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• Title Page
• Copyright Page
• Brief Contents
• Contents
• Preface
• About the Authors
• Part I Introduction to Analytics and AI
• Chapter 1 Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence:
• 1.1 Opening Vignette: How Intelligent Systems Work for KONE Elevators and Escalators Company
• 1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics
• Decision-Making Process
• The Influence of the External and Internal Environments on the Process
• Data and Its Analysis in Decision Making
• Technologies for Data Analysis and Decision Support
• 1.3 Decision-Making Processes and Computerized Decision Support Framework
• Simon’s Process: Intelligence, Design, and Choice
• The Intelligence Phase: Problem (or Opportunity) Identification
• Application Case 1.1 Making Elevators Go Faster
• The Design Phase
• The Choice Phase
• The Implementation Phase
• The Classical Decision Support System Framework
• A DSS Application
• Components of a Decision Support System
• The Data Management Subsystem
• The Model Management Subsystem
• Application Case 1.2 SNAP DSS Helps OneNet Make Telecommunications Rate Decisions
• The User Interface Subsystem
• The Knowledge-Based Management Subsystem
• 1.4 Evolution of Computerized Decision Support to Business Intelligence/Analytics/Data Science
• A Framework for Business Intelligence
• The Architecture of BI
• The Origins and Drivers of BI
• Data Warehouse as a Foundation for Business Intelligence
• Transaction Processing versus Analytic Processing
• A Multimedia Exercise in Business Intelligence
• 1.5 Analytics Overview
• Descriptive Analytics
• Application Case 1.3 An Post and the Use of Data Visualization in Daily Postal Operations
• Application Case 1.4 Siemens Reduces Cost with the Use of Data Visualization
• Predictive Analytics
• Application Case 1.5 SagaDigits and the Use of Predictive Analytics
• Prescriptive Analytics
• Application Case 1.6 A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise
• 1.6 Analytics Examples in Selected Domains
• Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics
• Analytics Applications in Healthcare—Humana Examples
• Application Case 1.7 Image Analysis Helps Estimate Plant Cover
• 1.7 Artificial Intelligence Overview
• What Is Artificial Intelligence
• The Major Benefits of AI
• The Landscape of AI
• Application Case 1.8 AI Increases Passengers’ Comfort and Security in Airports and Borders
• The Three Flavors of AI Decisions
• Autonomous AI
• Societal Impacts
• Application Case 1.9 Robots Took the Job of Camel-Racing Jockeys for Societal Benefits
• 1.8 Convergence of Analytics and AI
• Major Differences between Analytics and AI
• Why Combine Intelligent Systems
• How Convergence Can Help
• Big Data Is Empowering AI Technologies
• The Convergence of AI and the IoT
• The Convergence with Blockchain and Other Technologies
• Application Case 1.10 Amazon Go Is Open for Business
• IBM and Microsoft Support for Intelligent Systems Convergence
• 1.9 Overview of the Analytics Ecosystem
• 1.10 Plan of the Book
• 1.11 Resources, Links, and the Teradata University Network Connection
• Resources and Links
• Vendors, Products, and Demos
• Periodicals
• The Teradata University Network Connection
• The Book’s Web Site
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• Chapter 2 Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications
• 2.1 Opening Vignette: INRIX Solves Transportation Problems
• 2.2 Introduction to Artificial Intelligence
• Definitions
• Major Characteristics of AI Machines
• Major Elements of AI
• AI Applications
• Major Goals of AI
• Drivers of AI
• Benefits of AI
• Some Limitations of AI Machines
• Three Flavors of AI Decisions
• Artificial Brain
• 2.3 Human and Computer Intelligence
• What Is Intelligence
• How Intelligent Is AI
• Measuring AI
