Business Intelligence, Analytics, Data Science, and AI: A Managerial Perspective

Business Intelligence, Analytics, Data Science, and AI is your guide to the business-related impact of artificial intelligence, data science and analytics, designed to prepare you for a managerial role. The text's vignettes and cases feature modern companies and non-profit organizations and illustrate capabilities, costs and justifications of BI across various business units. With coverage of many data science/AI applications, you'll explore tools, then learn from various organizations' experiences employing such applications. Ample hands-on practice is provided, can be completed with a range of software, and will help you use analytics as a future manager. The 5th Edition integrates the fully updated content of Analytics, Data Science, and Artificial Intelligence, 11/e and Business Intelligence, Analytics, and Data Science, 4/e into one textbook, strengthened by 4 new chapters that will equip you for today's analytics and AI tech, such as ChatGPT. Examples explore analytics in sports, gaming, agriculture and “data for good.” Front Cover Title Page Copyright Page Pearson’s Commitment to Diversity, Equity, and Inclusion Brief Contents Contents Preface About the Authors PART I Introduction Chapter 1 An Overview of Business Intelligence, Analytics, Data Science, and AI 1.1 Opening Vignette: Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics 1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics Decision-Making Process 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 Analytics in Action 1.1 Making Elevators Go Faster! The Design Phase The Choice Phase The Implementation Phase 1.4 Evolution of Computerized Decision Support to Analytics/Data Science 1.5 A Framework for Business Intelligence Definitions of BI A Brief History of BI The Architecture of BI The Origins and Drivers of BI A Multimedia Exercise in Business Intelligence Transaction Processing versus Analytic Processing Appropriate Planning and Alignment with the Business Strategy Real-Time, On-Demand BI Is Attainable Developing or Acquiring BI Systems Justification and Cost–Benefit Analysis Security and Protection of Privacy Integration of Systems and Applications 1.6 Analytics Overview Descriptive Analytics Predictive Analytics Analytics in Action 1.2 Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capabilities Prescriptive Analytics Analytics in Action 1.3 How Big Will Be the Beef? Understanding Animals’ Eating Behavior and Their Final Weight The Modeling Process and Results Analytics in Action 1.4 A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates Analytics/Data Science/Machine Learning/AI? 1.7 Analytics Examples in Selected Domains Analytics Applications in Healthcare—Humana Examples Analytics in the Retail Value Chain Gaming Industry Applications Analytics in Action 1.5 Gulfstream Park Casino Employs AI to make Optimal Decisions on Slot Machine Placement COVID-19 Analytics Applications Mask Mandates and COVID-19 Spread: Evidence from within State Variation Problem Data Results 1.8 Plan of the Book 1.9 Resources, Links, and the Teradata University for Academics Connection Resources and Links Vendors, Products, and Demos Periodicals The Teradata University for Academics 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: Grant Thornton Employs Aisera Chatbot to Reduce IT Help Desk Burden 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 Three Flavors of AI Decisions Technology Insight 2.1 Augmented Intelligence 2.3 Human and Computer Intelligence What Is Intelligence? How Intelligent Is AI? Measuring AI 2.4 Major AI Technologies and Some Derivatives 2.5 AI Support for Decision Making Technology Insight 2.2 Schrage’s Models for Using AI to Make Decisions 2.6 AI Applications in Various Business Functions Analytics in Action 2.1 How EY, Deloitte, and PwC Are Using AI AI Applications in Financial Services AI in Marketing, Advertising, and CRM 2.7 Introduction to Robotics Analytics in Action 2.2 AI/Analytics in Action What We Can Learn from This Analytics in Action 2.2? 