-tracks at various information systems conferences. He is
the associate editor-in-chief for International journal of Experimental Algorithms, associate editor for International journal of RF Technologies and journal of Decision Analytics,
and is on the editorial boards of five other technical journals. His research and teaching
interests are in data and text mining, decision support systems, knowledge management,
business intelligence, and enterprise modeling.
Efraim Turban (M.B.A., Ph .D., University of California, Berkeley) is a visiting scholar
at the Pacific Institute for Information System Management, University of Hawaii. Prior
to this, he was on the staff of several universities, including City University of Hong
Kong; Lehigh University; Florida International University; California State University, Long
xxix
XXX
About the Authors
Beach; Eastern Illinois University; and the University of Southern California. Dr. Turban
is the author of more than 100 refereed papers published in leading journals, such as
Management Science, MIS Quarterly, and Decision Support Systems. He is also the author
of 20 books, including Electronic Commerce: A Managerial Perspective and Information
Technology for Management. He is also a consultant to major corporations worldwide.
Dr. Turban’s current areas of interest are Web-based decision support systems, social
commerce, and collaborative decision making.
P
A
R
T
Decision Making and Analytics
An Overview
LEARNING OBJECTIVES FOR PART I
• Understand the need for business analytics
• Understand the foundations and key issues of
managerial decision making
• Learn the major frameworks of computerized
decision support: analytics, decision support
systems (DSS), and business intelligence (BI)
• Understand the major categories and
applications of business analytics
This book deals with a collection of computer technologies that support managerial work-essentially,
decision making. These technologies have had a profound impact on corporate strategy, performance, and competitiveness. These techniques broadly encompass analytics, business intelligence,
and decision support systems, as shown throughout the book. In Part I, we first provide an overview
of the whole book in one chapter. We cover several topics in this chapter. The first topic is managerial
decision making and its computerized support; the second is frameworks for decision support. We
then introduce business analytics and business intelligence. We also provide examples of applications
of these analytical techniques, as well as a preview of the entire book. The second chapter within
Part I introduces the foundational methods for decision making and relates these to computerized
decision support. It also covers the components and technologies of decision support systems.
1
An Overview of Business Intelligence,
Analytics, and Decision Support
LEARNING OBJECTIVES
• Understand today’s turbulent business
environment and describe how
organizations survive and even excel in
such an environment (solving problems
and exploiting opportunities)
• Understand the need for computerized
support of managerial decision making
• Understand an early framework for
managerial decision making
• Learn the conceptual foundations of
the decision support systems (DSS 1)
methodology
• Describe the business intelligence (BI)
methodology and concepts and relate
them to DSS
• Understand the various types of analytics
• List the major tools of computerized
decision support
T
he business environment (climate) is constantly changing, and it is becoming more
and more complex. Organizations, private and public, are under pressures that
force them to respond quickly to changing conditions and to be innovative in the
way they operate. Such activities require organizations to be agile and to make frequent
and quick strategic, tactical, and operational decisions, some of which are very complex.
Making such decisions may require considerable amounts of relevant data, information,
and knowledge. Processing these, in the framework of the needed decisions, must be
done quickly, frequently in real time, and usually requires some computerized support.
This book is about using business analytics as computerized support for managerial decision making. It concentrates on both the theoretical and conceptual foundations of decision support, as well as on the commercial tools and techniques that are
available. This introductory chapter provides more details of these topics as well as an
overview of the book. This chapter has the following sections:
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 acronym DSS is treated as both singular and plural throughout this book. Similarly, other acronyms, such
as MIS and GSS, designate both plural and singular forms. This is also true of the word analytics.
2
Chapter 1 • An Overview of Business Intelligence, Analytics, and Decision Support
1.3 Managerial Decision Making
7
1.4 Information Systems Support for Decision Making 9
1.5 An Early Framework for Computerized Decision Support
1.6 The Concept of Decision Support Systems (DSS) 13
11
1. 7 A Framework for Business Intelligence (BI) 14
1.8 Business Analytics Overview 19
1.9 Brief Introduction to Big Data Analytics 27
1.10 Plan of the Book 29
1.11 Resources, Links, and the Teradata University Network Connection
1.1
31
OPENING VIGNETTE: Magpie Sensing Employs
Analytics to Manage a Vaccine Supply Chain
Effectively and Safely
Cold chain in healthcare is defined as the temperature-controlled supply chain involving a
system of transporting and storing vaccines and pharmaceutical drugs. It consists of three
major components-transport and storage equipment, trained personnel, and efficient
management procedures. The majority of the vaccines in the cold chain are typically maintained at a temperature of 35–46 degrees Fahrenheit [2-8 degrees Centigrade]. Maintaining
cold chain integrity is extremely important for healthcare product manufacturers.