• Application Case 2.1 How Smart Can a Vacuum Cleaner Be
• 2.4 Major AI Technologies and Some Derivatives
• Intelligent Agents
• Machine Learning
• Application Case 2.2 How Machine Learning Is Improving Work in Business
• Machine and Computer Vision
• Robotic Systems
• Natural Language Processing
• Knowledge and Expert Systems and Recommenders
• Chatbots
• Emerging AI Technologies
• 2.5 AI Support for Decision Making
• Some Issues and Factors in Using AI in Decision Making
• AI Support of the Decision-Making Process
• Automated Decision Making
• Application Case 2.3 How Companies Solve Real-World Problems Using Google’s Machine-Learning Tools
• Conclusion
• 2.6 AI Applications in Accounting
• AI in Accounting: An Overview
• AI in Big Accounting Companies
• Accounting Applications in Small Firms
• Application Case 2.4 How EY, Deloitte, and PwC Are Using AI
• Job of Accountants
• 2.7 AI Applications in Financial Services
• AI Activities in Financial Services
• AI in Banking: An Overview
• Illustrative AI Applications in Banking
• Insurance Services
• Application Case 2.5 AI in China’s Financial Sector
• 2.8 AI in Human Resource Management (HRM)
• AI in HRM: An Overview
• AI in Onboarding
• Application Case 2.6 How Alexander Mann Solutions (AMS) Is Using AI to Support the Recruiting Proces
• Introducing AI to HRM Operations
• 2.9 AI in Marketing, Advertising, and CRM
• Overview of Major Applications
• AI Marketing Assistants in Action
• Customer Experiences and CRM
• Application Case 2.7 Kraft Foods Uses AI for Marketing and CRM
• Other Uses of AI in Marketing
• 2.10 AI Applications in Production-Operation Management (POM)
• AI in Manufacturing
• Implementation Model
• Intelligent Factories
• Logistics and Transportation
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• Chapter 3 Nature of Data, Statistical Modeling, and Visualization
• 3.1 Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Dr
• 3.2 Nature of Data
• 3.3 Simple Taxonomy of Data
• Application Case 3.1 Verizon Answers the Call for Innovation: The Nation’s Largest Network Provide
• 3.4 Art and Science of Data Preprocessing
• Application Case 3.2 Improving Student Retention with Data-Driven Analytics
• 3.5 Statistical Modeling for Business Analytics
• Descriptive Statistics for Descriptive Analytics
• Measures of Centrality Tendency (Also Called Measures of Location or Centrality)
• Arithmetic Mean
• Median
• Mode
• Measures of Dispersion (Also Called Measures of Spread or Decentrality)
• Range
• Variance
• Standard Deviation
• Mean Absolute Deviation
• Quartiles and Interquartile Range
• Box-and-Whiskers Plot
• Shape of a Distribution
• Application Case 3.3 Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and De
• 3.6 Regression Modeling for Inferential Statistics
• How Do We Develop the Linear Regression Model
• How Do We Know If the Model Is Good Enough
• What Are the Most Important Assumptions in Linear Regression
• Logistic Regression
• Time-Series Forecasting
• Application Case 3.4 Predicting NCAA Bowl Game Outcomes
• 3.7 Business Reporting
• Application Case 3.5 Flood of Paper Ends at FEMA
• 3.8 Data Visualization
• Brief History of Data Visualization
• Application Case 3.6 Macfarlan Smith Improves Operational Performance Insight with Tableau Online
• 3.9 Different Types of Charts and Graphs
• Basic Charts and Graphs
• Specialized Charts and Graphs
• Which Chart or Graph Should You Use
• 3.10 Emergence of Visual Analytics
• Visual Analytics
• High-Powered Visual Analytics Environments
• 3.11 Information Dashboards
• Application Case 3.7 Flink Labs and Dashboard Applications Development
• Dashboard Design
• Application Case 3.8 Visual Analytics Helps Energy Supplier Make Better Connections
• What to Look for in a Dashboard
• Best Practices in Dashboard Design
• Benchmark Key Performance Indicators with Industry Standards