2.8 Illustrative Applications of Robotics Autonomous Cars: Robots in Motion 2.9 Conversational AI—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 2.10 Enterprise Chatbots The Interest of Enterprises in Chatbots Enterprise Chatbots: Marketing and Customer Experience Coca-Cola Analytics in Action 2.3 WeChat’s Super Chatbot Enterprise Chatbots: Financial Services Enterprise Chatbots: Service Industries Chatbot Platforms Knowledge for Enterprise Chatbots Virtual Personal Assistants If You Were Mark Zuckerberg, Facebook CEO Amazon’s Alexa and Echo Apple’s Siri Google Assistant Other Personal Assistants Chatbots as Professional Advisors (Robo Advisors) Robo Financial Advisors Evolution of Financial Robo Advisors Chapter Highlights Key Terms Questions for Discussion Exercises References PART II Descriptive Analytics Chapter 3 Descriptive Analytics I: Nature of Data, Big Data, and Statistical Modeling 3.1 Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing 3.2 The Nature of Data in Analytics 3.3 A Simple Taxonomy of Data 3.4 The Art and Science of Data Preprocessing Analytics in Action 3.1 Improving Student Retention with Data-Driven Analytics 3.5 Definition of Big Data The “V”s That Define Big Data Technology Insights 3.1 The Data Size Is Getting Big, Bigger, and Bigger 3.6 Fundamentals of Big Data Analytics Business Problems Addressed by Big Data Analytics 3.7 Big Data Technologies Hadoop How Does Hadoop Work MapReduce Why Use MapReduce Hadoop Technical Components Hadoop: The Pros and Cons Technology Insights 3.2 A Few Demystifying Facts about Hadoop Spark versus Hadoop NoSQL Data for Good 3.8 Big Data and Stream Analytics Applications of Stream Analytics e-Commerce Telecommunications Law Enforcement and Cybersecurity Power Industry Financial Services Health Sciences Government 3.9 Statistical Modeling for Business Analytics Descriptive Statistics for Descriptive Analytics Measures of Centrality Tendency (May Also Be Called Measures of Location or Centrality) Arithmetic Mean Median Mode Measures of Dispersion Range Variance Standard Deviation Mean Absolute Deviation Quartiles and Interquartile Range Box-and-Whiskers Plot The Shape of a Distribution Technology Insight 3.3 How to Calculate Descriptive Statistics in Microsoft Excel Analytics in Action 3.2 Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and Detect Problems 3.10 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 Analytics in Action 3.3 Predicting NCAA Bowl Game Outcomes Time Series Forecasting Chapter Highlights Key Terms Questions for Discussion Exercises References Chapter 4 Descriptive Analytics II: Business Intelligence Data Warehousing, and Visualization 4.1 Opening Vignette: Targeting Tax Fraud with Data Warehousing and Business Analytics 4.2 Business Intelligence and Data Warehousing What Is a Data Warehouse A Historical Perspective to Data Warehousing Analytics in Action 4.1 Data-Driven Customer Experience in Financial Services Characteristics of Data Warehousing Data Marts Operational Data Stores Enterprise Data Warehouses (EDW) Metadata 4.3 Data Warehousing Process 4.4 Data Warehousing Architectures Alternative Data Warehousing Architectures Which Architecture Is the Best 4.5 Data Management and Warehouse Development Data Warehouse Development Approaches Additional Data Warehouse Development Considerations Technology Insight 4.1 Hosted Data Warehouses Representation of Data in Data Warehouse Analysis of Data in Data Warehouse OLAP versus OLTP OLAP Operations Data Integration and the Extraction, Transformation, and Load (ETL) Processes Data Integration Analytics in Action 4.2 AARP Transforms Its BI Infrastructure and Achieves a 347% ROI in Three Years Extraction, Transformation, and Load 4.6 Data Warehouse Administration, Security Issues, and Future Trends The Future of Data Warehousing Technology Insight 4.2 Data Lakes 4.7 Business Reporting 4.8 Data Visualization A Brief History of Data Visualization 4.9 Different Types of Charts and Graphs Basic Charts and Graphs Specialized Charts and Graphs Which Chart or Graph Should You Use 4.10 The Emergence of Visual Analytics Technology Insights 4.3 Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Visual Analytics Technology Insights 4.4 Telling Great Stories with Data and Visualization High-Powered Visual Analytics Environments 4.11 Information Dashboards Analytics in Action 4.3 Increasing the Efficiency of Social Media Campaign Reporting to Get to Insights Quicker Dashboard Design 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 III Predictive Analytics Chapter 5 Predictive Analytics I: Data Mining Process, Methods, and Algorithms 5.1 Opening Vignette: Police