Especially for the vaccines, improper storage and handling practices that compromise
vaccine viability prove a costly, time-consuming affair. Vaccines must be stored properly
from manufacture until they are available for use. Any extreme temperatures of heat or
cold will reduce vaccine potency; such vaccines, if administered, might not yield effective
results or could cause adverse effects.
Effectively maintaining the temperatures of storage units throughout the healthcare
supply chain in real time-Le., beginning from the gathering of the resources, manufacturing, distribution, and dispensing of the products-is the most effective solution desired
in the cold chain. Also, the location-tagged real-time environmental data about the storage
units helps in monitoring the cold chain for spoiled products. The chain of custody can
be easily identified to assign product liability.
A study conducted by the Centers for Disease Control and Prevention ( CDC) looked at
the handling of cold chain vaccines by 45 healthcare providers around United States and
reported that three-quarters of the providers experienced serious cold chain violations.
A WAY TOWARD A POSSIBLE SOLUTION
Magpie Sensing, a start-up project under Ebers Smith and Douglas Associated LLC, provides a suite of cold chain monitoring and analysis technologies for the healthcare industry. It is a shippable, wireless temperature and humidity monitor that provides real-time,
location-aware tracking of cold chain products during shipment. Magpie Sensing’s solutions rely on rich analytics algorithms that leverage the data gathered from the monitoring devices to improve the efficiency of cold chain processes and predict cold storage
problems before they occur.
Magpie sensing applies all three types of analytical techniques-descriptive, predictive, and prescriptive analytics-to tum the raw data returned from the monitoring devices
into actionable recommendations and warnings.
The properties of the cold storage system, which include the set point of the storage
system’s thermostat, the typical range of temperature values in the storage system, and
3
4
Part I • Decision Making and Analytics: An Oveiview
the duty cycle of the system’s compressor, are monitored and reported in real time. This
information helps trained personnel to ensure that the storage unit is properly configured
to store a particular product. All the temperature information is displayed on a Web dashboard that shows a graph of the temperature inside the specific storage unit.
Based on information derived from the monitoring devices, Magpie’s predictive analytic algorithms can determine the set point of the storage unit’s thermostat and alert the
system’s users if the system is incorrectly configured, depending upon the various types
of products stored. This offers a solution to the users of consumer refrigerators where
the thermostat is not temperature graded. Magpie’s system also sends alerts about possible temperature violations based on the storage unit’s average temperature and subsequent compressor cycle runs, which may drop the temperature below the freezing point.
Magpie’s predictive analytics further report possible human errors, such as failure to shut
the storage unit doors or the presence of an incomplete seal, by analyzing the temperature trend and alerting users via Web interface, text message, or audible alert before the
temperature bounds are actually violated. In a similar way, a compressor or a power
failure can be detected; the estimated time before the storage unit reaches an unsafe temperature also is reported, which prepares the users to look for backup solutions such as
using dry ice to restore power.
In addition to predictive analytics, Magpie Sensing’s analytics systems can provide
prescriptive recommendations for improving the cold storage processes and business
decision making. Prescriptive analytics help users dial in the optimal temperature setting,
which helps to achieve the right balance between freezing and spoilage risk; this, in turn,
provides a cushion-time to react to the situation before the products spoil. Its prescriptive
analytics also gather useful meta-information on cold storage units, including the times of
day that are busiest and periods where the system’s doors are opened, which can be used
to provide additional design plans and institutional policies that ensure that the system is
being properly maintained and not overused.
Furthermore, prescriptive analytics can be used to guide equipment purchase decisions by constantly analyzing the performance of current storage units. Based on the
storage system’s efficiency, decisions on distributing the products across available storage
units can be made based on the product’s sensitivity.