• Wrap the Dashboard Metrics with Contextual Metadata
• Validate the Dashboard Design by a Usability Specialist
• Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard
• Enrich the Dashboard with Business-User Comments
• Present Information in Three Different Levels
• Pick the Right Visual Construct Using Dashboard Design Principles
• Provide for Guided Analytics
• Chapter Highlights
• Key terms
• Questions for Discussion
• Exercises
• References
• PART II Predictive Analytics/Machine Learning
• Chapter 4 Data Mining Process, Methods, and Algorithms
• 4.1 Opening Vignette: Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Figh
• 4.2 Data Mining Concepts
• Application Case 4.1 Visa Is Enhancing the Customer Experience While Reducing Fraud with Predictive
• Definitions, Characteristics, and Benefits
• How Data Mining Works
• Application Case 4.2 American Honda Uses Advanced Analytics to Improve Warranty Claims
• Data Mining versus Statistics
• 4.3 Data Mining Applications
• Application Case 4.3 Predictive Analytic and Data Mining Help Stop Terrorist Funding
• 4.4 Data Mining Process
• Step 1: Business Understanding
• Step 2: Data Understanding
• Step 3: Data Preparation
• Step 4: Model Building
• Application Case 4.4 Data Mining Helps in Cancer Research
• Step 5: Testing and Evaluation
• Step 6: Deployment
• Other Data Mining Standardized Processes and Methodologies
• 4.5 Data Mining Methods
• Classification
• Estimating the True Accuracy of Classification Models
• Estimating the Relative Importance of Predictor Variables
• Cluster Analysis for Data Mining
• Application Case 4.5 Influence Health Uses Advanced Predictive Analytics to Focus on the Factors Tha
• Association Rule Mining
• 4.6 Data Mining Software Tools
• Application Case 4.6 Data Mining Goes to Hollywood: Predicting Financial Success of Movies
• 4.7 Data Mining Privacy Issues, Myths, and Blunders
• Application Case 4.7 Predicting Customer Buying Patterns—The Target Story
• Data Mining Myths and Blunders
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• Chapter 5 Machine-Learning Techniques for Predictive Analytics
• 5.1 Opening Vignette: Predictive Modeling Helps Better Understand and Manage Complex Medical Procedu
• 5.2 Basic Concepts of Neural Networks
• Biological versus Artificial Neural Networks
• Application Case 5.1 Neural Networks Are Helping to Save Lives in the Mining Industry
• 5.3 Neural Network Architectures
• Kohonen’s Self-Organizing Feature Maps
• Hopfield Networks
• Application Case 5.2 Predictive Modeling Is Powering the Power Generators
• 5.4 Support Vector Machines
• Application Case 5.3 Identifying Injury Severity Risk Factors in Vehicle Crashes with Predictive Ana
• Mathematical Formulation of SVM
• Primal Form
• Dual Form
• Soft Margin
• Nonlinear Classification
• Kernel Trick
• 5.5 Process-Based Approach to the Use of SVM
• Support Vector Machines versus Artificial Neural Networks
• 5.6 Nearest Neighbor Method for Prediction
• Similarity Measure: The Distance Metric
• Parameter Selection
• Application Case 5.4 Efficient Image Recognition and Categorization with knn
• 5.7 Naïve Bayes Method for Classification
• Bayes Theorem
• Naïve Bayes Classifier
• Process of Developing a Naïve Bayes Classifier
• Testing Phase
• Application Case 5.5 Predicting Disease Progress in Crohn’s Disease Patients: A Comparison of Anal
• 5.8 Bayesian Networks
• How Does BN Work
• How Can BN Be Constructed
• 5.9 Ensemble Modeling
• Motivation—Why Do We Need to Use Ensembles
• Different Types of Ensembles
• Bagging
• Boosting
• Variants of Bagging and Boosting
• Stacking
• Information Fusion
• Summary—Ensembles Are Not Perfect
• Application Case 5.6 To Imprison or Not to Imprison: A Predictive Analytics–Based Decision Support
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• Internet Exercises
• References