Departments Are Using Predictive Analytics to Foresee and Fight Crime 5.2 Data Mining Concepts and Applications Definitions, Characteristics, and Benefits How Data Mining Works Data Mining versus Statistics 5.3 Data Mining Applications 5.4 Data Mining Process Step 1: Business Understanding Step 2: Data Understanding Step 3: Data Preparation Step 4: Model Building Analytics in Action 5.1 Data Mining Helps in Cancer Research Step 5: Testing and Evaluation Step 6: Deployment Other Data Mining Standardized Processes and Methodologies 5.5 Data Mining Methods Classification Estimating the True Accuracy of Classification Models Cluster Analysis for Data Mining Association Rule Mining 5.6 Data Mining Software Tools Analytics in Action 5.2 Data Mining Goes to Hollywood: Predicting Financial Success of Movies 5.7 Data Mining Privacy Issues, Myths, and Blunders Analytics in Action 5.3 Predicting Customer Buying Patterns—the Target Story Data Mining Myths and Blunders Chapter Highlights Key Terms Questions for Discussion Exercises References Chapter 6 Predictive Analytics II: Text, Web, and Social Media Analytics 6.1 Opening Vignette: Machine versus Human on Jeopardy!: The Story of Watson 6.2 Text Analytics and Text Mining Overview Technology Insights 6.1 Text Mining Terminology 6.3 Natural Language Processing (NLP) Analytics in Action 6.1 Deliver Innovation by Understanding Customer Sentiments 6.4 Text Mining Applications Marketing Applications Security Applications Analytics in Action 6.2 Mining for Lies Biomedical Applications Academic Applications 6.5 Text Mining Process Task 1: Establish the Corpus Task 2: Create the Term–Document Matrix Task 3: Extract the Knowledge Analytics in Action 6.3 Research Literature Survey with Text Mining 6.6 Sentiment Analysis and Topic Modeling Sentiment Analysis Analytics in Action 6.4 Creating a Unique Digital Experience to Capture the Moments That Matter at Wimbledon Sentiment Analysis Applications Sentiment Analysis Process Methods for Polarity Identification Using a Lexicon Using a Collection of Training Documents Technology Insights 6.2 Large Textual Data Sets for Predictive Text Mining and Sentiment Analysis Identifying Semantic Orientation of Sentences and Phrases Identifying Semantic Orientation of Documents Topic Modeling Latent Dirichlet Allocation 6.7 Web Mining Overview Web Content and Web Structure Mining 6.8 Search Engines Anatomy of a Search Engine 1.Development Cycle 2.Response Cycle Search Engine Optimization Technology Insights 6.3 Top 15 Most Popular Search Engines Methods for Search Engine Optimization 6.9 Web Usage Mining (Web Analytics) Web Analytics Technologies Web Analytics Metrics Web Site Usability Traffic Sources Visitor Profiles Conversion Statistics 6.10 Social Analytics Social Network Analysis Social Network Analysis Metrics Connections Distributions Segmentation Social Media Analytics How Do People Use Social Media Analytics in Action 6.5 Increasing the Efficiency of Social Media Campaigns Measuring the Social Media Impact Best Practices in Social Media Analytics Chapter Highlights Key Terms Questions for Discussion Exercises References Chapter 7 Deep Learning and Cognitive Computing 7.1 Opening Vignette: Fighting Fraud with Deep Learning and Artificial Intelligence 7.2 Introduction to Deep Learning 7.3 Basics of “Shallow” Neural Networks Analytics in Action 7.1 Gaming Companies Use Data Analytics to Score Points with Players Technology Insights 7.1 Elements of an Artificial Neural Network 7.4 Process of Developing Neural Network–Based Systems Learning Process in ANN Backpropagation for ANN Training 7.5 Illuminating the Black Box of Ann Technology Insights 7.2 Explanability and Transparency in Machine Learning Models Analytics in Action 7.2 Sensitivity Analysis Reveals Injury Severity Factors in Traffic Accidents 7.6 Deep Neural Networks Feedforward Multilayer Perceptron (Mlp)-Type Deep Networks Impact of Random Weights in Deep MLP More Hidden Layers versus More Neurons Analytics in Action 7.3 Georgia DOT Variable Speed Limit Analytics Help Solve Traffic Congestions 7.7 Convolutional Neural Networks Convolution Function Pooling Image Processing Using Convolutional Networks Analytics in Action 7.4 From Image Recognition to Face Recognition Text Processing Using Convolutional Networks 7.8 Recurrent Networks and Long Short-Term Memory Networks LSTM Networks Applications