Using Magpie Sensing’s cold chain analytics, additional manufacturing time and
expenditure can be eliminated by ensuring that product safety can be secured throughout
the supply chain and effective products can be administered to the patients. Compliance
with state and federal safety regulations can be better achieved through automatic data
gathering and reporting about the products involved in the cold chain.
QUESTIONS FOR THE OPENING VIGNETTE
1. What information is provided by the descriptive analytics employed at Magpie
Sensing?
2. What type of support is provided by the predictive analytics employed at Magpie
Sensing?
3. How does prescriptive analytics help in business decision making?
4. In what ways can actionable information be reported in real time to concerned
users of the system?
5. In what other situations might real-time monitoring applications be needed?
WHAT WE CAN LEARN FROM THIS VIGNETIE
This vignette illustrates how data from a business process can be used to generate insights
at various levels. First, the graphical analysis of the data (termed reporting analytics) allows
Chapter 1 • An Overview of Business Intelligence, Analytics, and Decision Support
users to get a good feel for the situation. Then, additional analysis using data mining
techniques can be used to estimate what future behavior would be like. This is the domain
of predictive analytics. Such analysis can then be taken to create specific recommendations
for operators. This is an example of what we call prescriptive analytics. Finally, this opening vignette also suggests that innovative applications of analytics can create new business
ventures. Identifying opportunities for applications of analytics and assisting with decision
making in specific domains is an emerging entrepreneurial opportunity.
Sources: Magpiesensing.com, “Magpie Sensing Cold Chain Analytics and Monitoring,” magpiesensing.com/
wp-content/uploads/2013/01/ColdChainAnalyticsMagpieSensing-Whitepaper.pdf (accessed July 2013);
Centers for Disease Control and Prevention, Vaccine Storage and Handling, http://www.cdc.gov/vaccines/pubs/
pinkbook/vac-storage.html#storage (accessed July 2013); A. Zaleski, “Magpie Analytics System Tracks ColdChain Products to Keep Vaccines, Reagents Fresh” (2012), technicallybaltimore.com/profiles/startups/magpieanalytics-system-track.s-cold-chain-products-to-keep-vaccines-reagents-fresh (accessed February 2013).
1.2
CHANGING BUSINESS ENVIRONMENTS AND COMPUTERIZED
DECISION SUPPORT
The opening vignette illustrates how a company can employ technologies to make sense
of data and make better decisions. Companies are moving aggressively to computerized
support of their operations. To understand why companies are embracing computerized support, including business intelligence, we developed a model called the Business
Pressures-Responses-Support Model, which is shown in Figure 1.1.
The Business Pressures-Responses-Support Model
The Business Pressures-Responses-Support Model, as its name indicates, has three components: business pressures that result from today’s business climate, responses (actions
taken) by companies to counter the pressures (or to take advantage of the opportunities
available in the environment), and computerized support that facilitates the monitoring
of the environment and enhances the response actions taken by organizations.
Decisions and
Support
Organization
Responses
Business
Environmental Factors
Strategy
Analyses
.
Predictions
Decisions
Partners’ collaboration
Globalization
Pressures
Real-time response
i i i
Agility
Integrated
Increased productivity
computerized
Competition
New vendors
decision
Etc.
New business models
support
Customer demand
Government regulations
Market conditions
Opportunities
Etc.
Business
intelligence
FIGURE 1.1
The Business Pressures-Responses-Support Model.
5
6
Part I • Decision Making and Analytics: An Overview
The environment in which organizations operate today
is becoming more and more complex. This complexity creates opportunities on the one
hand and problems on the other. Take globalization as an example. Today, you can easily find suppliers and customers in many countries, which means you can buy cheaper
materials and sell more of your products and services; great opportunities exist. However,
globalization also means more and stronger competitors. Business environment factors
can be divided into four major categories: markets, consumer demands, technology, and
societal. These categories are summarized in Table 1.1.
Note that the intensity of most of these factors increases with time, leading to
more pressures, more competition, and so on. In addition, organizations and departments
within organizations face decreased budgets and amplified pressures from top managers
to increase performance and profit. In this kind of environment, managers must respond
quickly, innovate, and be agile. Let’s see how they do it.
