Discussion (Chapter 6): List and briefly describe the nine-step process in con-ducting a neural network project.Note: The first post should be made by Wednesday EST. I am looking for active engagement in the discussion. Please engage early and often.Your response should be 100-300 words. Respond to two postings provided by your classmates.ELEVENTH EDITION
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
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Preface xxv
About the Authors
Introduction to Analytics and AI
Chapter 1
Chapter 2
Chapter 3
Chapter 6
Chapter 7
Chapter 9
Chapter 12
Chapter 13
Data Mining Process, Methods, and Algorithms
Machine-Learning Techniques for Predictive
Analytics 251
Deep Learning and Cognitive Computing 315
Text Mining, Sentiment Analysis, and Social
Analytics 388
Prescriptive Analytics: Optimization and
Simulation 460
Big Data, Cloud Computing, and Location Analytics:
Concepts and Tools 509
Robotics, Social Networks, AI and IoT
Chapter 10
Chapter 11
Overview of Business Intelligence, Analytics,
Data Science, and Artificial Intelligence: Systems
for Decision Support 2
Artificial Intelligence: Concepts, Drivers, Major
Technologies, and Business Applications 73
Nature of Data, Statistical Modeling, and
Visualization 117
Prescriptive Analytics and Big Data
Chapter 8
Predictive Analytics/Machine Learning
Chapter 4
Chapter 5
Robotics: Industrial and Consumer Applications 580
Group Decision Making, Collaborative Systems, and
AI Support 610
Knowledge Systems: Expert Systems, Recommenders,
Chatbots, Virtual Personal Assistants, and Robo
Advisors 648
The Internet of Things as a Platform for Intelligent
Applications 687
Caveats of Analytics and AI
Chapter 14
Implementation Issues: From Ethics and Privacy to
Organizational and Societal Impacts 726
Glossary 770
Index 785
About the Authors
Introduction to Analytics and AI
Chapter 1 Overview of Business Intelligence, Analytics, Data
Science, and Artificial Intelligence: Systems for Decision
Support 2
Opening Vignette: How Intelligent Systems Work for
KONE Elevators and Escalators Company 3
Changing Business Environments and Evolving Needs for
Decision Support and Analytics 5
Decision-Making Process 6
The Influence of the External and Internal Environments on the Process 6
Data and Its Analysis in Decision Making 7
Technologies for Data Analysis and Decision Support 7
Decision-Making Processes and Computerized Decision
Support Framework 9
Simon’s Process: Intelligence, Design, and Choice 9
The Intelligence Phase: Problem (or Opportunity) Identification 10
0 APPLICATION CASE 1.1 Making Elevators Go Faster!
The Design Phase 12
The Choice Phase 13
The Implementation Phase 13
The Classical Decision Support System Framework 14
A DSS Application 16
Components of a Decision Support System 18
The Data Management Subsystem 18
The Model Management Subsystem 19
Telecommunications Rate Decisions 20
The User Interface Subsystem 20
The Knowledge-Based Management Subsystem 21
Evolution of Computerized Decision Support to Business
Intelligence/Analytics/Data Science 22
A Framework for Business Intelligence 25
The Architecture of BI 25
The Origins and Drivers of BI 26
Data Warehouse as a Foundation for Business Intelligence 27
Transaction Processing versus Analytic Processing 27
A Multimedia Exercise in Business Intelligence 28
Analytics Overview 30
Descriptive Analytics 32
0 APPLICATION CASE 1.3 Silvaris Increases Business with Visual
Analysis and Real-Time Reporting Capabilities 32
0 APPLICATION CASE 1.4 Siemens Reduces Cost with the Use of Data
Visualization 33
Predictive Analytics 33
0 APPLICATION CASE 1.5 Analyzing Athletic Injuries
Prescriptive Analytics 34
0 APPLICATION CASE 1.6 A Specialty Steel Bar Company Uses Analytics
to Determine Available-to-Promise Dates 35
Analytics Examples in Selected Domains 38
Sports Analytics—An Exciting Frontier for Learning and Understanding
Applications of Analytics 38
Analytics Applications in Healthcare—Humana Examples 43
0 APPLICATION CASE 1.7 Image Analysis Helps Estimate Plant Cover 50
Artificial Intelligence Overview
What Is Artificial Intelligence? 52
The Major Benefits of AI 52
The Landscape of AI 52
0 APPLICATION CASE 1.8 AI Increases Passengers’ Comfort and
Security in Airports and Borders 54
The Three Flavors of AI Decisions 55
Autonomous AI 55
Societal Impacts 56
0 APPLICATION CASE 1.9 Robots Took the Job of Camel-Racing Jockeys
for Societal Benefits 58
Convergence of Analytics and AI 59
Major Differences between Analytics and AI 59
Why Combine Intelligent Systems? 60
How Convergence Can Help? 60
Big Data Is Empowering AI Technologies 60
The Convergence of AI and the IoT 61
The Convergence with Blockchain and Other Technologies 62
0 APPLICATION CASE 1.10 Amazon Go Is Open for Business
IBM and Microsoft Support for Intelligent Systems Convergence 63
1.9 Overview of the Analytics Ecosystem 63
1.10 Plan of the Book 65
1.11 Resources, Links, and the Teradata University Network
Connection 66
Resources and Links 66
Vendors, Products, and Demos 66
Periodicals 67
The Teradata University Network Connection 67
The Book’s Web Site 67
Chapter Highlights
Questions for Discussion

Key Terms
• Exercises
Chapter 2 Artificial Intelligence: Concepts, Drivers, Major
Technologies, and Business Applications
Opening Vignette: INRIX Solves Transportation
Problems 74
Introduction to Artificial Intelligence 76
Definitions 76
Major Characteristics of AI Machines 77
Major Elements of AI 77
AI Applications 78
Major Goals of AI 78
Drivers of AI 79
Benefits of AI 79
Some Limitations of AI Machines 81
Three Flavors of AI Decisions 81
Artificial Brain 82
Human and Computer Intelligence 83
What Is Intelligence? 83
How Intelligent Is AI? 84
Measuring AI 85
0 APPLICATION CASE 2.1 How Smart Can a Vacuum Cleaner Be?
Major AI Technologies and Some Derivatives
Intelligent Agents 87
Machine Learning 88
0 APPLICATION CASE 2.2 How Machine Learning Is Improving Work
in Business 89
Machine and Computer Vision 90
Robotic Systems 91
Natural Language Processing 92
Knowledge and Expert Systems and Recommenders 93
Chatbots 94
Emerging AI Technologies 94
AI Support for Decision Making 95
Some Issues and Factors in Using AI in Decision Making 96
AI Support of the Decision-Making Process 96
Automated Decision Making 97
0 APPLICATION CASE 2.3 How Companies Solve Real-World Problems
Using Google’s Machine-Learning Tools 97
Conclusion 98
AI Applications in Accounting 99
AI in Accounting: An Overview 99
AI in Big Accounting Companies 100
Accounting Applications in Small Firms 100
0 APPLICATION CASE 2.4 How EY, Deloitte, and PwC Are Using AI
Job of Accountants 101
AI Applications in Financial Services
AI Activities in Financial Services 101
AI in Banking: An Overview 101
Illustrative AI Applications in Banking 102
Insurance Services 103
0 APPLICATION CASE 2.5 US Bank Customer Recognition and
Services 104
AI in Human Resource Management (HRM)
AI in HRM: An Overview 105
AI in Onboarding 105
0 APPLICATION CASE 2.6 How Alexander Mann Solutions (AMS) Is
Using AI to Support the Recruiting Process 106
Introducing AI to HRM Operations 106
AI in Marketing, Advertising, and CRM
Overview of Major Applications 107
AI Marketing Assistants in Action 108
Customer Experiences and CRM 108
0 APPLICATION CASE 2.7 Kraft Foods Uses AI for Marketing
and CRM 109
Other Uses of AI in Marketing 110
2.10 AI Applications in Production-Operation
Management (POM) 110
AI in Manufacturing 110
Implementation Model 111
Intelligent Factories 111
Logistics and Transportation 112
Chapter Highlights
Questions for Discussion