• Chapter 6 Deep Learning and Cognitive Computing
• 6.1 Opening Vignette: Fighting Fraud with Deep Learning and Artificial Intelligence
• 6.2 Introduction to Deep Learning
• Application Case 6.1 Finding the Next Football Star with Artificial Intelligence
• 6.3 Basics of “Shallow” Neural Networks
• Application Case 6.2 Gaming Companies Use Data Analytics to Score Points with Players
• Application Case 6.3 Artificial Intelligence Helps Protect Animals from Extinction
• 6.4 Process of Developing Neural Network–Based Systems
• Learning Process in ANN
• Backpropagation for ANN Training
• 6.5 Illuminating the Black Box of ANN
• Application Case 6.4 Sensitivity Analysis Reveals Injury Severity Factors in Traffic Accidents
• 6.6 Deep Neural Networks
• Feedforward Multilayer Perceptron (MLP)-Type Deep Networks
• Impact of Random Weights in Deep MLP
• More Hidden Layers versus More Neurons
• Application Case 6.5 Georgia DOT Variable Speed Limit Analytics Help Solve Traffic Congestions
• 6.7 Convolutional Neural Networks
• Convolution Function
• Pooling
• Image Processing Using Convolutional Networks
• Application Case 6.6 From Image Recognition to Face Recognition
• Text Processing Using Convolutional Networks
• 6.8 Recurrent Networks and Long Short-Term Memory Networks
• Application Case 6.7 Deliver Innovation by Understanding Customer Sentiments
• LSTM Networks Applications
• 6.9 Computer Frameworks for Implementation of Deep Learning
• Torch
• Caffe
• TensorFlow
• Theano
• Keras: An Application Programming Interface
• 6.10 Cognitive Computing
• How Does Cognitive Computing Work
• How Does Cognitive Computing Differ from AI
• Cognitive Search
• IBM Watson: Analytics at Its Best
• Application Case 6.8 IBM Watson Competes against the Best at Jeopardy
• How Does Watson Do It
• What Is the Future for Watson
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• Chapter 7 Text Mining, Sentiment Analysis, and Social Analytics
• 7.1 Opening Vignette: Amadori Group Converts Consumer Sentiments into Near-Real-Time Sales
• 7.2 Text Analytics and Text Mining Overview
• Application Case 7.1 Netflix: Using Big Data to Drive Big Engagement: Unlocking the Power of Analyti
• 7.3 Natural Language Processing (NLP)
• Application Case 7.2 AMC Networks Is Using Analytics to Capture New Viewers, Predict Ratings, and Ad
• 7.4 Text Mining Applications
• Marketing Applications
• Security Applications
• Biomedical Applications
• Application Case 7.3 Mining for Lies
• Academic Applications
• Application Case 7.4 The Magic behind the Magic: Instant Access to Information Helps the Orlando Mag
• 7.5 Text Mining Process
• Task 1: Establish the Corpus
• Task 2: Create the Term–Document Matrix
• Task 3: Extract the Knowledge
• Application Case 7.5 Research Literature Survey with Text Mining
• 7.6 Sentiment Analysis
• Application Case 7.6 Creating a Unique Digital Experience to Capture Moments That Matter at Wimbledo
• Sentiment Analysis Applications
• Sentiment Analysis Process
• Methods for Polarity Identification
• Using a Lexicon
• Using a Collection of Training Documents
• Identifying Semantic Orientation of Sentences and Phrases
• Identifying Semantic Orientation of Documents
• 7.7 Web Mining Overview
• Web Content and Web Structure Mining
• 7.8 Search Engines
• Anatomy of a Search Engine
• 1. Development Cycle
• 2. Response Cycle
• Search Engine Optimization
• Methods for Search Engine Optimization
• Application Case 7.7 Delivering Individualized Content and Driving Digital Engagement: How Barbour C
• 7.9 Web Usage Mining (Web Analytics)
• Web Analytics Technologies
• Web Analytics Metrics
• Web Site Usability
• Traffic Sources
• Visitor Profiles
• Conversion Statistics
• 7.10 Social Analytics
• Social Network Analysis
• Social Network Analysis Metrics
• Application Case 7.8 Tito’s Vodka Establishes Brand Loyalty with an Authentic Social Strategy