ChatGPT What Is ChatGPT and How Does It Work Applications of ChatGPT Limitations of ChatGPT 7.9 Computer Frameworks for Implementation of Deep Learning Torch Caffe TensorFlow Theano Keras: An Application Programming Interface Analytics in Action 7.5 A Deep Learning Approach to Predicting Early Bounce-Backs to the Emergency Departments 7.10 Cognitive Computing How Does Cognitive Computing Work How Does Cognitive Computing Differ from AI Cognitive Search An Example of Cognitive Computing: IBM Watson Chapter Highlights Key Terms Questions for Discussion Exercises References PART IV Prescriptive Analytics Chapter 8 Prescriptive Analytics: Optimization and Simulation 8.1 Opening Vignette: Balancing Delivery Routes, Production Schedules, and Inventory 8.2 Model-Based Decision-Making Analytics in Action 8.1 Canadian Football League Optimizes Game Schedule Prescriptive Analytics Model Examples Identification of the Problem and Environmental Analysis Analytics in Action 8.2 Refinery Modeling 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) 8.5 Decision Modeling with Spreadsheets Analytics in Action 8.3 Pennsylvania Adoption Exchange Uses Spreadsheet Model to Better Match Children with Families Analytics in Action 8.4 Metro Meals on Wheels Treasure Valley Uses Excel to Find Optimal Delivery Routes 8.6 Mathematical Programming Optimization Analytics in Action 8.5 Mixed-Integer Programming Model Helps the University of Tennessee Medical Center with Scheduling Physicians Linear Programming Model Technology Insights 8.1 Linear Programming 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 Analytics in Action 8.6 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 Conventional Simulation Inadequacies Visual Interactive Simulation Visual Interactive Models and DSS Simulation Software 8.10 Genetic Algorithms and Developing GA Applications Terminology of Genetic Algorithms How Do Genetic Algorithms Work Genetic Algorithm Applications Chapter Highlights Key Terms Questions for Discussion Exercises References PART V Software and Trends Chapter 9 Landscape of Business Analytics Tools 9.1 Opening Vignette: How Seagate Is Using Knime to Tackle the Digital Transformation 9.2 Importance of Analytics Tools A Multidimensional Categorization of Analytics Tools Popularity of the Analytics Tools Analytics in Action 9.1 Predictive Analytic and Data Mining Help Stop Terrorist Funding 9.3 Free and Open-Source Analytics’ Programming Languages R How to Get Start With R Tutorial R for Analytics Application Tutorial—Predicting Employee Attrition Data Visualization Machine Learning for Predictive Modeling Explainable AI Rattle (for visual analytics programming in R) Python How to get started with Python Tutorial Python for Analytics, Application Tutorial—Predicting Movie Financial Success 9.4 Free and Open-Source Analytics’ Visual Tools KNIME Analytics in Action 9.2 Leveraging Predictive Analytics Prevents $1.3 Million Worth of Medical Supply Waste Tutorial Predicting Customer Churn Orange Tutorial Text Mining of Published Literature Step 1.Gathering and cleaning the textual data Step 2.Preprocessing the textual data Step 3.Performing the text analysis Step 4.Reporting of the obtained results Weka RapidMiner Tutorial Predicting Survival using the Titanic Data Set 9.5 Commercial Analytics Tools Alteryx IBM SAS JMP Tutorial Text Mining of Published Literature Step 1.Gathering and cleaning the textual data Step 2.Preprocessing the textual data Step 3.Performing the text analysis Step 4.Reporting of the obtained results A Comparison of JMP Pro and Orange Teradata Analytic Engines and Functions Analytics in Action 9.3 Analyzing Disease Patterns from an Electronic Medical Records Data Warehouse TIBCO Other Analytics Tools Chapter Highlights Key Terms Questions for Discussion Exercises References Chapter 10 AI-Based Trends in Analytics and Data Science 10.1 Application Vignette: Discover Foods Employs IoT and Machine Learning to Ensure Food Quality 10.2 Cloud-Based Analytics Data as a Service (DaaS) Desktop as a Service (DaaS) Software as a Service (SaaS) Platform as a Service (PaaS) Infrastructure as a Service (IaaS) Essential Technologies for Cloud Computing Analytics in Action 10.1 Major West Coast Utility Uses Cloud-Mobile Technology to Provide Real-Time Incident Reporting Cloud Deployment Models Major Cloud Platform Providers in App Development