Key Terms
• Exercises
Chapter 3 Nature of Data, Statistical Modeling, and Visualization 117
3.1 Opening Vignette: SiriusXM Attracts and Engages a
New Generation of Radio Consumers with Data-Driven
Marketing 118
3.2 Nature of Data 121
3.3 Simple Taxonomy of Data 125
0 APPLICATION CASE 3.1 Verizon Answers the Call for Innovation: The
Nation’s Largest Network Provider uses Advanced Analytics to Bring
the Future to its Customers 127
viii Contents
Art and Science of Data Preprocessing
0 APPLICATION CASE 3.2 Improving Student Retention with
Data-Driven Analytics 133
Statistical Modeling for Business Analytics
Descriptive Statistics for Descriptive Analytics 140
Measures of Centrality Tendency (Also Called Measures of Location or
Centrality) 140
Arithmetic Mean 140
Median 141
Mode 141
Measures of Dispersion (Also Called Measures of Spread or
Decentrality) 142
Range 142
Variance 142
Standard Deviation 143
Mean Absolute Deviation 143
Quartiles and Interquartile Range 143
Box-and-Whiskers Plot 143
Shape of a Distribution 145
0 APPLICATION CASE 3.3 Town of Cary Uses Analytics to Analyze Data
from Sensors, Assess Demand, and Detect Problems 150
Regression Modeling for Inferential Statistics 151
How Do We Develop the Linear Regression Model? 152
How Do We Know If the Model Is Good Enough? 153
What Are the Most Important Assumptions in Linear Regression? 154
Logistic Regression 155
Time-Series Forecasting 156
0 APPLICATION CASE 3.4 Predicting NCAA Bowl Game
Outcomes 157
Business Reporting
0 APPLICATION CASE 3.5 Flood of Paper Ends at FEMA
Data Visualization 166
Brief History of Data Visualization 167
0 APPLICATION CASE 3.6 Macfarlan Smith Improves Operational
Performance Insight with Tableau Online 169
Different Types of Charts and Graphs
Basic Charts and Graphs 171
Specialized Charts and Graphs 172
Which Chart or Graph Should You Use? 174
3.10 Emergence of Visual Analytics
Visual Analytics 178
High-Powered Visual Analytics Environments 180
3.11 Information Dashboards 182
0 APPLICATION CASE 3.7 Dallas Cowboys Score Big with Tableau
and Teknion 184
Dashboard Design 184
0 APPLICATION CASE 3.8 Visual Analytics Helps Energy Supplier Make
Better Connections 185
What to Look for in a Dashboard 186
Best Practices in Dashboard Design 187
Benchmark Key Performance Indicators with Industry Standards 187
Wrap the Dashboard Metrics with Contextual Metadata 187
Validate the Dashboard Design by a Usability Specialist 187
Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard 188
Enrich the Dashboard with Business-User Comments 188
Present Information in Three Different Levels 188
Pick the Right Visual Construct Using Dashboard Design Principles 188
Provide for Guided Analytics 188
Chapter Highlights
Questions for Discussion

Key Terms
• Exercises
Predictive Analytics/Machine Learning
Chapter 4 Data Mining Process, Methods, and Algorithms 194
4.1 Opening Vignette: Miami-Dade Police Department Is Using
Predictive Analytics to Foresee and Fight Crime 195
4.2 Data Mining Concepts 198
0 APPLICATION CASE 4.1 Visa Is Enhancing the Customer
Experience while Reducing Fraud with Predictive Analytics
and Data Mining 199
Definitions, Characteristics, and Benefits 201
How Data Mining Works 202
0 APPLICATION CASE 4.2 American Honda Uses Advanced Analytics to
Improve Warranty Claims 203
Data Mining Versus Statistics 208
Data Mining Applications 208
0 APPLICATION CASE 4.3 Predictive Analytic and Data Mining Help
Stop Terrorist Funding 210
Data Mining Process
Step 1: Business Understanding 212
Step 2: Data Understanding 212
Step 3: Data Preparation 213
Step 4: Model Building 214
0 APPLICATION CASE 4.4 Data Mining Helps in
Cancer Research 214
Step 5: Testing and Evaluation 217
Step 6: Deployment 217
Other Data Mining Standardized Processes and Methodologies 217
Data Mining Methods 220
Classification 220
Estimating the True Accuracy of Classification Models 221
Estimating the Relative Importance of Predictor Variables 224
Cluster Analysis for Data Mining 228
0 APPLICATION CASE 4.5 Influence Health Uses Advanced Predictive
Analytics to Focus on the Factors That Really Influence People’s
Healthcare Decisions 229
Association Rule Mining 232
Data Mining Software Tools
0 APPLICATION CASE 4.6 Data Mining goes to Hollywood: Predicting
Financial Success of Movies 239
Data Mining Privacy Issues, Myths, and Blunders
0 APPLICATION CASE 4.7 Predicting Customer Buying Patterns—The
Target Story 243
Data Mining Myths and Blunders 244
Chapter Highlights
Questions for Discussion

Key Terms
• Exercises
Chapter 5 Machine-Learning Techniques for Predictive
Opening Vignette: Predictive Modeling Helps
Better Understand and Manage Complex Medical
Procedures 252
Basic Concepts of Neural Networks 255
Biological versus Artificial Neural Networks 256
0 APPLICATION CASE 5.1 Neural Networks are Helping to Save
Lives in the Mining Industry 258
Neural Network Architectures 259
Kohonen’s Self-Organizing Feature Maps 259
Hopfield Networks 260
0 APPLICATION CASE 5.2 Predictive Modeling Is Powering the Power
Generators 261
Support Vector Machines
0 APPLICATION CASE 5.3 Identifying Injury Severity Risk Factors in
Vehicle Crashes with Predictive Analytics 264
Mathematical Formulation of SVM 269
Primal Form 269
Dual Form 269
Soft Margin 270
Nonlinear Classification 270
Kernel Trick 271
Process-Based Approach to the Use of SVM 271
Support Vector Machines versus Artificial Neural Networks 273
Nearest Neighbor Method for Prediction 274
Similarity Measure: The Distance Metric 275
Parameter Selection 275
0 APPLICATION CASE 5.4 Efficient Image Recognition and
Categorization with knn 277
Naïve Bayes Method for Classification 278
Bayes Theorem 279
Naïve Bayes Classifier 279
Process of Developing a Naïve Bayes Classifier 280
Testing Phase 281
0 APPLICATION CASE 5.5 Predicting Disease Progress in Crohn’s
Disease Patients: A Comparison of Analytics Methods 282
Bayesian Networks 287
How Does BN Work? 287
How Can BN Be Constructed? 288
Ensemble Modeling
Motivation—Why Do We Need to Use Ensembles? 293
Different Types of Ensembles 295
Bagging 296
Boosting 298
Variants of Bagging and Boosting 299
Stacking 300
Information Fusion 300
Summary—Ensembles are not Perfect! 301
0 APPLICATION CASE 5.6 To Imprison or Not to Imprison:
A Predictive Analytics-Based Decision Support System for
Drug Courts 304
Chapter Highlights
Questions for Discussion
Internet Exercises

Key Terms

• Exercises
Chapter 6 Deep Learning and Cognitive Computing 315
6.1 Opening Vignette: Fighting Fraud with Deep Learning
and Artificial Intelligence 316
6.2 Introduction to Deep Learning 320
0 APPLICATION CASE 6.1 Finding the Next Football Star with
Artificial Intelligence 323
Basics of “Shallow” Neural Networks
0 APPLICATION CASE 6.2 Gaming Companies Use Data Analytics to
Score Points with Players 328
0 APPLICATION CASE 6.3 Artificial Intelligence Helps Protect Animals
from Extinction 333
xii Contents
Process of Developing Neural Network–Based
Systems 334
Learning Process in ANN 335
Backpropagation for ANN Training 336
Illuminating the Black Box of ANN 340
0 APPLICATION CASE 6.4 Sensitivity Analysis Reveals Injury Severity
Factors in Traffic Accidents 341
Deep Neural Networks 343
Feedforward Multilayer Perceptron (MLP)-Type Deep Networks 343
Impact of Random Weights in Deep MLP 344
More Hidden Layers versus More Neurons? 345
0 APPLICATION CASE 6.5 Georgia DOT Variable Speed Limit Analytics
Help Solve Traffic Congestions 346
Convolutional Neural Networks 349
Convolution Function 349
Pooling 352
Image Processing Using Convolutional Networks 353
0 APPLICATION CASE 6.6 From Image Recognition to Face
Recognition 356
Text Processing Using Convolutional Networks 357
Recurrent Networks and Long Short-Term Memory
Networks 360
0 APPLICATION CASE 6.7 Deliver Innovation by Understanding
Customer Sentiments 363
LSTM Networks Applications 365
Computer Frameworks for Implementation of Deep
Learning 368
Torch 368
Caffe 368
TensorFlow 369
Theano 369
Keras: An Application Programming Interface 370
Cognitive Computing 370
How Does Cognitive Computing Work? 371
How Does Cognitive Computing Differ from AI? 372
Cognitive Search 374
IBM Watson: Analytics at Its Best 375
0 APPLICATION CASE 6.8 IBM Watson Competes against the
Best at Jeopardy! 376
How Does Watson Do It? 377
What Is the Future for Watson? 377
Chapter Highlights
Questions for Discussion