• Connections
• Distributions
• Segmentation
• Social Media Analytics
• How Do People Use Social Media
• Measuring the Social Media Impact
• Best Practices in Social Media Analytics
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• PART III Prescriptive Analytics and Big Data
• Chapter 8 Prescriptive Analytics: Optimization and Simulation
• 8.1 Opening Vignette: School District of Philadelphia Uses Prescriptive Analytics to Find Optimal So
• 8.2 Model-Based Decision Making
• Application Case 8.1 Canadian Football League Optimizes Game Schedule
• Prescriptive Analytics Model Examples
• Identification of the Problem and Environmental Analysis
• Application Case 8.2 Ingram Micro Uses Business Intelligence Applications to Make Pricing Decisions
• Model Categories
• 8.3 Structure of Mathematical Models for Decision Support
• The Components of Decision Support Mathematical Models
• The Structure of Mathematical Models
• 8.4 Certainty, Uncertainty, and Risk
• Decision Making under Certainty
• Decision Making under Uncertainty
• Decision Making under Risk (Risk Analysis)
• Application Case 8.3 American Airlines Uses Should-Cost Modeling to Assess the Uncertainty of Bids f
• 8.5 Decision Modeling with Spreadsheets
• Application Case 8.4 Pennsylvania Adoption Exchange Uses Spreadsheet Model to Better Match Children
• Application Case 8.5 Metro Meals on Wheels Treasure Valley Uses Excel to Find Optimal Delivery Route
• 8.6 Mathematical Programming Optimization
• Application Case 8.6 Mixed-Integer Programming Model Helps the University of Tennessee Medical Cente
• Linear Programming Model
• Modeling in LP: An Example
• Implementation
• 8.7 Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking
• Multiple Goals
• Sensitivity Analysis
• What-If Analysis
• Goal Seeking
• 8.8 Decision Analysis with Decision Tables and Decision Trees
• Decision Tables
• Decision Trees
• 8.9 Introduction to Simulation
• Major Characteristics of Simulation
• Application Case 8.7 Steel Tubing Manufacturer Uses a Simulation-Based Production Scheduling System
• Advantages of Simulation
• Disadvantages of Simulation
• The Methodology of Simulation
• Simulation Types
• Monte Carlo Simulation
• Discrete Event Simulation
• Application Case 8.8 Cosan Improves Its Renewable Energy Supply Chain Using Simulation
• 8.10 Visual Interactive Simulation
• Conventional Simulation Inadequacies
• Visual Interactive Simulation
• Visual Interactive Models and DSS
• Simulation Software
• Application Case 8.9 Improving Job-Shop Scheduling Decisions through RFID: A Simulation-Based Assess
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• Chapter 9 Big Data, Cloud Computing, and Location Analytics: Concepts and Tools
• 9.1 Opening Vignette: Analyzing Customer Churn in a Telecom Company Using Big Data Methods
• 9.2 Definition of Big Data
• The “V”s That Define Big Data
• Application Case 9.1 Alternative Data for Market Analysis or Forecasts
• 9.3 Fundamentals of Big Data Analytics
• Business Problems Addressed by Big Data Analytics
• Application Case 9.2 Big Data and Retail Business: The Rise of ABEJA
• 9.4 Big Data Technologies
• MapReduce
• Why Use MapReduce
• Hadoop
• How Does Hadoop Work
• Hadoop Technical Components
• Hadoop: The Pros and Cons
• NoSQL
• Application Case 9.3 eBay’s Big Data Solution
• Application Case 9.4 Understanding Quality and Reliability of Healthcare Support Information on Twit
• 9.5 Big Data and Data Warehousing
• Use Cases for Hadoop
• Use Cases for Data Warehousing
• The Gray Areas (Any One of the Two Would Do the Job)
• Coexistence of Hadoop and Data Warehouse
• 9.6 In-Memory Analytics and Apache Spark
• Application Case 9.5 Databrick’s Apache SparkTM: Asia-Pacific Big Data Processing in Action
• Architecture of Apache SparkTM
• Getting Started with Apache SparkTM
