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• Title Page
• Copyright Page
• Brief Contents
• Contents
• Preface
• Acknowledgments
• About the Authors
• Chapter 1: An Overview of Business Intelligence, Analytics, and Data Science
• 1.1. Opening Vignette: Sports Analytics—An Exciting Frontier for Learning and Understanding Applic
• 1.2. Changing Business Environments and Evolving Needs for Decision Support and Analytics
• 1.3. Evolution of Computerized Decision Support to Analytics/Data Science
• 1.4. A Framework for Business Intelligence
• Definitions of BI
• A Brief History of BI
• The Architecture of BI
• The Origins and Drivers of BI
• Application Case 1.1: Sabre Helps Its Clients Through Dashboards and Analytics
• 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
• 1.5. Analytics Overview
• Descriptive Analytics
• Application Case 1.2: Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capab
• Application Case 1.3: Siemens Reduces Cost with the Use of Data Visualization
• Predictive Analytics
• Application Case 1.4: Analyzing Athletic Injuries
• Prescriptive Analytics
• Analytics Applied to Different Domains
• Application Case 1.5: A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise
• Analytics or Data Science?
• 1.6. Analytics Examples in Selected Domains
• Analytics Applications in Healthcare—Humana Examples
• Analytics in the Retail Value Chain
• 1.7. A Brief Introduction to Big Data Analytics
• What Is Big Data?
• Application Case 1.6: CenterPoint Energy Uses Real-Time Big Data Analytics to Improve Customer Servi
• 1.8. An Overview of the Analytics Ecosystem
• Data Generation Infrastructure Providers
• Data Management Infrastructure Providers
• Data Warehouse Providers
• Middleware Providers
• Data Service Providers
• Analytics-Focused Software Developers
• Application Developers: Industry Specific or General
• Analytics Industry Analysts and Influencers
• Academic Institutions and Certification Agencies
• Regulators and Policy Makers
• Analytics User Organizations
• 1.9. Plan of the Book
• 1.10. 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: Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization
• 2.1. Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-D
• 2.2. The Nature of Data
• 2.3. A Simple Taxonomy of Data
• Application Case 2.1: Medical Device Company Ensures Product Quality While Saving Money
• 2.4. The Art and Science of Data Preprocessing
• Application Case 2.2: Improving Student Retention with Data-Driven Analytics
• 2.5. 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 (May Also Be Called Measures of Spread Decentrality)
• Range
• Variance
• Standard Deviation
• Mean Absolute Deviation
• Quartiles and Interquartile Range
• Box-and-Whiskers Plot
• The Shape of a Distribution
• Application Case 2.3: Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and D
• 2.6 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
• Application Case 2.4: Predicting NCAA Bowl Game Outcomes
• Time Series Forecasting
• 2.7. Business Reporting
• Application Case 2.5: Flood of Paper Ends at FEMA
• 2.8. Data Visualization
• A Brief History of Data Visualization
• Application Case 2.6: Macfarlan Smith Improves Operational Performance Insight with Tableau Online
• 2.9. Different Types of Charts and Graphs
• Basic Charts and Graphs
• Specialized Charts and Graphs
• Which Chart or Graph Should You Use?
• 2.10. The Emergence of Visual Analytics
• Visual Analytics
• High-Powered Visual Analytics Environments
• 2.11. Information Dashboards
• Application Case 2.7: Dallas Cowboys Score Big with Tableau and Teknion
• Dashboard Design
• Application Case 2.8: 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
• Chapter 3: Descriptive Analytics II: Business Intelligence and Data Warehousing
• 3.1. Opening Vignette: Targeting Tax Fraud with Business Intelligence and Data Warehousing
• 3.2. Business Intelligence and Data Warehousing
• What Is a Data Warehouse?
• A Historical Perspective to Data Warehousing
• Characteristics of Data Warehousing
• Data Marts
• Operational Data Stores
• Enterprise Data Warehouses (EDW)
• Metadata
• Application Case 3.1: A Better Data Plan: Well- Established TELCOs Leverage Data Warehousing and Ana
• 3.3. Data Warehousing Process
• 3.4. Data Warehousing Architectures
• Alternative Data Warehousing Architectures
• Which Architecture Is the Best?