THE BUSINESS ENVIRONMENT
ORGANIZATIONAL RESPONSES: BE REACTIVE, ANTICIPATIVE, ADAPTIVE, AND PROACTIVE
Both private and public organizations are aware of today’s business environment and
pressures. They use different actions to counter the pressures. Vodafone New Zealand
Ltd (Krivda, 2008), for example, turned to BI to improve communication and to support
executives in its effort to retain existing customers and increase revenue from these customers. Managers may take other actions, including the following:
•
•
•
•
•
•
Employ strategic planning.
Use new and innovative business models.
Restructure business processes.
Participate in business alliances.
Improve corporate information systems.
Improve partnership relationships.
TABLE 1.1
Business Environment Factors That Create Pressures on Organizations
Factor
Description
Markets
Strong competition
Expanding global markets
Booming electronic markets on the Internet
Innovative marketing methods
Opportunities for outsourcing with IT support
Need for real-time, on-demand transactions
Consumer demands
Desire for customization
Desire for quality, diversity of products, and speed of delivery
Customers getting powerful and less loyal
Technology
More innovations, new products, and new services
Increasing obsolescence rate
Increasing information overload
Social networking, Web 2.0 and beyond
Societal
Growing government regulations and deregulation
Workforce more diversified, older, and composed of more women
Prime concerns of homeland security and terrorist attacks
Necessity of Sarbanes-Oxley Act and other reporting-related legislation
Increasing social responsibility of companies
Greater emphasis on sustainability
Chapter 1 • An Overview of Business Intelligence, Analytics, and Decision Support
•
•
•
•
•
•
•
•
•
Encourage innovation and creativity.
Improve customer service and relationships.
Employ social media and mobile platforms for e-commerce and beyond.
Move to make-to-order production and on-demand manufacturing and services.
Use new IT to improve communication, data access (discovery of information), and
collaboration.
Respond quickly to competitors’ actions (e.g., in pricing, promotions, new products
and services).
Automate many tasks of white-collar employees.
Automate certain decision processes, especially those dealing with customers.
Improve decision making by employing analytics.
Many, if not all, of these actions require some computerized support. These and other
response actions are frequently facilitated by computerized decision support (DSS).
CLOSING THE STRATEGY GAP One of the major objectives of computerized decision
support is to facilitate closing the gap between the current performance of an organization and its desired performance, as expressed in its mission, objectives, and goals,
and the strategy to achieve them. In order to understand why computerized support
is needed and how it is provided, especially for decision-making support, let’s look at
managerial decision making.
SECTION 1.2 REVIEW QUESTIONS
1. List the components of and explain the Business Pressures-Responses-Support
Model.
2. What are some of the major factors in today’s business environment?
3. What are some of the major response activities that organizations take?
1.3
MANAGERIAL DECISION MAKING
Management is a process by which organizational goals are achieved by using
resources . The resources are considered inputs, and attainment of goals is viewed as
the output of the process. The degree of success of the organization and the manager
is often measured by the ratio of outputs to inputs. This ratio is an indication of the
organization’s productivity, which is a reflection of the organizational and managerial
pe,fonnance.
The level of productivity or the success of management depends on the performance of managerial functions, such as planning, organizing, directing, and controlling. To perform their functions , managers engage in a continuous process of making
decisions. Making a decision means selecting the best alternative from two or more
solutions.
The Nature of Managers’ Work
Mintzberg’s (2008) classic study of top managers and several replicated studies suggest
that managers perform 10 major roles that can be classified into three major categories:
interpersonal, infonnational, and decisional (see Table 1.2).
To perform these roles, managers need information that is delivered efficiently and
in a timely manner to personal computers (PCs) on their desktops and to mobile devices.
This information is delivered by networks, generally via Web technologies.