Key Terms
• Exercises
Chapter 7 Text Mining, Sentiment Analysis, and Social Analytics 388
7.1 Opening Vignette: Amadori Group Converts Consumer
Sentiments into Near-Real-Time Sales 389
7.2 Text Analytics and Text Mining Overview 392
0 APPLICATION CASE 7.1 Netflix: Using Big Data to Drive Big
Engagement: Unlocking the Power of Analytics to Drive
Content and Consumer Insight 395
Natural Language Processing (NLP)
0 APPLICATION CASE 7.2 AMC Networks Is Using Analytics to
Capture New Viewers, Predict Ratings, and Add Value for Advertisers
in a Multichannel World 399
Text Mining Applications
Marketing Applications 403
Security Applications 403
Biomedical Applications 404
0 APPLICATION CASE 7.3 Mining for Lies
Academic Applications 407
0 APPLICATION CASE 7.4 The Magic Behind the Magic: Instant Access
to Information Helps the Orlando Magic Up their Game and the Fan’s
Experience 408
Text Mining Process 410
Task 1: Establish the Corpus 410
Task 2: Create the Term–Document Matrix 411
Task 3: Extract the Knowledge 413
0 APPLICATION CASE 7.5 Research Literature Survey with Text
Mining 415
Sentiment Analysis
0 APPLICATION CASE 7.6 Creating a Unique Digital Experience to
Capture Moments That Matter at Wimbledon 419
Sentiment Analysis Applications 422
Sentiment Analysis Process 424
Methods for Polarity Identification 426
Using a Lexicon 426
Using a Collection of Training Documents 427
Identifying Semantic Orientation of Sentences and Phrases 428
Identifying Semantic Orientation of Documents 428
Web Mining Overview 429
Web Content and Web Structure Mining 431
Search Engines 433
Anatomy of a Search Engine 434
1. Development Cycle 434
2. Response Cycle 435
Search Engine Optimization 436
Methods for Search Engine Optimization 437
0 APPLICATION CASE 7.7 Delivering Individualized Content and
Driving Digital Engagement: How Barbour Collected More Than 49,000
New Leads in One Month with Teradata Interactive 439
Web Usage Mining (Web Analytics)
Web Analytics Technologies 441
Web Analytics Metrics 442
Web Site Usability 442
Traffic Sources 443
Visitor Profiles 444
Conversion Statistics 444
Social Analytics 446
Social Network Analysis 446
Social Network Analysis Metrics 447
0 APPLICATION CASE 7.8 Tito’s Vodka Establishes Brand Loyalty with
an Authentic Social Strategy 447
Connections 450
Distributions 450
Segmentation 451
Social Media Analytics 451
How Do People Use Social Media? 452
Measuring the Social Media Impact 453
Best Practices in Social Media Analytics 453
Chapter Highlights
Questions for Discussion

Key Terms
• Exercises
Prescriptive Analytics and Big Data
Chapter 8 Prescriptive Analytics: Optimization and Simulation 460
8.1 Opening Vignette: School District of Philadelphia Uses
Prescriptive Analytics to Find Optimal Solution for
Awarding Bus Route Contracts 461
8.2 Model-Based Decision Making 462
0 APPLICATION CASE 8.1 Canadian Football League Optimizes Game
Schedule 463
Prescriptive Analytics Model Examples 465
Identification of the Problem and Environmental Analysis 465
0 APPLICATION CASE 8.2 Ingram Micro Uses Business Intelligence
Applications to Make Pricing Decisions 466
Model Categories 467
Structure of Mathematical Models for Decision
Support 469
The Components of Decision Support Mathematical Models 469
The Structure of Mathematical Models 470
Certainty, Uncertainty, and Risk 471
Decision Making under Certainty 471
Decision Making under Uncertainty 472
Decision Making under Risk (Risk Analysis) 472
0 APPLICATION CASE 8.3 American Airlines Uses Should-Cost
Modeling to Assess the Uncertainty of Bids for Shipment
Routes 472
Decision Modeling with Spreadsheets
0 APPLICATION CASE 8.4 Pennsylvania Adoption Exchange Uses
Spreadsheet Model to Better Match Children with Families 474
0 APPLICATION CASE 8.5 Metro Meals on Wheels Treasure Valley Uses
Excel to Find Optimal Delivery Routes 475
Mathematical Programming Optimization
0 APPLICATION CASE 8.6 Mixed-Integer Programming Model
Helps the University of Tennessee Medical Center with Scheduling
Physicians 478
Linear Programming Model 479
Modeling in LP: An Example 480
Implementation 484
Multiple Goals, Sensitivity Analysis, What-If Analysis, and
Goal Seeking 486
Multiple Goals 486
Sensitivity Analysis 487
What-If Analysis 488
Goal Seeking 489
Decision Analysis with Decision Tables and Decision
Trees 490
Decision Tables 490
Decision Trees 492
Introduction to Simulation
Major Characteristics of Simulation 493
0 APPLICATION CASE 8.7 Steel Tubing Manufacturer Uses a
Simulation-Based Production Scheduling System 493
Advantages of Simulation 494
Disadvantages of Simulation 495
The Methodology of Simulation 495
Simulation Types 496
Monte Carlo Simulation 497
Discrete Event Simulation 498
0 APPLICATION CASE 8.8 Cosan Improves Its Renewable Energy
Supply Chain Using Simulation
Visual Interactive Simulation 500
Conventional Simulation Inadequacies 500
Visual Interactive Simulation 500
Visual Interactive Models and DSS 500
Simulation Software 501
0 APPLICATION CASE 8.9 Improving Job-Shop Scheduling Decisions
through RFID: A Simulation-Based Assessment
Chapter Highlights
Questions for Discussion

Key Terms
• Exercises
Chapter 9 Big Data, Cloud Computing, and Location Analytics:
Concepts and Tools 509
Opening Vignette: Analyzing Customer Churn in a Telecom
Company Using Big Data Methods 510
Definition of Big Data 513
The “V”s That Define Big Data 514
0 APPLICATION CASE 9.1 Alternative Data for Market Analysis or
Forecasts 517
Fundamentals of Big Data Analytics 519
Business Problems Addressed by Big Data Analytics 521
0 APPLICATION CASE 9.2 Overstock.com Combines Multiple Datasets
to Understand Customer Journeys 522
Big Data Technologies 523
MapReduce 523
Why Use MapReduce? 523
Hadoop 524
How Does Hadoop Work? 525
Hadoop Technical Components 525
Hadoop: The Pros and Cons 527
NoSQL 528
0 APPLICATION CASE 9.3 eBay’s Big Data Solution
0 APPLICATION CASE 9.4 Understanding Quality and Reliability
of Healthcare Support Information on Twitter 531
Big Data and Data Warehousing 532
Use Cases for Hadoop 533
Use Cases for Data Warehousing 534
The Gray Areas (Any One of the Two Would Do the Job) 535
Coexistence of Hadoop and Data Warehouse 536
In-Memory Analytics and Apache Spark™ 537
0 APPLICATION CASE 9.5 Using Natural Language Processing to
analyze customer feedback in TripAdvisor reviews 538
Architecture of Apache SparkTM 538
Getting Started with Apache SparkTM 539
Big Data and Stream Analytics 543
Stream Analytics versus Perpetual Analytics 544
Critical Event Processing 545
Data Stream Mining 546
Applications of Stream Analytics 546
e-Commerce 546
Telecommunications 546
0 APPLICATION CASE 9.6 Salesforce Is Using Streaming Data to
Enhance Customer Value 547
Law Enforcement and Cybersecurity 547
Power Industry 548
Financial Services 548
Health Sciences 548
Government 548
Big Data Vendors and Platforms 549
Infrastructure Services Providers 550
Analytics Solution Providers 550
Business Intelligence Providers Incorporating Big Data 551
0 APPLICATION CASE 9.7 Using Social Media for Nowcasting
Flu Activity 551
0 APPLICATION CASE 9.8 Analyzing Disease Patterns from an
Electronic Medical Records Data Warehouse 554
Cloud Computing and Business Analytics
Data as a Service (DaaS) 558
Software as a Service (SaaS) 559
Platform as a Service (PaaS) 559
Infrastructure as a Service (IaaS) 559
Essential Technologies for Cloud Computing 560
0 APPLICATION CASE 9.9 Major West Coast Utility Uses Cloud-Mobile
Technology to Provide Real-Time Incident Reporting 561
Cloud Deployment Models 563
Major Cloud Platform Providers in Analytics 563
Analytics as a Service (AaaS) 564
Representative Analytics as a Service Offerings 564
Illustrative Analytics Applications Employing the Cloud Infrastructure 565
Using Azure IOT, Stream Analytics, and Machine Learning to Improve Mobile
Health Care Services 565
Gulf Air Uses Big Data to Get Deeper Customer Insight 566
Chime Enhances Customer Experience Using Snowflake 566
Location-Based Analytics for Organizations 567
Geospatial Analytics 567
0 APPLICATION CASE 9.10 Great Clips Employs Spatial Analytics to
Shave Time in Location Decisions 570
0 APPLICATION CASE 9.11 Starbucks Exploits GIS and Analytics to
Grow Worldwide 570
Real-Time Location Intelligence 572
Analytics Applications for Consumers 573
Chapter Highlights
Questions for Discussion

