University of Virginia Writing Business Intelligence Discussion Questions

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University of Virginia

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I’m trying to learn for my Writing class and I’m stuck. Can you help?

Chapter 10 –discussion question #1-2 & exercise 1 & 7

1. Based upon the current state of the art of robotics applications, which industries are most likely to embrace 

robotics? Why?

2. Watch the following two videos:  and  for a 

different view on impact of AI on future jobs. What are your takeaways from these videos? What is the more likely scenario in your view? How can you prepare for 

the day when humans indeed may not need to apply for many jobs?

Exercise 1:

Identify applications other than those discussed in this chapter where Pepper is being used for commercial and personal purposes.

Exercise 7:

Conduct research to identify the most recent developments in self-driving cars.

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ELEVENTH EDITION ANALYTICS, DATA SCIENCE, & ARTIFICIAL INTELLIGENCE SYSTEMS FOR DECISION SUPPORT Ramesh Sharda Oklahoma State University Dursun Delen Oklahoma State University Efraim Turban University of Hawaii Vice President of Courseware Portfolio Management: Andrew Gilfillan Executive Portfolio Manager: Samantha Lewis Team Lead, Content Production: Laura Burgess Content Producer: Faraz Sharique Ali Portfolio Management Assistant: Bridget Daly Director of Product Marketing: Brad Parkins Director of Field Marketing: Jonathan Cottrell Product Marketing Manager: Heather Taylor Field Marketing Manager: Bob Nisbet Product Marketing Assistant: Liz Bennett Field Marketing Assistant: Derrica Moser Senior Operations Specialist: Diane Peirano Senior Art Director: Mary Seiner Interior and Cover Design: Pearson CSC Cover Photo: Phonlamai Photo/Shutterstock Senior Product Model Manager: Eric Hakanson Manager, Digital Studio: Heather Darby Course Producer, MyLab MIS: Jaimie Noy Digital Studio Producer: Tanika Henderson Full-Service Project Manager: Gowthaman Sadhanandham Full Service Vendor: Integra Software Service Pvt. 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Copyright © 2020, 2015, 2011 by Pearson Education, Inc. 221 River Street, Hoboken, NJ 07030. All rights reserved. Manufactured in the United States of America. This publication is protected by Copyright, and permission should be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. For information regarding permissions, request forms and the appropriate contacts within the Pearson Education Global Rights & Permissions Department, please visit Acknowledgments of third-party content appear on the appropriate page within the text, which constitutes an extension of this copyright page. Unless otherwise indicated herein, any third-party trademarks that may appear in this work are the property of their respective owners and any references to third-party trademarks, logos or other trade dress are for demonstrative or descriptive purposes only. Such references are not intended to imply any sponsorship, endorsement, authorization, or promotion of Pearson’s products by the owners of such marks, or any relationship between the owner and Pearson Education, Inc. or its affiliates, authors, licensees or distributors. Library of Congress Cataloging-in-Publication Data Library of Congress Cataloging in Publication Control Number: 2018051774 ISBN 10: 0-13-519201-3 ISBN 13: 978-0-13-519201-6 BRIEF CONTENTS Preface xxv About the Authors PART I Introduction to Analytics and AI Chapter 1 Chapter 2 Chapter 3 PART II Chapter 6 Chapter 7 Chapter 9 Chapter 12 Chapter 13 193 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 388 194 459 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 PART V 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 PART IV 1 Predictive Analytics/Machine Learning Chapter 4 Chapter 5 PART III xxxiv 579 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 725 Chapter 14 Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 726 Glossary 770 Index 785 iii CONTENTS Preface xxv About the Authors PART I xxxiv Introduction to Analytics and AI 1 Chapter 1 Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support 2 1.1 1.2 1.3 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! 11 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 0 APPLICATION CASE 1.2 SNAP DSS Helps OneNet Make Telecommunications Rate Decisions 20 1.4 iv 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 Contents 1.5 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 34 Prescriptive Analytics 34 0 APPLICATION CASE 1.6 A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates 35 1.6 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 1.7 Artificial Intelligence Overview What Is Artificial Intelligence? 52 The Major Benefits of AI 52 The Landscape of AI 52 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 1.8 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 62 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 v vi Contents The Book’s Web Site 67 Chapter Highlights 67 Questions for Discussion References • 68 Key Terms 68 • Exercises 69 70 Chapter 2 Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications 2.1 2.2 2.3 73 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? 