and Deployment Analytics as a Service (AaaS) Representative Analytics as a Service Offerings Analytics in Action 10.2 GO-JEK Employs Google Cloud Platform for Forecasting and Pricing Illustrative Analytics Applications Employing the Cloud Infrastructure Using Azure IoT (Internet of Things), Stream Analytics, and Machine Learning to Improve Mobile Healthcare Services Chime Enhances Customer Experience Using Snowflake 10.3 Location-Based Analytics Geospatial Analytics Analytics in Action 10.3 Improving Home Loan Appraisal Process Using BI and Geographic Information Systems Analytics in Action 10.4 Starbucks Exploits GIS and Analytics to Grow Worldwide A Multimedia Exercise in Analytics Employing Geospatial Analytics Real-Time Location Intelligence Analytics Applications for Consumers 10.4 Image Analytics/Alternative Data Analytics in Action 10.5 Image Analysis Helps Estimate Plant Cover Analytics in Action 10.6 How Unilever Used Image Analytics in Its Supply Chain to Examine Deforestation 10.5 IoT Essentials Definitions and Characteristics The IoT Ecosystem Structure of IoT Systems Major Benefits and Drivers of IoT Major Benefits of IoT Major Drivers of IoT How IoT Works IoT and Decision Support Sensors and Their Role in IoT Brief Introduction to Sensor Technology How Sensors Work with IoT Analytics in Action 10.7 Rockwell Automation Monitors Expensive Oil and Gas Exploration Assets to Predict Failures Sensor Applications and Radio-Frequency Identification (RFID) Sensors Technology Insights 10.1 RFID Sensors Use of RFID and Smart Sensors in IoT 10.6 IoT Applications Smart Homes and Appliances Typical Components of Smart Homes Smart Appliances A Smart Home Is Where the Bot Is Barriers to Smart Home Adoption Smart Components in Smart Cities and Smart Factories Improving Transportation in a Smart City Autonomous (Self-Driving) Vehicles Implementation Issues in Autonomous Vehicles The Future of the IoT 10.7 5G Technologies and Impact on AI 10.8 Other Emerging AI Topics: Robotic Process Automation (RPA) Analytics in Action 10.8 Monitoring Quarantined Persons in the State District Analytics in Action 10.9 Walgreens’ HR Shared Service Efficiency Increased by 73% by Blue Prism’s Digital Workforce 10.9 Bioinformatics and Health Network Science Analytics in Action 10.10 Analyzing the Genomics Data to Optimize Animals’ Gut Health and Performance Network Analytics Analytics in Action 10.11 Network Analytics for Predictive Modeling: Predicting Hospital Length of Stay using Comorbidity Networks 10.10 Other Recent Developments Web 3.0 Metaverse and Digital Twins GPT-3/ChatGPT LaMDA Blenderbot Chapter Highlights Key Terms Exercises References Chapter 11 Ethical, Privacy, and Managerial Considerations in Analytics 11.1 Opening Vignette: Lessons Learned from Analytics Journey in an Organization 11.2 Implementing Intelligent Systems: An Overview The Intelligent Systems Implementation Process 11.3 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 11.4 Implementing IoT and Managerial Considerations Major Implementation Issues Strategy for Turning Industrial IoT into Competitive Advantage 11.5 Legal, Privacy, and Ethical Issues Legal Issues A Sample of AI Potential Legal Issues Privacy Issues Who Owns Our Private Data Ethics Issues Ethical Issues of Intelligent Systems Other Topics in Intelligent Systems Ethics 11.6 Ethical/Responsible/Trustworthy AI The Curious Case Of BlenderBot 3.0 The O’Neil Claim of Potential Analytics’ Dangers 11.7 Impacts of Intelligent Systems on Organizations New Organizational Units and Their Management Transforming Businesses and Increasing Competitive Advantage Analytics in Action 11.1 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 11.8 Impacts on Jobs and Work An Overview Are Intelligent Systems Going to Take Jobs—My Job AI Puts Many Jobs at Risk Analytics in Action 11.2 Administrative/Office Jobs That Robots Have Already Taken Which Jobs Are Most in Danger? Which Ones Are Safe Some More Job Losses Observations Intelligent Systems May Actually Add Jobs Jobs and the Nature of Work Will Change Conclusion: Let Us Be Optimistic 11.9 Potential Dangers of AI Position of AI Dystopia The AI Utopia’s Position 11.10 Citizen Science and Citizen Data Scientists Citizen Science Citizen Data Scientist Concluding Remarks Chapter Highlights Key Terms Questions for Discussion Exercises References Glossary Index