Key Terms
• Exercises
xviii Contents
Robotics, Social Networks, AI and IoT
Chapter 10 Robotics: Industrial and Consumer Applications 580
10.1 Opening Vignette: Robots Provide Emotional Support
to Patients and Children 581
10.2 Overview of Robotics 584
10.3 History of Robotics 584
10.4 Illustrative Applications of Robotics 586
Changing Precision Technology 586
Adidas 586
BMW Employs Collaborative Robots 587
Tega 587
San Francisco Burger Eatery 588
Spyce 588
Mahindra & Mahindra Ltd. 589
Robots in the Defense Industry 589
Pepper 590
Da Vinci Surgical System 592
Snoo – A Robotic Crib 593
MEDi 593
Care-E Robot 593
10.5 Components of Robots 595
10.6 Various Categories of Robots 596
10.7 Autonomous Cars: Robots in Motion 597
Autonomous Vehicle Development 598
Issues with Self-Driving Cars 599
10.8 Impact of Robots on Current and Future Jobs 600
10.9 Legal Implications of Robots and Artificial Intelligence 603
Tort Liability 603
Patents 603
Property 604
Taxation 604
Practice of Law 604
Constitutional Law 605
Professional Certification 605
Law Enforcement 605
Chapter Highlights
Questions for Discussion

Key Terms
• Exercises
Chapter 11 Group Decision Making, Collaborative Systems, and
AI Support
Opening Vignette: Hendrick Motorsports Excels with
Collaborative Teams 611
Making Decisions in Groups: Characteristics, Process,
Benefits, and Dysfunctions 613
Characteristics of Group Work 613
Types of Decisions Made by Groups 614
Group Decision-Making Process 614
Benefits and Limitations of Group Work 615
Supporting Group Work and Team Collaboration with
Computerized Systems 616
Overview of Group Support Systems (GSS) 617
Time/Place Framework 617
Group Collaboration for Decision Support 618
Electronic Support for Group Communication and
Collaboration 619
Groupware for Group Collaboration 619
Synchronous versus Asynchronous Products 619
Virtual Meeting Systems 620
Collaborative Networks and Hubs 622
Collaborative Hubs 622
Social Collaboration 622
Sample of Popular Collaboration Software 623
Direct Computerized Support for Group Decision
Making 623
Group Decision Support Systems (GDSS) 624
Characteristics of GDSS 625
Supporting the Entire Decision-Making Process 625
Brainstorming for Idea Generation and Problem Solving 627
Group Support Systems 628
Collective Intelligence and Collaborative
Intelligence 629
Definitions and Benefits 629
Computerized Support to Collective Intelligence 629
0 APPLICATION CASE 11.1 Collaborative Modeling for Optimal
Water Management: The Oregon State University
Project 630
How Collective Intelligence May Change Work and Life 631
Collaborative Intelligence 632
How to Create Business Value from Collaboration: The IBM
Study 632
Crowdsourcing as a Method for Decision Support
The Essentials of Crowdsourcing 633
Crowdsourcing for Problem-Solving and Decision Support 634
Implementing Crowdsourcing for Problem Solving 635
0 APPLICATION CASE 11.2 How InnoCentive Helped GSK Solve a
Difficult Problem 636
Artificial Intelligence and Swarm AI Support of Team
Collaboration and Group Decision Making 636
AI Support of Group Decision Making 637
AI Support of Team Collaboration 637
Swarm Intelligence and Swarm AI 639
0 APPLICATION CASE 11.3 XPRIZE Optimizes Visioneering
Human–Machine Collaboration and Teams of Robots
Human–Machine Collaboration in Cognitive Jobs 641
Robots as Coworkers: Opportunities and Challenges 641
Teams of collaborating Robots 642
Chapter Highlights
Questions for Discussion

Key Terms
• Exercises
Chapter 12 Knowledge Systems: Expert Systems, Recommenders,
Chatbots, Virtual Personal Assistants, and Robo
Advisors 648
Opening Vignette: Sephora Excels with Chatbots
Expert Systems and Recommenders 650
Basic Concepts of Expert Systems (ES) 650
Characteristics and Benefits of ES 652
Typical Areas for ES Applications 653
Structure and Process of ES 653
0 APPLICATION CASE 12.1 ES Aid in Identification of Chemical,
Biological, and Radiological Agents 655
Why the Classical Type of ES Is Disappearing 655
Recommendation Systems 657
0 APPLICATION CASE 12.3 Netflix Recommender: A Critical Success
Factor 658
Concepts, Drivers, and Benefits of Chatbots 660
What Is a Chatbot? 660
Chatbot Evolution 660
Components of Chatbots and the Process of Their Use 662
Drivers and Benefits 663
Representative Chatbots from Around the World 663
Enterprise Chatbots 664
The Interest of Enterprises in Chatbots 664
Enterprise Chatbots: Marketing and Customer Experience 665
0 APPLICATION CASE 12.4 WeChat’s Super Chatbot
0 APPLICATION CASE 12.5 How Vera Gold Mark Uses Chatbots to
Increase Sales 667
Enterprise Chatbots: Financial Services 668
Enterprise Chatbots: Service Industries 668
Chatbot Platforms 669
0 APPLICATION CASE 12.6 Transavia Airlines Uses Bots for
Communication and Customer Care Delivery 669
Knowledge for Enterprise Chatbots 671
Virtual Personal Assistants 672
Assistant for Information Search 672
If You Were Mark Zuckerberg, Facebook CEO 672
Amazon’s Alexa and Echo 672
Apple’s Siri 675
Google Assistant 675
Other Personal Assistants 675
Competition Among Large Tech Companies 675
Knowledge for Virtual Personal Assistants 675
Chatbots as Professional Advisors (Robo Advisors)
Robo Financial Advisors 676
Evolution of Financial Robo Advisors 676
Robo Advisors 2.0: Adding the Human Touch 676
0 APPLICATION CASE 12.7 Betterment, the Pioneer of Financial Robo
Advisors 677
Managing Mutual Funds Using AI 678
Other Professional Advisors 678
IBM Watson 680
Implementation Issues 680
Technology Issues 680
Disadvantages and Limitations of Bots 681
Quality of Chatbots 681
Setting Up Alexa’s Smart Home System 682
Constructing Bots 682
Chapter Highlights
Questions for Discussion

Key Terms
• Exercises
Chapter 13 The Internet of Things as a Platform for Intelligent
Opening Vignette: CNH Industrial Uses the Internet of
Things to Excel 688
Essentials of IoT 689
Definitions and Characteristics 690
The IoT Ecosystem 691
Structure of IoT Systems 691
Major Benefits and Drivers of IoT 694
Major Benefits of IoT 694
Major Drivers of IoT 695
Opportunities 695
How IoT Works 696
IoT and Decision Support 696
Sensors and Their Role in IoT 697
Brief Introduction to Sensor Technology 697
0 APPLICATION CASE 13.1 Using Sensors, IoT, and AI for
Environmental Control at the Athens, Greece,
International Airport 697
How Sensors Work with IoT 698
0 APPLICATION CASE 13.2 Rockwell Automation
Monitors Expensive Oil and Gas Exploration Assets to
Predict Failures 698
Sensor Applications and Radio-Frequency Identification (RFID) Sensors 699
Selected IoT Applications 701
A Large-scale IoT in Action 701
Examples of Other Existing Applications 701
Smart Homes and Appliances 703
Typical Components of Smart Homes 703
Smart Appliances 704
A Smart Home Is Where the Bot Is 706
Barriers to Smart Home Adoption 707
Smart Cities and Factories 707
0 APPLICATION CASE 13.3 Amsterdam on the Road to Become a
Smart City 708
Smart Buildings: From Automated to Cognitive Buildings 709
Smart Components in Smart Cities and Smart Factories 709
0 APPLICATION CASE 13.4 How IBM Is Making Cities Smarter
Worldwide 711
Improving Transportation in the Smart City 712
Combining Analytics and IoT in Smart City Initiatives 713
Bill Gates’ Futuristic Smart City 713
Technology Support for Smart Cities 713
Autonomous (Self-Driving) Vehicles
The Developments of Smart Vehicles 714
0 APPLICATION CASE 13.5 Waymo and Autonomous Vehicles 715
Flying Cars 717
Implementation Issues in Autonomous Vehicles 717
Contents xxiii
13.10 Implementing IoT and Managerial Considerations 717
Major Implementation Issues 718
Strategy for Turning Industrial IoT into Competitive Advantage 719
The Future of the IoT 720
Chapter Highlights
Questions for Discussion