2.4 Major AI Technologies and Some Derivatives Intelligent Agents 87 Machine Learning 88 86 87 0 APPLICATION CASE 2.2 How Machine Learning Is Improving Work in Business 89 2.5 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 Contents 2.6 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 2.7 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 103 100 101 0 APPLICATION CASE 2.5 US Bank Customer Recognition and Services 104 2.8 AI in Human Resource Management (HRM) AI in HRM: An Overview 105 AI in Onboarding 105 105 0 APPLICATION CASE 2.6 How Alexander Mann Solutions (AMS) Is Using AI to Support the Recruiting Process 106 2.9 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 108 107 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 112 Questions for Discussion References • Key Terms 113 113 • Exercises 114 114 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 vii viii Contents 3.4 Art and Science of Data Preprocessing 129 0 APPLICATION CASE 3.2 Improving Student Retention with Data-Driven Analytics 133 3.5 Statistical Modeling for Business Analytics Descriptive Statistics for Descriptive Analytics 140 139 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 3.6 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 3.7 Business Reporting 163 0 APPLICATION CASE 3.5 Flood of Paper Ends at FEMA 3.8 165 Data Visualization 166 Brief History of Data Visualization 167 0 APPLICATION CASE 3.6 Macfarlan Smith Improves Operational Performance Insight with Tableau Online 169 3.9 Different Types of Charts and Graphs 171 Basic Charts and Graphs 171 Specialized Charts and Graphs 172 Which Chart or Graph Should You Use? 174 3.10 Emergence of Visual Analytics 176 Visual Analytics 178 High-Powered Visual Analytics Environments 180 3.11 Information Dashboards 182 Contents 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 188 Questions for Discussion References PART II • Key Terms 190 189 • Exercises 190 192 Predictive Analytics/Machine Learning 193 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 4.3 Data Mining Versus Statistics 208 Data Mining Applications 208 0 APPLICATION CASE 4.3 Predictive Analytic and Data Mining Help Stop Terrorist Funding 210 4.4 Data Mining Process 211 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 ix x Contents 4.5 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 4.6 Association Rule Mining 232 Data Mining Software Tools 236 0 APPLICATION CASE 4.6 Data Mining goes to Hollywood: Predicting Financial Success of Movies 239 4.7 Data Mining Privacy Issues, Myths, and Blunders 242 0 APPLICATION CASE 4.7 Predicting Customer Buying Patterns—The Target Story 243 Data Mining Myths and Blunders 244 Chapter Highlights 246 Questions for Discussion References • 247 Key Terms 247 • Exercises 248 250 Chapter 5 Machine-Learning Techniques for Predictive Analytics 5.1 5.2 251 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 5.3 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 5.4 Support Vector Machines 263 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 Contents 5.5 5.6 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 5.7 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 5.8 5.9 Bayesian Networks 287 How Does BN Work? 287 How Can BN Be Constructed? 288 Ensemble Modeling 293 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 306 Questions for Discussion Internet Exercises 312 • Key Terms 308 • 308 • Exercises References 309 313 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 6.3 Basics of “Shallow” Neural Networks 325 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 xi xii Contents 6.4 6.5 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 6.6 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 6.7 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 6.8 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 6.9 6.10 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 381 Questions for Discussion References 385 • Key Terms 383 383 • Exercises 384 Contents 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 7.3 Natural Language Processing (NLP) 397 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 7.4 Text Mining Applications Marketing Applications 403 Security Applications 403 Biomedical Applications 404 402 0 APPLICATION CASE 7.3 Mining for Lies 404 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 7.5 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 7.6 Sentiment Analysis 418 0 APPLICATION CASE 7.6 Creating a Unique Digital Experience to Capture Moments That Matter at Wimbledon 419 7.7 7.8 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 xiii xiv Contents 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 7.9 7.10 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 447 441 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 455 Questions for Discussion References PART III • 456 Key Terms 456 • Exercises 456 457 Prescriptive Analytics and Big Data 459 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 8.3 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 Contents 8.4 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 8.5 Decision Modeling with Spreadsheets 473 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 8.6 Mathematical Programming Optimization 477 0 APPLICATION CASE 8.6 Mixed-Integer Programming Model Helps the University of Tennessee Medical Center with Scheduling