• 9.7 Big Data and Stream Analytics
• Stream Analytics versus Perpetual Analytics
• Critical Event Processing
• Data Stream Mining
• Applications of Stream Analytics
• e-Commerce
• Telecommunications
• Application Case 9.6 Salesforce Is Using Streaming Data to Enhance Customer Value
• Law Enforcement and Cybersecurity
• Power Industry
• Financial Services
• Health Sciences
• Government
• 9.8 Big Data Vendors and Platforms
• Infrastructure Services Providers
• Analytics Solution Providers
• Business Intelligence Providers Incorporating Big Data
• Application Case 9.7 Using Social Media for Nowcasting Flu Activity
• Application Case 9.8 Analyzing Disease Patterns from an Electronic Medical Records Data Warehouse
• 9.9 Cloud Computing and Business Analytics
• Data as a Service (DaaS)
• Software as a Service (SaaS)
• Platform as a Service (PaaS)
• Infrastructure as a Service (IaaS)
• Essential Technologies for Cloud Computing
• Application Case 9.9 Major West Coast Utility Uses Cloud-Mobile Technology to Provide Real-Time Inci
• Cloud Deployment Models
• Major Cloud Platform Providers in Analytics
• Analytics as a Service (AaaS)
• Representative Analytics as a Service Offerings
• Illustrative Analytics Applications Employing the Cloud Infrastructure
• Using Azure IOT, Stream Analytics, and Machine Learning to Improve Mobile Health Care Services
• Gulf Air Uses Big Data to Get Deeper Customer Insight
• Chime Enhances Customer Experience Using Snowflake
• 9.10 Location-Based Analytics for Organizations
• Geospatial Analytics
• Application Case 9.10 GIS and the Indian Retail Industry
• Application Case 9.11 Starbucks Exploits GIS and Analytics to Grow Worldwide
• Real-Time Location Intelligence
• Analytics Applications for Consumers
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• PART IV Robotics, Social Networks, AI, and IoT
• Chapter 10 Robotics: Industrial and Consumer Applications
• 10.1 Opening Vignette: Robots Provide Emotional Support to Patients and Children
• 10.2 Overview of Robotics
• 10.3 History of Robotics
• 10.4 Illustrative Applications of Robotics
• Changing Precision Technology
• Adidas
• BMW Employs Collaborative Robots
• Tega
• San Francisco Burger Eatery
• Spyce
• Mahindra & Mahindra Ltd
• Robots in the Defense Industry
• Pepper
• Da Vinci Surgical System
• Snoo–A Robotic Crib
• MEDi
• Care-E Robot
• AGROBOT
• 10.5 Components of Robots
• 10.6 Various Categories of Robots
• 10.7 Autonomous Cars: Robots in Motion
• Autonomous Vehicle Development
• Issues with Self-Driving Cars
• 10.8 Impact of Robots on Current and Future Jobs
• 10.9 Legal Implications of Robots and Artificial Intelligence
• Tort Liability
• Patents
• Property
• Taxation
• Practice of Law
• Constitutional Law
• Professional Certification
• Law Enforcement
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• Chapter 11 Group Decision Making, Collaborative Systems, and AI Support
• 11.1 Opening Vignette: Hendrick Motorsports Excels with Collaborative Teams
• 11.2 Making Decisions in Groups: Characteristics, Process, Benefits, and Dysfunctions
• Characteristics of Group Work
• Types of Decisions Made by Groups
• Group Decision-Making Process
• Benefits and Limitations of Group Work
• 11.3 Supporting Group Work and Team Collaboration with Computerized Systems
• Overview of Group Support Systems (GSS)
• Time/Place Framework
• Group Collaboration for Decision Support
• 11.4 Electronic Support for Group Communication and Collaboration
• Groupware for Group Collaboration
• Synchronous versus Asynchronous Products
• Virtual Meeting Systems
• Collaborative Networks and Hubs
• Collaborative Hubs
• Social Collaboration
• Sample of Popular Collaboration Software
• 11.5 Direct Computerized Support for Group Decision Making
• Group Decision Support Systems (GDSS)
• Characteristics of GDSS
• Supporting the Entire Decision-Making Process