Key Terms
• Exercises
Caveats of Analytics and AI
Chapter 14 Implementation Issues: From Ethics and Privacy to
Organizational and Societal Impacts
Opening Vignette: Why Did Uber Pay $245 Million to
Waymo? 727
Implementing Intelligent Systems: An Overview 729
The Intelligent Systems Implementation Process 729
The Impacts of Intelligent Systems 730
Legal, Privacy, and Ethical Issues 731
Legal Issues 731
Privacy Issues 732
Who Owns Our Private Data? 735
Ethics Issues 735
Ethical Issues of Intelligent Systems 736
Other Topics in Intelligent Systems Ethics 736
Successful Deployment of Intelligent Systems 737
Top Management and Implementation 738
System Development Implementation Issues 738
Connectivity and Integration 739
Security Protection 739
Leveraging Intelligent Systems in Business 739
Intelligent System Adoption 740
Impacts of Intelligent Systems on Organizations 740
New Organizational Units and Their Management 741
Transforming Businesses and Increasing Competitive Advantage 741
0 APPLICATION CASE 14.1 How 1-800-Flowers.com Uses Intelligent
Systems for Competitive Advantage 742
Redesign of an Organization Through the Use of Analytics 743
Intelligent Systems’ Impact on Managers’ Activities, Performance, and Job
Satisfaction 744
Impact on Decision Making 745
Industrial Restructuring 746
Impacts on Jobs and Work 747
An Overview 747
Are Intelligent Systems Going to Take Jobs—My Job? 747
AI Puts Many Jobs at Risk 748
0 APPLICATION CASE 14.2 White-Collar Jobs That Robots Have
Already Taken 748
Which Jobs Are Most in Danger? Which Ones Are Safe? 749
Intelligent Systems May Actually Add Jobs 750
Jobs and the Nature of Work Will Change 751
Conclusion: Let’s Be Optimistic! 752
Potential Dangers of Robots, AI, and Analytical Modeling
Position of AI Dystopia 753
The AI Utopia’s Position 753
The Open AI Project and the Friendly AI 754
The O’Neil Claim of Potential Analytics’ Dangers 755
Relevant Technology Trends 756
Gartner’s Top Strategic Technology Trends for 2018 and 2019 756
Other Predictions Regarding Technology Trends 757
Summary: Impact on AI and Analytics 758
Ambient Computing (Intelligence) 758
Future of Intelligent Systems 760
What Are the Major U.S. High-Tech Companies Doing in the Intelligent
Technologies Field? 760
AI Research Activities in China 761
0 APPLICATION CASE 14.3 How Alibaba.com Is Conducting AI 762
The U.S.–China Competition: Who Will Control AI? 764
The Largest Opportunity in Business 764
Conclusion 764
Chapter Highlights
Questions for Discussion