In addition to obtaining information necessary to better perform their roles, managers use computers directly to support and improve decision making, which is a key task
7
8
Part I • Decision Making and Analytics: An Overview
TABLE 1.2
Mintzberg’s 10 Managerial Roles
Role
Interpersonal
Figurehead
Description
Is symbolic head; obliged to perform a number of routine duties of a
legal or social nature
Leader
Is responsible for the motivation and activation of subordinates;
responsible for staffing, training, and associated duties
Liaison
Maintains self-developed network of outside contacts and informers
who provide favors and information
Informational
Monitor
Seeks and receives a wide variety of special information (much of it
current) to develop a thorough understanding of the organization
and environment; emerges as the nerve center of the organization’s
internal and external information
Disseminator
Transmits information received from outsiders or from subordinates to
members of the organization; some of this information is factual,
and some involves interpretation and integration
Spokesperson
Transmits information to outsiders about the organization’s plans,
policies, actions, results, and so forth; serves as an expert on the
organization’s industry
Decisional
Entrepreneur
Disturbance handler
Resource allocator
Negotiator
Searches the organization and its environment for opportunities and
initiates improvement projects to bring about change; supervises
design of certain projects
Is responsible for corrective action when the organization faces
important, unexpected disturbances
Is responsible for the allocation of organizational resources of all
kinds; in effect, is responsible for the making or approval of all
significant organizational decisions
Is responsible for representing the organization at major negotiations
Sources: Compiled from H. A. Mintzberg, The Nature of Managerial Work. Prentice Hall, Englew ood Cliffs,
NJ, 1980; and H. A. Mintzberg, The Rise and Fall of Strategic Planning. The Free Press, New York, 1993.
that is part of most of these roles. Many managerial activities in all roles revolve around
decision making. Managers, especially those at high managerial levels, are primarily decision makers. We review the decision-making process next but will study it in more detail
in the next chapter.
The Decision-Making Process
For years, managers considered decision making purely an art-a talent acquired over a
long period through experience (i.e., learning by trial-and-error) and by using intuition.
Management was considered an art because a variety of individual styles could be used
in approaching and successfully solving the same types of managerial problems. These
styles were often based on creativity, judgment, intuition, and experience rather than
on systematic quantitative methods grounded in a scientific approach. However, recent
research suggests that companies with top managers who are more focused on persistent
work (almost dullness) tend to outperform those with leaders whose main strengths are
interpersonal communication skills (Kaplan et al., 2008; Brooks, 2009). It is more important to emphasize methodical, thoughtful, analytical decision making rather than flashiness and interpersonal communication skills.
Chapter 1 • An Overview of Business Intelligence, Analytics, and Decision Support
Managers usually make decisions by following a four-step process Cwe learn more
about these in Chapter 2):
1. Define the problem (i.e., a decision situation that may deal with some difficulty or
with an opportunity).
2. Construct a model that describes the real-world problem.
3. Identify possible solutions to the modeled problem and evaluate the solutions.
4. Compare, choose, and recommend a potential solution to the problem.
To follow this process, one must make sure that sufficient alternative solutions are
being considered, that the consequences of using these alternatives can be reasonably
predicted, and that comparisons are done properly. However, the environmental factors
listed in Table 1.1 make such an evaluation process difficult for the following reasons:
• Technology, information systems, advanced search engines, and globalization result
in more and more alternatives from which to choose.
• Government regulations and the need for compliance, political instability and terrorism, competition, and changing consumer demands produce more uncertainty,
making it more difficult to predict consequences and the future.
• Other factors are the need to make rapid decisions, the frequent and unpredictable
changes that make trial-and-error learning difficult, and the potential costs of making
mistakes.
• These environments are growing more complex every day. Therefore, making decisions today is indeed a complex task.
Because of these trends and changes, it is nearly impossible to rely on a trial-anderror approach to management, especially for decisions for which the factors shown in
Table 1.1 are strong influences. Managers must be more sophisticated; they must use the
new tools and techniques of their fields. Most of those tools and techniques are discussed
in this book. Using them to support decision making can be extremely rewarding in
making effective decisions. In the following section, we look at why we need computer
support and how it is provided.
SECTION 1.3 REVIEW QUESTIONS
1. Describe the three major managerial roles , and list some of the specific activities in each.
2. Why have some argued that management is the same as decision making?
3. Describe the four steps managers take in making a decision.
1.4
INFORMATION SYSTEMS SUPPORT FOR DECISION MAKING
From traditional uses in payroll and bookkeeping functions, computerized systems have
penetrated complex managerial areas ranging from the design and management of automated factories to the application of analytical methods for the evaluation of proposed
mergers and acquisitions. Nearly all executives know that information technology is vital
to their business and extensively use information technologies.