• Brainstorming for Idea Generation and Problem Solving
• Group Support Systems
• 11.6 Collective Intelligence and Collaborative Intelligence
• Definitions and Benefits
• Computerized Support to Collective Intelligence
• Application Case 11.1 Collaborative Modeling for Optimal Water Management: The Oregon State Universi
• How Collective Intelligence May Change Work and Life
• Collaborative Intelligence
• How to Create Business Value from Collaboration: The IBM Study
• 11.7 Crowdsourcing as a Method for Decision Support
• The Essentials of Crowdsourcing
• Crowdsourcing for Problem-Solving and Decision Support
• Implementing Crowdsourcing for Problem Solving
• Application Case 11.2 How InnoCentive Helped GSK Solve a Difficult Problem
• 11.8 Artificial Intelligence and Swarm AI Support of Team Collaboration and Group Decision Making
• AI Support of Group Decision Making
• AI Support of Team Collaboration
• Swarm Intelligence and Swarm AI
• Application Case 11.3 XPRIZE Optimizes Visioneering
• 11.9 Human–Machine Collaboration and Teams of Robots
• Human–Machine Collaboration in Cognitive Jobs
• Robots as Coworkers: Opportunities and Challenges
• Teams of collaborating Robots
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• Chapter 12 Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants, a
• 12.1 Opening Vignette: Sephora Excels with Chatbots
• 12.2 Expert Systems and Recommenders
• Basic Concepts of Expert Systems (ES)
• Characteristics and Benefits of ES
• Typical Areas for ES Applications
• Structure and Process of ES
• Application Case 12.1 ES Aid in Identification of Chemical, Biological, and Radiological Agents
• Why the Classical Type of ES Is Disappearing
• Application Case 12.2 VisiRule
• Recommendation Systems
• Application Case 12.3 Netflix Recommender: A Critical Success Factor
• 12.3 Concepts, Drivers, and Benefits of Chatbots
• What Is a Chatbot
• Chatbot Evolution
• Components of Chatbots and the Process of Their Use
• Drivers and Benefits
• Representative Chatbots from around the World
• 12.4 Enterprise Chatbots
• The Interest of Enterprises in Chatbots
• Enterprise Chatbots: Marketing and Customer Experience
• Application Case 12.4 WeChat’s Super Chatbot
• Application Case 12.5 How Vera Gold Mark Uses Chatbots to Increase Sales
• Enterprise Chatbots: Financial Services
• Enterprise Chatbots: Service Industries
• Chatbot Platforms
• Application Case 12.6 Transavia Airlines Uses Bots for Communication and Customer Care Delivery
• Knowledge for Enterprise Chatbots
• 12.5 Virtual Personal Assistants
• Assistant for Information Search
• If You Were Mark Zuckerberg, Facebook CEO
• Amazon’s Alexa and Echo
• Apple’s Siri
• Google Assistant
• Other Personal Assistants
• Competition among Large Tech Companies
• Knowledge for Virtual Personal Assistants
• 12.6 Chatbots as Professional Advisors (Robo Advisors)
• Robo Financial Advisors
• Evolution of Financial Robo Advisors
• Robo Advisors 2.0: Adding the Human Touch
• Application Case 12.7 Barclays: AI and Chatbots in Banking
• Managing Mutual Funds Using AI
• Other Professional Advisors
• IBM Watson
• 12.7 Implementation Issues
• Technology Issues
• Disadvantages and Limitations of Bots
• Quality of Chatbots
• Setting Up Alexa’s Smart Home System
• Constructing Bots
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• Chapter 13 The Internet of Things as a Platform for Intelligent Applications
• 13.1 Opening Vignette: CNH Industrial Uses the Internet of Things to Excel
• 13.2 Essentials of IoT
• Definitions and Characteristics
• The IoT Ecosystem
• Structure of IoT Systems
• 13.3 Major Benefits and Drivers of IoT
• Major Benefits of IoT
• Major Drivers of IoT
• Opportunities
• 13.4 How IoT Works
• IoT and Decision Support
• 13.5 Sensors and Their Role in IoT
• Brief Introduction to Sensor Technology