Key Terms
• Exercises
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 data and computerized tools to make better decisions. Even consumers
are using analytics tools directly or indirectly to make decisions on routine activities such
as shopping, health care, and entertainment. The field of business analytics (BA)/data science (DS)/decision support systems (DSS)/business intelligence (BI) is evolving rapidly
to become more focused on innovative methods and applications to utilize data streams
that were not even captured some time back, much less analyzed in any significant way.
New applications emerge daily in customer relationship management, banking and finance, health care and medicine, sports and entertainment, manufacturing and supply
chain management, utilities and energy, and virtually every industry imaginable.
The theme of this revised edition is analytics, data science, and AI 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 means of introducing AI, machine-learning, robotics,
chatbots, IoT, and Web/Internet-related enablers throughout the text. We highlight these
technologies as emerging components of modern-day business analytics systems. AI technologies have a major impact on decision making by enabling autonomous decisions and
by supporting steps in the process of making decisions. AI and analytics support each
other by creating a synergy that assists decision making.
The purpose of this book is to introduce the reader to the technologies that are
generally and collectively called analytics (or business analytics) but have been known
by other names such as decision support systems, executive information systems, and
business intelligence, among others. We use these terms interchangeably. This book presents the fundamentals of the methods, methodologies, and techniques used to design and
develop these systems. In addition, we introduce the essentials of AI both as it relates to
analytics as well as a standalone discipline for decision support.
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. In our own
teaching experience, projects undertaken in the class facilitate such exploration after the
students have been exposed to the myriad of applications and concepts in the book and
they have experienced specific software introduced by the professor.
This edition of the book can be used to offer a one-semester overview course on
analytics, which covers most or all of the topics/chapters included in the book. It can
also be used to teach two consecutive courses. For example, one course could focus on
the overall analytics coverage. It could cover selective sections of Chapters 1 and 3–9.
A second course could focus on artificial intelligence and emerging technologies as the
enablers of modern-day analytics as a subsequent course to the first course. This second
course could cover portions of Chapters 1, 2, 6, 9, and 10–14. The book can be used to
offer managerial-level exposure to applications and techniques as noted in the previous
paragraph, but it also includes sufficient technical details in selected chapters to allow an
instructor to focus on some technical methods and hands-on exercises.
Most of the specific improvements made in this eleventh edition concentrate on
three areas: reorganization, content update/upgrade (including AI, machine-learning,
chatbots, and robotics as enablers of analytics), and a sharper focus. Despite the many
changes, we have preserved the comprehensiveness and user friendliness that have made
the textbook a market leader in the last several decades. We have also optimized the
book’s size and content by eliminating older and redundant material and by adding and
combining material that is parallel to the current trends and is also demanded by many
professors. Finally, we present accurate and updated material that is not available in any
other text. We next describe the changes in the eleventh edition.
The book is supported by a Web site (pearsonhighered.com/sharda). We provide
links to additional learning materials and software tutorials through a special section of
the book Web site.
With the goal of improving the text and making it current with the evolving technology
trends, this edition marks a major reorganization to better reflect on the current focus on
analytics and its enabling technologies. The last three editions transformed the book from
the traditional DSS to BI and then from BI to BA and fostered a tight linkage with the
Teradata University Network (TUN). This edition is enhanced with new materials paralleling the latest trends in analytics including AI, machine learning, deep learning, robotics,
IoT, and smart/robo-collaborative assisting systems and applications. The following summarizes the major changes made to this edition.
• New organization. The book is now organized around two main themes: (1)
presentation of motivations, concepts, methods, and methodologies for different
types of analytics (focusing heavily on predictive and prescriptive analytic), and
(2) introduction and due coverage of new technology trends as the enablers of the
modern-day analytics such as AI, machine learning, deep learning, robotics, IoT,
smart/robo-collaborative assisting systems, etc. Chapter 1 provides an introduction
to the journey of decision support and enabling technologies. It begins with a brief
overview of the classical decision making and decision support systems. Then it
moves to business intelligence, followed by an introduction to analytics, Big Data,
and AI. We follow that with a deeper introduction to artificial intelligence in Chapter 2.
Because data is fundamental to any analysis, Chapter 3 introduces data issues as
well as descriptive analytics including statistical concepts and visualization. An online chapter covers data warehousing processes and fundamentals for those who
like to dig deeper into these issues. The next section covers predictive analytics and
machine learning. Chapter 4 provides an introduction to data mining applications
and the data mining process. Chapter 5 introduces many of the common data mining techniques: classification, clustering, association mining, and so forth. Chapter 6
includes coverage of deep learning and cognitive computing. Chapter 7 focuses on
text mining applications as well as Web analytics, including social media analytics,
sentiment analysis, and other related topics. The following section brings the “data
science” angle to a further depth. Chapter 8 covers prescriptive analytics including
optimization and simulation. Chapter 9 includes more details of Big Data analytics. It
also includes introduction to cloud-based analytics as well as location analytics. The
next section covers Robotics, social networks, AI, and the Internet of Things (IoT).
Chapter 10 introduces robots in business and consumer applications and also studies the future impact of such devices on society. Chapter 11 focuses on collaboration
systems, crowdsourcing, and social networks. Chapter 12 reviews personal assistants, chatbots, and the exciting developments in this space. Chapter 13 studies IoT
and its potential in decision support and a smarter society. The ubiquity of wireless
and GPS devices and other sensors is resulting in the creation of massive new databases and unique applications. Finally, Chapter 14 concludes with a brief discussion
of security, privacy, and societal dimensions of analytics and AI.
We should note that several chapters included in this edition have been available in the following companion book: Business Intelligence, Analytics, and Data
Science: A Managerial Perspective, 4th Edition, Pearson (2018) (Hereafter referred to
as BI4e). The structure and contents of these chapters have been updated somewhat
before inclusion in this edition of the book, but the changes are more significant in
the chapters marked as new. Of course, several of the chapters that came from BI4e
were not included in previous editions of this book.
• New chapters. The following chapters have been added:
Chapter 2 “Artificial Intelligence: Concepts, Drivers, Major Technologies,
and Business Applications” This chapter covers the essentials of AI, outlines its
benefits, compares it with humans’ intelligence, and describes the content of the
field. Example applications in accounting, finance, human resource management,
marketing and CRM, and production-operation management illustrate the benefits
to business (100% new material)
Chapter 6, “Deep Learning and Cognitive Computing” This chapter covers the
generation of machine learning technique, deep learning as well as the increasingly
more popular AI topic, cognitive computing. It is an almost entirely new chapter
(90% new material).
Chapter 10, “Robotics: Industrial and Consumer Applications” This chapter
introduces many robotics applications in industry and for consumers and concludes
with impacts of such advances on jobs and some legal ramifications (100% new
Chapter 12, “Knowledge Systems: Expert Systems, Recommenders, Chatbots,
Virtual Personal Assistants, and Robo Advisors” This new chapter concentrates
on different types of knowledge systems. Specifically, we cover new generations of
expert systems and recommenders, chatbots, enterprise chatbots, virtual personal
assistants, and robo-advisors (95% new).
Chapter 13, “The Internet of Things as a Platform for Intelligent Applications”
This new chapter introduces IoT as an enabler to analytics and AI applications. The
following technologies are described in detail: smart homes and appliances, smart
cities (including factories), and autonomous vehicles (100% new).
Chapter 14, “Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts” This mostly new chapter deals with implementation
issues of intelligent systems (including analytics). The major issues covered are
protection of privacy, intellectual property, ethics, technical issues (e.g., integration
and security) and administrative issues. We also cover the impact of these technologies on organizations and people and specifically deal with the impact on work and
jobs. Special attention is given to possible unintended impacts of analytics and AI
(robots). Then we look at relevant technology trends and conclude with an assessment of the future of analytics and AI (85% new).
• Streamlined coverage. We have optimized the book size and content by adding a lot of new material to cover new and cutting-edge analytics and AI trends
and technologies while eliminating most of the older, less-used material. We use a
dedicated Web site for the textbook to provide some of the older material as well as
updated content and links.
• Revised and updated content. Several chapters have new opening vignettes
that are based on recent stories and events. In addition, application cases throughout
the book are new or 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. The specific changes made to each chapter are as follows:
Chapters 1, 3–5, and 7–9 borrow material from BI4e to a significant degree.
Chapter 1, “Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support” This chapter includes some material
from DSS10e Chapters 1 and 2, but includes several new application cases, entirely new
material on AI, and of course, a new plan for the book (about 50% new material).
Chapter 3, “Nature of Data, Statistical Modeling, and Visualization”
• 75% new content.
• Most of the content related to nature of data and statistical analysis is new.
• New opening case.
• Mostly new cases throughout.
Chapter 4, “Data Mining Process, Methods, and Algorithms”
• 25% of the material is new.
• Some of the application cases are new.
Chapter 5, “Machine Learning Techniques for Predictive Analytics”
• 40% of the material is new.
• New machine-learning methods: naïve Bayes, Bayesian networks, and ensemble
• Most of the cases are new.
Chapter 7, “Text Mining, Sentiment Analysis, and Social Analytics”
• 25% of the material is new.
• Some of the cases are new.
Chapter 8, “Prescriptive Analytics: Optimization and Simulation”
• Several new optimization application exercises are included.
• A new application case is included.
• 20% of the material is new.
Chapter 9, “Big Data, Cloud Computing, and Location Analytics: Concepts and
Tools” This material has bene updated substantially in this chapter to include greater
coverage of stream analytics. It also updates material from Chapters 7 and 8 from BI4e
(50% new material).
Chapter 11,“Group Decision Making, Collaborative Systems, and AI Support” The
chapter is completely revised, regrouping group decision support. New topics include
Preface xxix
collective and collaborative intelligence, crowdsourcing, swarm AI, and AI support of all
related activities (80% new material).
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 the 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.
• 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.
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.
• 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/irc. 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, Moodle, 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.
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 eleventh 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
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
David Olson, University of Nebraska
Souren Paul, Southern Illinois University
Joshua Pauli, Dakota State University
Roger Alan Pick, University of Missouri–St. Louis
Saeed Piri, University of Oregon
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
Preface xxxi
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
Selim Zaim, Sehir University
Steve Zanakis, Florida International University
Fan Zhao, Florida Gulf Coast University
Hamed Majidi Zolbanin, Ball State University
Several individuals contributed material to the text or the supporting material.
For this new edition, assistance from the following students and colleagues is gratefully acknowledged: Behrooz Davazdahemami, Bhavana Baheti, Varnika Gottipati,