Physicians 478 8.7 8.8 8.9 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 493 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 498 8.10 Visual Interactive Simulation 500 Conventional Simulation Inadequacies 500 Visual Interactive Simulation 500 xv xvi Contents 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 501 Chapter Highlights 505 Questions for Discussion References • Key Terms 505 505 • Exercises 506 508 Chapter 9 Big Data, Cloud Computing, and Location Analytics: Concepts and Tools 509 9.1 9.2 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 9.3 Fundamentals of Big Data Analytics 519 Business Problems Addressed by Big Data Analytics 521 0 APPLICATION CASE 9.2 Combines Multiple Datasets to Understand Customer Journeys 522 9.4 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 529 0 APPLICATION CASE 9.4 Understanding Quality and Reliability of Healthcare Support Information on Twitter 531 9.5 9.6 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 9.7 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 Contents 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 9.8 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 9.9 Cloud Computing and Business Analytics Data as a Service (DaaS) 558 557 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 9.10 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 574 Questions for Discussion References 576 • Key Terms 575 575 • Exercises 575 xvii xviii Contents PART IV Robotics, Social Networks, AI and IoT 579 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 AGROBOT 594 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 606 Questions for Discussion References 607 • Key Terms 606 606 • Exercises 607 Contents Chapter 11 Group Decision Making, Collaborative Systems, and AI Support 11.1 11.2 11.3 11.4 11.5 11.6 610 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 xix xx Contents 11.7 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 635 633 0 APPLICATION CASE 11.2 How InnoCentive Helped GSK Solve a Difficult Problem 636 11.8 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 11.9 639 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 644 Questions for Discussion References • 645 Key Terms 640 645 • Exercises 645 646 Chapter 12 Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants, and Robo Advisors 648 12.1 12.2 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 653 649 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 0 APPLICATION CASE 12.2 VisiRule 656 Recommendation Systems 657 0 APPLICATION CASE 12.3 Netflix Recommender: A Critical Success Factor 658 12.3 12.4 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 Contents Enterprise Chatbots: Marketing and Customer Experience 665 0 APPLICATION CASE 12.4 WeChat’s Super Chatbot 666 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 12.5 12.6 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 676 676 0 APPLICATION CASE 12.7 Betterment, the Pioneer of Financial Robo Advisors 677 12.7 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 683 Questions for Discussion References • Key Terms 684 683 • Exercises 684 685 Chapter 13 The Internet of Things as a Platform for Intelligent Applications 13.1 13.2 687 Opening Vignette: CNH Industrial Uses the Internet of Things to Excel 688 Essentials of IoT 689 Definitions and Characteristics 690 xxi xxii Contents 13.3 13.4 13.5 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 13.6 13.7 13.8 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 13.9 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 714 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 721 Questions for Discussion References PART V • Key Terms 722 721 • Exercises 722 722 Caveats of Analytics and AI 725 Chapter 14 Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 14.1 14.2 14.3 14.4 14.5 726 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 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 xxiv Contents 14.6 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 14.7 14.8 14.9 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 Is Conducting AI 762 The U.S.–China Competition: Who Will Control AI? 764 The Largest Opportunity in Business 764 Conclusion 764 Chapter Highlights 765 Questions for Discussion References Glossary Index 770 785 767 • Key Terms 766 766 • Exercises 766 753 PREFACE Analytics has become the technology driver of this decade. Companies such as IBM, Oracle, Microsoft, and others are creating new organizational units focused on analytics that help businesses become more effective and efficient in their operations. Decision makers are using 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. xxv xxvi Preface 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 ( We provide links to additional learning materials and software tutorials through a special section of the book Web site. WHAT’S NEW IN THE ELEVENTH EDITION? 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 Preface 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 material). 