• Application Case 13.1 Using Sensors, IoT, and AI for Environmental Control at the Athens Internation
• How Sensors Work with IoT
• Application Case 13.2 Rockwell Automation Monitors Expensive Oil and Gas Exploration Assets to Predi
• Sensor Applications and Radio-Frequency Identification (RFID) Sensors
• 13.6 Selected IoT Applications
• A Large-Scale IoT in Action
• Examples of Other Existing Applications
• 13.7 Smart Homes and Appliances
• Typical Components of Smart Homes
• Smart Appliances
• A Smart Home Is Where the Bot Is
• Barriers to Smart Home Adoption
• 13.8 Smart Cities and Factories
• Application Case 13.3 Amsterdam on the Road to Become a Smart City
• Smart Buildings: From Automated to Cognitive Buildings
• Smart Components in Smart Cities and Smart Factories
• Application Case 13.4 How IBM Is Making Cities Smarter Worldwide
• Improving Transportation in the Smart City
• Combining Analytics and IoT in Smart City Initiatives
• Bill Gates’ Futuristic Smart City
• Technology Support for Smart Cities
• 13.9 Autonomous (Self-Driving) Vehicles
• The Developments of Smart Vehicles
• Application Case 13.5 Waymo and Autonomous Vehicles
• Flying Cars
• Implementation Issues in Autonomous Vehicles
• 13.10 Implementing IoT and Managerial Considerations
• Major Implementation Issues
• Strategy for Turning Industrial IoT into Competitive Advantage
• The Future of the IoT
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• PART V Caveats of Analytics and AI
• Chapter 14 Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts
• 14.1 Opening Vignette: Why Did Uber Pay $245 Million to Waymo
• 14.2 Implementing Intelligent Systems: An Overview
• The Intelligent Systems Implementation Process
• The Impacts of Intelligent Systems
• 14.3 Legal, Privacy, and Ethical Issues
• Legal Issues
• Privacy Issues
• Who Owns Our Private Data
• Ethics Issues
• Ethical Issues of Intelligent Systems
• Other Topics in Intelligent Systems Ethics
• 14.4 Successful Deployment of Intelligent Systems
• Top Management and Implementation
• System Development Implementation Issues
• Connectivity and Integration
• Security Protection
• Leveraging Intelligent Systems in Business
• Intelligent System Adoption
• 14.5 Impacts of Intelligent Systems on Organizations
• New Organizational Units and Their Management
• Transforming Businesses and Increasing Competitive Advantage
• Application Case 14.1 How 1-800-Flowers.com Uses Intelligent Systems for Competitive Advantage
• Redesign of an Organization through the Use of Analytics
• Intelligent Systems’ Impact on Managers’ Activities, Performance, and Job Satisfaction
• Impact on Decision Making
• Industrial Restructuring
• 14.6 Impacts on Jobs and Work
• An Overview
• Are Intelligent Systems Going to Take Jobs—My Job
• AI Puts Many Jobs at Risk
• Application Case 14.2 White-Collar Jobs That Robots Have Already Taken
• Which Jobs Are Most in Danger? Which Ones Are Safe
• Intelligent Systems May Actually Add Jobs
• Jobs and the Nature of Work Will Change
• Conclusion: Let’s Be Optimistic
• 14.7 Potential Dangers of Robots, AI, and Analytical Modeling
• Position of AI Dystopia
• The AI Utopia’s Position
• The Open AI Project and the Friendly AI
• The O’Neil Claim of Potential Analytics’ Dangers
• 14.8 Relevant Technology Trends
• Gartner’s Top Strategic Technology Trends for 2018 and 2019
• Other Predictions Regarding Technology Trends
• Summary: Impact on AI and Analytics
• Ambient Computing (Intelligence)
• 14.9 Future of Intelligent Systems
• What Are the Major U.S. High-Tech Companies Doing in the Intelligent Technologies Field
• AI Research Activities in China
• Application Case 14.3 How Alibaba.com Is Conducting AI
• The U.S.–China Competition: Who Will Control AI
• The Largest Opportunity in Business
• Conclusion
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• Glossary
• Index