and Chakradhar Pathi (all of Oklahoma State University). Prof. Rick Wilson contributed some examples and new exercise questions for Chapter 8. Prof. Pankush Kalgotra
(Auburn University) contributed the new streaming analytics tutorial in Chapter 9. Other
contributors of materials for specific application stories are identified as sources in the
respective sections. Susan Baskin, Imad Birouty, Sri Raghavan, and Yenny Yang of Teradata provided special help in identifying new TUN content for the book and arranging
permissions for the same.
Many other colleagues and students have assisted us in developing previous editions
or the recent edition of the companion book from which some of the content has been
adapted in this revision. Some of that content is still included this edition. Their assistance
and contributions are acknowledged as well in chronological order. Dr. Dave Schrader
contributed the sports examples used in Chapter 1. These will provide a great introduction to analytics. We also thank INFORMS for their permission to highlight content from
Interfaces. We also recognize the following individuals for their assistance in developing Previous edition of the book: Pankush Kalgotra, Prasoon Mathur, Rupesh Agarwal,
Shubham Singh, Nan Liang, Jacob Pearson, Kinsey Clemmer, and Evan Murlette (all of
Oklahoma State University). Their help for BI 4e is gratefully acknowledged. The Teradata Aster team, especially Mark Ott, provided the material for the opening vignette for
Chapter 9. Dr. Brian LeClaire, CIO of Humana Corporation led with contributions of several real-life healthcare case studies developed by his team at Humana. Abhishek Rathi of
vCreaTek contributed his vision of analytics in the retail industry. In addition, the following former PhD students and research colleagues of ours have provided content or advice
and support for the book in many direct and indirect ways: Asil Oztekin, University of
Massachusetts-Lowell; Enes Eryarsoy, Sehir University; Hamed Majidi Zolbanin, Ball State
University; Amir Hassan Zadeh, Wright State University; Supavich (Fone) Pengnate, North
Dakota State University; Christie Fuller, Boise State University; Daniel Asamoah, Wright
State University; Selim Zaim, Istanbul Technical University; and Nihat Kasap, Sabanci University. 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 Interfaces. Assistance from Natraj Ponna, Daniel
Asamoah, Amir Hassan-Zadeh, Kartik Dasika, and Angie Jungermann (all of Oklahoma
State University) is gratefully acknowledged for DSS 10th edition. We also acknowledge
Jongswas Chongwatpol (NIDA, Thailand) for the material on SIMIO software, and Kazim
Topuz (University of Tulsa) for his contributions to the Bayesian networks section in
Chapter 5. For other previous editions, we acknowledge the contributions of Dave King
(a technology consultant and former executive at JDA Software Group, Inc.) and Jerry
Wagner (University of Nebraska–Omaha). Major contributors for earlier editions include
Mike Goul (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); 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 include 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), 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), Late 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:
Dan Fylstra of Frontline Systems, Gregory Piatetsky-Shapiro 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), Gary Miner of StatSoft, Inc. (Tulsa, Oklahoma), Ward Systems Group, Inc. (Frederick,
Maryland), Idea Fisher Systems, Inc. (Irving, California), and Wordtech Systems (Orinda,
Special thanks to the Teradata University Network and especially to Hugh Watson,
Michael Goul, and Susan Baskin, 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). Jon Outland
assisted with the supplements.
Finally, the Pearson team is to be commended: Executive Editor Samantha Lewis
who orchestrated this project; the copyeditors; and the production team, Faraz Sharique
Ali at Pearson, and Gowthaman and staff at Integra Software Services, who transformed
the manuscript into a book.
Preface xxxiii
We would like to thank all these individuals and corporations. Without their help,
the creation of this book would not have been possible. We 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.
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.
Ramesh Sharda (M.B.A., Ph.D., University of Wisconsin–Madison) is the Vice Dean for
Research and Graduate Programs, Watson/ConocoPhillips Chair and a Regents Professor
of Management Science and Information Systems in the Spears School of Business at
Oklahoma State University. His research has been published in major journals in management science and information systems including Management Science, Operations
Research, Information Systems Research, Decision Support Systems, Decision Sciences
Journal, EJIS, JMIS, Interfaces, INFORMS Journal on Computing, ACM Data Base, and
many others. He is a member of the editorial boards of journals such as the Decision
Support Systems, Decision Sciences, and ACM Database. He has worked on many sponsored research projects with government and industry, and has also served as consultants
to many organizations. He also serves as the Faculty Director of Teradata University Network. He received the 2013 INFORMS Computing Society HG Lifetime Service Award,
and was inducted into Oklahoma Higher Education Hall of Fame in 2016. He is a Fellow
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 Regents 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 Sciences, Decision
Support Systems, Communications of the ACM, Computers and Operations Research,
Computers in Industry, Journal of Production Operations Management, Journal of
American Medical Informatics Association, Artificial Intelligence in Medicine, Expert
Systems with Applications, among others. He has published eight books/textbooks and
more than 100 peer-reviewed journal articles. He is often invited to national and international conferences for keynote addresses on topics related to business analytics, Big
Data, 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 serves as chair on tracks and mini-tracks at various
business analytics and information systems conferences. He is the co-editor-in-chief for
the Journal of Business Analytics, the area editor for Big Data and Business Analytics on
the Journal of Business Research, and also serves as chief editor, senior editor, associate
editor, and editorial board member on more than a dozen other journals. His consultancy, research, and teaching interests are in business analytics, data and text mining,
health analytics, decision support systems, knowledge management, systems analysis
and design, 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
About the Authors
Beach; Eastern Illinois University; and the University of Southern California. Dr. Turban
is the author of more than 110 refereed papers published in leading journals, such as
Management Science, MIS Quarterly, and Decision Support Systems. He is also the author
of 22 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, digital
commerce, and applied artificial intelligence.
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Introduction to
Analytics and AI
Overview of Business
Intelligence, Analytics, Data
Science, and Artificial Intelligence:
Systems for Decision Support
Understand the need for computerized support of
managerial decision making
■■ Understand the development of systems for
providing decision-making support
■■ Recognize the evolution of such computerized
support to the current state of analytics/data
science and artificial intelligence
■■ Describe the business intelligence (BI)
methodology and concepts
Understand the different types of analytics and
review selected applications
■■ Understand the basic concepts of artificial
intelligence (AI) and see selected applications
■■ Understand the analytics ecosystem to identify
various key players and career opportunities
he business environment (climate) is constantly changing, and it is becoming
more and more complex. Organizations, both 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. As technologies are evolving, many decisions are being automated, leading to a
major impact on knowledge work and workers in many ways.
This book is about using business analytics and artificial intelligence (AI) as a
computerized support portfolio for managerial decision making. It concentrates on the
Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence
theoretical and conceptual foundations of decision support as well as on the commercial
tools and techniques that are available. The book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an
EEE (exposure, experience, and exploration) approach to introducing these topics. The
book primarily provides exposure to various analytics/AI techniques and their applications. The idea is that students will be inspired to learn from how various organizations
have employed these technologies to make decisions or to gain a competitive edge. We
believe that such exposure to what is being accomplished with analytics and that how
it can be achieved is the key component of learning about analytics. In describing the
techniques, we also give examples of specific software tools that can be used for developing such applications. However, the book is not limited to any one software tool, so
students can experience these techniques using any number of available software tools.
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 the reader to Teradata University Network (TUN) and other
sites that include team-oriented exercises where appropriate. In our own teaching experience, projects undertaken in the class facilitate such exploration after students have been
exposed to the myriad of applications and concepts in the book and they have experienced specific software introduced by the professor.
This introductory chapter provides an introduction to analytics and artificial intelligence as well as an overview of the book. The chapter has the following sections:
1.1 Opening Vignette: How Intelligent Systems Work for KONE Elevators and
Escalators Company 3
1.2 Changing Business Environments and Evolving Needs for Decision Support
and Analytics 5
1.3 Decision-Making Processes and Computer Decision Support Framework 9
1.4 Evolution of Computerized Decision Support to Business Intelligence/
Analytics/Data Science 22
1.5 Analytics Overview 30
1.6 Analytics Examples in Selected Domains 38
1.7 Artificial Intelligence Overview 52
1.8 Convergence of Analytics and AI 59
1.9 Overview of the Analytics Ecosystem 63
1.10 Plan of the Book 65
1.11 Resources, Links, and the Teradata University Network Connection 66
1.1 OPENING VIGNETTE: How Intelligent Systems Work for
KONE Elevators and Escalators Company
KONE is a global industrial company (based in Finland) that manufactures mostly elevators and escalators and also services over 1.1 million elevators, escalators, and related
equipment in several countries. The company employs over 50,000 people.
Over 1 billion people use the elevators and escalators manufactured and serviced by
KONE every day. If equipment does not work properly, people may be late to work, cannot get home in time, and may miss important meetings and events. So, KONE’s objective
is to minimize the downtime and users’ suffering.
Part I • Introduction to Analytics and AI
The company has over 20,000 technicians who are dispatched to deal with the elevators
anytime a problem occurs. As buildings are getting higher (the trend in many places), more
people are using elevators, and there is more pressure on elevators to handle the growing
amount of traffic. KONE faced the responsibility to serve users smoothly and safely.
KONE decided to use IBM Watson IoT Cloud platform. As we will see in Chapter 6, IBM
installed cognitive abilities in buildings that make it possible to recognize situations and
behavior of both people and equipment. The Internet of Things (IoT), as we will see in
Chapter 13, is a platform that can connect millions of “things” together and to a central
command that can manipulate the connected things. Also, the IoT connects sensors that
are attached to KONE’s elevators and escalators. The sensors collect information and data
about the elevators (such as noise level) and other equipment in real time. Then, the IoT
transfers to information centers via the collected data “cloud.” There, analytic systems (IBM
Advanced Analytic Engine) and AI process the collected data and predict things such as
potential failures. The systems also identify the likely causes of problems and suggest potential remedies. Note the predictive power of IBM Watson Analytics (using machine learning,
an AI technology described in Chapters 4–6) for finding problems before they occur.
The KONE system collects a significant amount of data that are analyzed for other
purposes so that future design of equipment can be improved. This is because Watson
Analytics offers a convenient environment for communication of and collaboration
around the data. In addition, the analysis suggests how to optimize buildings and equipment operations. Finally, KONE and its customers can get insights regarding the financial
aspects of managing the elevators.
KONE also integrates the Watson capabilities with Salesforce’s service tools (Service
Cloud Lightning and Field Service Lightning). This combination helps KONE to immediately respond to emergencies or soon-to-occur failures as quickly as possible, dispatching some of its 20,000 technicians to the problems’ sites. Salesforce also provides superb
customer relationship management (CRM). The people–machine communication, query,
and collaboration in the system are in a natural language (an AI capability of Watson
Analytics; see Chapter 6). Note that IBM Watson analytics includes two types of analytics:
predictive, which predicts when failures may occur, and prescriptive, which recommends
actions (e.g., preventive maintenance).
KONE has minimized downtime and shortened the repair time. Obviously, elevators/
escalators users are much happier if they do not have problems because of equipment
downtime, so they enjoy trouble-free rides. The prediction of “soon-to-happen” can save
many problems for the equipment owners. The owners can also optimize the schedule of
their own employees (e.g., cleaners and maintenance workers). All in all, the decision makers at both KONE and the buildings can make informed and better decisions. Some day in
the future, robots may perform maintenance and repairs of elevators and escalators.
Note: This case is a sample of IBM Watson’s success using its cognitive buildings capability. To learn more, we
suggest you view the following YouTube videos: (1) youtube.com/watch?v=6UPJHyiJft0 (1:31 min.) (2017);
(2) youtube.com/watch?v=EVbd3ejEXus (2:49 min.) (2017).
Sources: Compiled from J. Fernandez. (2017, April). “A Billion People a Day. Millions of Elevators. No Room for
Downtime.” IBM developer Works Blog. developer.ibm.com/dwblog/2017/kone-watson-video/ (accessed
September 2018); H. Srikanthan. “KONE Improves ‘People Flow’ in 1.1 Million Elevators with IBM Watson IoT.”
Generis. https://generisgp.com/2018/01/08/ibm-case-study-kone-corp/ (accessed September 2018); L.
Slowey. (2017, February 16). “Look Who’s Talking: KONE Makes Elevator Services Truly Intelligent with Watson
IoT.” IBM Internet of Things Blog. ibm.com/blogs/internet-of-things/kone/ (accessed September 2018).
Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence
1. It is said that KONE is embedding intelligence across its supply chain and enables
smarter buildings. Explain.
2. Describe the role of IoT in this case.
3. What makes IBM Watson a necessity in this case?
4. Check IBM Advanced Analytics. What tools were included that relate to this case?
5. Check IBM cognitive buildings. How do they relate to this case?
Today, intelligent technologies can embark on large-scale complex projects when they
include AI combined with IoT. The capabilities of integrated intelligent platforms, such
as IBM Watson, make it possible to solve problems that were economically and technologically unsolvable just a few years ago. The case introduces the reader to several of the
technologies, including advanced analytics, sensors, IoT, and AI that are covered in this
book. The case also points to the use of “cloud.” The cloud is used to centrally process
large amounts of information using analytics and AI algorithms, involving “things” in different locations. This vignette also introduces us to two major types of analytics: predictive analytics (Chapters 4–6) and prescriptive analytics (Chapter 8).
Several AI technologies are discussed: machine learning, natural language processing, computer vision, and prescriptive analysis.
The case is an example of augmented intelligence in which people and machines
work together. The case illustrates the benefits to the vendor, the implementing companies, and their employees and to the users of the elevators and escalators.
Decision making is one of the most important activities in organizations of all kind—
probably the most important one. Decision making leads to the success or failure of organizations and how well they perform. Making decisions is getting difficult due to internal
and external factors. The rewards of making appropriate decisions can be very high and
so can the loss of inappropriate ones.
Unfortunately, it is not simple to make decisions. To begin with, there are several
types of decisions, each of which requires a different decision-making approach. For example, De Smet et al. (2017) of McKinsey & Company management consultants classify
organizational decision into the following four groups:
• Big-bet, high-risk decisions.
• Cross-cutting decisions, which are repetitive but high risk that require group work
(Chapter 11).
• Ad hoc decisions that arise episodically.
• Delegated decisions to individuals or small groups.
Therefore, it is necessary first to understand the nature of decision making. For a
comprehensive discussion, see (De Smet et al. 2017).
Modern business is full of uncertainties and rapid changes. To deal with these, organizational decision makers need to deal with ever-increasing and changing data. This
book is about the technologies that can assist decision makers in their jobs.
Part I • Introduction to Analytics and AI
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 tend to outperform those with leaders whose main
strengths are interpersonal communication skills. It is more important to emphasize
methodical, thoughtful, analytical decision making rather than flashiness and interpersonal communication skills.
Managers usually make decisions by following a four-step process (we learn more
about these in the next section):
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.
A more detailed process is offered by Quain (2018), who suggests the following steps:
Understand the decision you have to make.
Collect all the information.
Identify the alternatives.
Evaluate the pros and cons.
Select the best alternative.
Make the decision.
Evaluate the impact of your decision.
We will return to this process in Section 1.3.
The Influence of the External and Internal Environments
on the Process
To follow these decision-making processes, one must make sure that sufficient alternative solutions, including good ones, are being considered, that the consequences of using
these alternatives can be reasonably predicted, and that comparisons are done properly.
However, rapid changes in internal and external environments 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.
• Political factors. Major decisions may be influenced by both external and
internal politics. An example is the 2018 trade war on tariffs.
• Economic factors. These range from competition to the genera and state
of the economy. These factors, both in the short and long run, need to be
Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence
• Sociological and psychological factors regarding employees and customers.
These need to be considered when changes are being made.
• Environment factors. The impact on the physical environment must be
assessed in many decision-making situations.
Other factors include 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 that may be large.
These environments are growing more complex every day. Therefore, making decisions today is indeed a complex task. For further discussion, see Charles (2018). For how
to make effective decisions under uncertainty and pressure, see Zane (2016).
Because of these trends and changes, it is nearly impossible to rely on a trialand-error approach to management. 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. Further, many tools that are evolving impact
even the very existence of several decision-making tasks that are being automated.
This impacts future demand for knowledge workers and begs many legal and societal
impact questions.
Data and Its Analysis in Decision Making
We will see several times in this book how an entire industry can employ analytics to
develop reports on what is happening, predict what is likely to happen, and then make
decisions to make the best use of the situation at hand. These steps require an organization to collect and analyze vast stores of data. In general, the amount of data doubles
every two years. From traditional uses in payroll and bookkeeping functions, computerized systems are now used for 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 these 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 cloud-based technologies, in many cases accessed through mobile devices. Analytics
and BI tools such as data warehousing, data mining, online analytical processing (OLAP),
dashboards, and the use of cloud-based systems for decision support are the cornerstones
of today’s modern management. Managers must have high-speed, networked information
systems (wired or wireless) to assist them with their most important task: making decisions. In many cases, such decisions are routinely being fully automated (see Chapter 2),
eliminating the need for any managerial intervention.
Technologies for Data Analysis and Decision Support
Besides the obvious growth in hardware, software, and network capacities, some developments have clearly contributed to facilitating the growth of decision support and analytics technologies in a number of ways:
• 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 collaboration 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
Part I • Introduction to Analytics and AI
costly. Information systems can improve the collaboration process of a group and
enable its members to be at different locations (saving travel costs). More critically,
such supply chain collaboration permits manufacturers to know about the changing
patterns of demand in near real time and thus react to marketplace changes faster.
For a comprehensive coverage and the impact of AI, see Chapters 2, 10, and 14.
• Improved data management. Many decisions involve complex computations.
Data for these can be stored in different databases anywhere in the organization
and even possibly outside the organization. The data may include text, sound,
graphics, and video, and these can be in different languages. Many times it is necessary to transmit data quickly from distant locations. Systems today can search, store,
and transmit needed data quickly, economically, securely, and transparently. See
Chapters 3 and 9 and the online chapter for details.
• Managing giant data warehouses and Big Data. Large data warehouses
(DWs), like the ones operated by Walmart, contain huge amounts of data. Special
methods, including parallel computing and Hadoop/Spark, are available to organize, search, and mine the data. The costs related to data storage and mining are
declining rapidly. 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 of organizational performance that was not possible in the past. See Chapter 9 for details.
• 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. See Chapters 4–7.
• Overcoming cognitive limits in processing and storing information. The
human mind has only a limited ability to process and store information. People
sometimes find it difficult to recall and use information 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. One way to overcome humans’ cognitive limitations is to use AI support.
For coverage of cognitive aspects, see Chapter 6.
• 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 various stakeholders. Knowledge management systems (KMS) have
become sources of formal and informal support for decision making to managers, although sometimes they may not even be called KMS. Technologies such as
text analytics and IBM Watson are making it possible to generate value from such
knowledge stores. (See Chapters 6 and 12 for details.
• Anywhere, anytime support. Using wireless technology, managers can access
information anytime and from any place, analyze and interpret it, and communicate
with those using it. 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 the use of computerized decision
support since the late 1960s, especially since the mid-1990s. The growth of mobile
technologies, social media platforms, and analytical tools has enabled a different
level of information systems (IS) to support managers. This growth in providing
Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence
data-driven support for any decision extends not just to managers but also to consumers. We will first study an overview of technologies that have been broadly
referred to as BI. From there we will broaden our horizons to introduce various
types of analytics.
• Innovation and artificial intelligence. Because of the complexities in the
decision-making process discussed earlier and the environment surrounding the
process, a more innovative approach is frequently need. A major facilitation of
innovation is provided by AI. Almost every step in the decision-making process can
be influenced by AI. AI is also integrated with analytics, creating synergy in making
decisions (Section 1.8).
Why is it difficult to make organizational decisions?
Describe the major steps in the decision-making process.
Describe the major external environments that can impact decision making.
What are some of the key system-oriented trends that have fostered IS-supported
decision making to a new level?
5. List some capabilities of information technologies that can facilitate managerial decision making.
In this section, we focus on some classical decision-making fundamentals and in more
detail on the decision-making process. These two concepts will help us ground much of
what we will learn in terms of analytics, data science, and artificial intelligence.
Decision making is a process of choosing among two or more alternative courses of
action for the purpose of attaining one or more goals. According to Simon (1977), managerial decision making is synonymous with the entire management process. Consider
the important managerial function of planning. Planning involves a series of decisions:
What should be done? When? Where? Why? How? By whom? Managers set goals, or plan;
hence, planning implies decision making. Other managerial functions, such as organizing
and controlling, also involve decision making.
Simon’s Process: Intelligence, Design, and Choice
It is advisable to follow a systematic decision-making process. Simon (1977) said that
this involves three major phases: intelligence, design, and choice. He later added a
fourth phase: implementation. Monitoring can be considered a fifth phase—a form of
feedback. However, we view monitoring as the intelligence phase applied to the implementation phase. Simon’s model is the mo…
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