Computer applications have moved from transaction processing and monitoring
activities to problem analysis and solution applications, and much of the activity is done
with Web-based technologies, in many cases accessed through mobile devices. Analytics
and BI tools such as data warehousing, data mining, online analytical processing (OLAF) ,
dashboards , and the use of the Web for decision support are the cornerstones of today’s
modern management. Managers must have high-speed, networked information systems (wireline or wireless) to assist them with their most important task: making decisions. Besides the obvious growth in hardware, software, and network capacities, some
9
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Part I • Decision Making and Analytics: An Oveiview
developments have clearly contributed to facilitating growth of decision support and
analytics in a number of ways, including the following:
• Group communication and collaboration. Many decisions are made today by
groups whose members may be in different locations. Groups can collaborate and
communicate readily by using Web-based tools as well as the ubiquitous smartphones.
Collaboration is especially important along the supply chain, where partners-all the
way from vendors to customers-must share information. Assembling a group of
decision makers, especially experts, in one place can be costly. Infonnation systems
can improve the collaboration process of a group and enable its members to be at different locations (saving travel costs). We will study some applications in Chapter 12.
• Improved data management. Many decisions involve complex computations.
Data for these can be stored in different databases anywhere in the organization
and even possibly at Web sites outside the organization. The data may include text,
sound, graphics, and video, and they can be in different languages. It may be necessary to transmit data quickly from distant locations. Systems today can search, store,
and transmit needed data quickly, economically, securely, and transparently.
• Managing giant data warehouses and Big Data. Large data warehouses, like
the ones operated by Walmart, contain terabytes and even petabytes of data. Special
methods, including parallel computing, are available to organize, search, and mine
the data. The costs related to data warehousing are declining. Technologies that fall
under the broad category of Big Data have enabled massive data coming from a
variety of sources and in many different forms, which allows a very different view
into organizational performance that was not possible in the past.
• Analytical support. With more data and analysis technologies, more alternatives can be evaluated, forecasts can be improved, risk analysis can be performed
quickly, and the views of experts (some of whom may be in remote locations) can
be collected quickly and at a reduced cost. Expertise can even be derived directly
from analytical systems. With such tools, decision makers can perform complex
simulations, check many possible scenarios, and assess diverse impacts quickly and
economically. This, of course, is the focus of several chapters in the book.
• Overcoming cognitive limits in processingandstoringinformation. According
to Simon 0977), the human mind has only a limited ability to process and store information. People sometimes find it difficult to recall and use infonnation in an error-free
fashion due to their cognitive limits. The term cognitive limits indicates that an individual’s problem-solving capability is limited when a wide range of diverse information
and knowledge is required. Computerized systems enable people to overcome their
cognitive limits by quickly accessing and processing vast amounts of stored information
(see Chapter 2).
• Knowledge management. Organizations have gathered vast stores of information about their own operations, customers, internal procedures, employee interactions, and so forth through the unstructured and structured communications taking
place among the various stakeholders. Knowledge management systems (KMS,
Chapter 12) have become sources of formal and informal support for decision
making to managers, although sometimes they may not even be called KMS.
• Anywhere, any time support. Using wireless technology, managers can access
information anytime and from any place, analyze and interpret it, and communicate
with those involved. This perhaps is the biggest change that has occurred in the last
few years. The speed at which information needs to be processed and converted
into decisions has truly changed expectations for both consumers and businesses.
These and other capabilities have been driving tl1e use of computerized decision support
since the late 1960s, but especially since the mid-1990s. The growth of mobile technologies,
Chapter 1 • An Overview of Business Intelligence, Analytics, and Decision Support
social media platforms, and analytical tools has enabled a much higher level of information
systems support for managers. In the next sections we study a historical classification of
decision support tasks. This leads us to be introduced to decision support systems. We will
then study an overview of technologies that have been broadly referred to as business intelligence. From there we will broaden our horizons to introduce various types of analytics.
SECTION 1.4 REVIEW QUESTIONS
1. What are some of the key system-oriented trends that have fostered IS-supported
decision making to a new level?
2. List some capabilities of information systems that can facilitate managerial decision
making.
3. How can a computer help overcome the cognitive limits of humans?
1.5
A N EARLY FRAMEWORK FOR COMPUTERIZED
DECISION SUPPORT
An early fra…
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