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 xxvii xxviii Preface 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 modeling. • 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 ( We encourage the instructors to register and join 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. THE SUPPLEMENT PACKAGE: PEARSONHIGHERED.COM/SHARDA A comprehensive and flexible technology-support package is available to enhance the teaching and learning experience. The following instructor and student supplements are available on the book’s Web site, • 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 • 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 The TestGen is also available in Respondus and can be found on • PowerPoint slides. PowerPoint slides are available that illuminate and build on key concepts in the text. Faculty can download the PowerPoint slides from ACKNOWLEDGMENTS Many individuals have provided suggestions and criticisms since the publication of the first edition of this book. Dozens of students participated in class testing of various chapters, software, and problems and assisted in collecting material. It is not possible to name everyone who participated in this project, but our thanks go to all of them. Certain individuals made significant contributions, and they deserve special recognition. First, we appreciate the efforts of those individuals who provided formal reviews of the first through eleventh editions (school affiliations as of the date of review): Robert Blanning, Vanderbilt University Ranjit Bose, University of New Mexico xxx Preface 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 xxxii Preface 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, 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, California). 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. R.S. D.D. E.T. Note that Web site URLs are dynamic. As this book went to press, we verified that all the cited Web sites were active and valid. Web sites to which we refer in the text sometimes change or are discontinued because companies change names, are bought or sold, merge, or fail. Sometimes Web sites are down for maintenance, repair, or redesign. Most organizations have dropped the initial “www” designation for their sites, but some still use it. If you have a problem connecting to a Web site that we mention, please be patient and simply run a Web search to try to identify the new site. Most times, the new site can be found quickly. Some sites also require a free registration before allowing you to see the content. We apologize in advance for this inconvenience. ABOUT THE AUTHORS Ramesh Sharda (M.B.A., Ph.D., University of Wisconsin–Madison) is 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 of INFORMS. 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 xxxiv 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. xxxv This page is intentionally left blank P A R I T Introduction to Analytics and AI 1 CHAPTER 1 Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support LEARNING OBJECTIVES 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 ■■ T 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 2 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. THE PROBLEM 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. 3 4 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. THE SOLUTION 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). THE RESULTS 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) (1:31 min.) (2017); (2) (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. (accessed September 2018); H. Srikanthan. “KONE Improves ‘People Flow’ in 1.1 Million Elevators with IBM Watson IoT.” Generis. (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. (accessed September 2018). Chapter 1 • Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence u QUESTIONS FOR THE OPENING VIGNETTE 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? WHAT CAN WE LEARN FROM THIS VIGNETTE? 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. 1.2 CHANGING BUSINESS ENVIRONMENTS AND EVOLVING NEEDS FOR DECISION SUPPORT AND ANALYTICS 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. 5 6 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: 1. 2. 3. 4. 5. 6. 7. 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 considered. 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 7 8 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). u SECTION 1.2 REVIEW QUESTIONS 1. 2. 3. 4. 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. 1.3 DECISION-MAKING PROCESSES AND COMPUTERIZED DECISION SUPPORT FRAMEWORK 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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Business Intelligence Questions

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Business Intelligence Questions
Based on the current state of the art of robots applications, different industries are most
likely to embrace robots. The medical and healthcare industry is the first one most likely to
embrace robots. Robots are being used to assist surgeons around the world. Hospitals are also
making an effort to use robots to offer social and emotional support to patients and their families
(Ramesh Sharda, Dursun Delen & Efraim Turban, 2019). The second one is the agriculture
industry which is likely to embrace robots because of continued labor shortages and AI data
analytics. Robots are likely to be welcomed for emergency and first response to rescue people.
The defense and law enforcement is also expected to embrace robots...

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Rice University

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