• 3.5. Data Integration and the Extraction, Transformation, and Load (ETL) Processes
• Data Integration
• Application Case 3.2: BP Lubricants Achieves BIGS Success
• Extraction, Transformation, and Load
• 3.6. Data Warehouse Development
• Application Case 3.3: Use of Teradata Analytics for SAP Solutions Accelerates Big Data Delivery
• Data Warehouse Development Approaches
• Additional Data Warehouse Development Considerations
• Representation of Data in Data Warehouse
• Analysis of Data in Data Warehouse
• OLAP versus OLTP
• OLAP Operations
• 3.7. Data Warehousing Implementation Issues
• Massive Data Warehouses and Scalability
• Application Case 3.4: EDW Helps Connect State Agencies in Michigan
• 3.8. Data Warehouse Administration, Security Issues, and Future Trends
• The Future of Data Warehousing
• 3.9. Business Performance Management
• Closed-Loop BPM Cycle
• Application Case 3.5: AARP Transforms Its BI Infrastructure and Achieves a 347% ROI in Three Years
• 3.10. Performance Measurement
• Key Performance Indicator (KPI)
• Performance Measurement System
• 3.11. Balanced Scorecards
• The Four Perspectives
• The Meaning of Balance in BSC
• 3.12. Six Sigma as a Performance Measurement System
• The DMAIC Performance Model
• Balanced Scorecard versus Six Sigma
• Effective Performance Measurement
• Application Case 3.6: Expedia.com’s Customer Satisfaction Scorecard
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• Chapter 4: Predictive Analytics I: Data Mining Process, Methods, and Algorithms
• 4.1. Opening Vignette: Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fig
• 4.2. Data Mining Concepts and Applications
• 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: Dell Is Staying Agile and Effective with Analytics in the 21st Century
• Data Mining versus Statistics
• 4.3. Data Mining Applications
• Application Case 4.3: Bank Speeds Time to Market with Advanced Analytics
• 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
• Application Case 4.5: Influence Health Uses Advanced Predictive Analytics to Focus on the Factors Th
• Cluster Analysis for Data Mining
• 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: Predictive Analytics II: Text, Web, and Social Media Analytics
• 5.1. Opening Vignette: Machine versus Men on Jeopardy!: The Story of Watson
• 5.2. Text Analytics and Text Mining Overview
• Application Case 5.1: Insurance Group Strengthens Risk Management with Text Mining Solution
• 5.3. Natural Language Processing (NLP)
• Application Case 5.2: AMC Networks Is Using Analytics to Capture New Viewers, Predict Ratings, and A
• 5.4. Text Mining Applications
• Marketing Applications
• Security Applications
• Application Case 5.3: Mining for Lies
• Biomedical Applications
• Academic Applications
• Application Case 5.4: Bringing the Customer into the Quality Equation: Lenovo Uses Analytics to Reth
• 5.5. Text Mining Process
• Task 1: Establish the Corpus
• Task 2: Create the Term–Document Matrix
• Task 3: Extract the Knowledge
• Application Case 5.5: Research Literature Survey with Text Mining
• 5.6. Sentiment Analysis
• Application Case 5.6: Creating a Unique Digital Experience to Capture the Moments That Matter at Wim
• 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
• 5.7. Web Mining Overview
• Web Content and Web Structure Mining
• 5.8. Search Engines
• Anatomy of a Search Engine
• 1. Development Cycle
• 2. Response Cycle
• Search Engine Optimization
• Methods for Search Engine Optimization
• Application Case 5.7: Understanding Why Customers Abandon Shopping Carts Results in a $10 Million Sa
• 5.9. Web Usage Mining (Web Analytics)
• Web Analytics Technologies
• Web Analytics Metrics
• Web Site Usability
• Traffic Sources
• Visitor Profiles
• Conversion Statistics
• 5.10. Social Analytics
• Social Network Analysis
• Social Network Analysis Metrics
• Application Case 5.8: 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
• Chapter 6: Prescriptive Analytics: Optimization and Simulation
• 6.1. Opening Vignette: School District of Philadelphia Uses Prescriptive Analytics to Find Optimal S
• 6.2. Model-Based Decision Making
• Prescriptive Analytics Model Examples
• Application Case 6.1: Optimal Transport for ExxonMobil Downstream through a DSS
• Identification of the Problem and Environmental Analysis
• Model Categories
• Application Case 6.2: Ingram Micro Uses Business Intelligence Applications to Make Pricing Decisions
• 6.3. Structure of Mathematical Models for Decision Support
• The Components of Decision Support Mathematical Models
• The Structure of Mathematical Models
• 6.4. Certainty, Uncertainty, and Risk
• Decision Making under Certainty
• Decision Making under Uncertainty
• Decision Making under Risk (Risk Analysis)
• 6.5. Decision Modeling with Spreadsheets
• Application Case 6.3: Primary Schools in Slovenia Use Interactive and Automated Scheduling Systems t
• Application Case 6.4: Spreadsheet Helps Optimize Production Planning in Chilean Swine Companies
• Application Case 6.5: Metro Meals on Wheels Treasure Valley Uses Excel to Find Optimal Delivery Rout
• 6.6 Mathematical Programming Optimization
• Application Case 6.6: Mixed-Integer Programming Model Helps the University of Tennessee Medical Cent
• Linear Programming Model
• Modeling in LP: An Example
• Implementation
• 6.7. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking
• Multiple Goals
• Sensitivity Analysis
• What-If Analysis
• Goal Seeking
• 6.8. Decision Analysis with Decision Tables and Decision Trees
• Decision Tables
• Decision Trees
• 6.9. Introduction to Simulation
• Major Characteristics of Simulation
• Application Case 6.7: Syngenta Uses Monte Carlo Simulation Models to Increase Soybean Crop Productio
• Advantages of Simulation
• Disadvantages of Simulation
• The Methodology of Simulation
• Simulation Types
• Monte Carlo Simulation
• Discrete Event Simulation
• Application Case 6.8: Cosan Improves Its Renewable Energy Supply Chain Using Simulation
• 6.10. Visual Interactive Simulation
• Conventional Simulation Inadequacies
• Visual Interactive Simulation
• Visual Interactive Models and DSS
• Simulation Software
• Application Case 6.9: Improving Job-Shop Scheduling Decisions through RFID: A Simulation-Based Asses
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• Chapter 7: Big Data Concepts and Tools
• 7.1. Opening Vignette: Analyzing Customer Churn in a Telecom Company Using Big Data Methods
• 7.2. Definition of Big Data
• The “V”s That Define Big Data
• Application Case 7.1: Alternative Data for Market Analysis or Forecasts
• 7.3. Fundamentals of Big Data Analytics
• Business Problems Addressed by Big Data Analytics
• Application Case 7.2: Top Five Investment Bank Achieves Single Source of the Truth
• 7.4. Big Data Technologies
• MapReduce
• Why Use MapReduce?
• Hadoop
• How Does Hadoop Work?
• Hadoop Technical Components
• Hadoop: The Pros and Cons
• NoSQL
• Application Case 7.3: eBay’s Big Data Solution
• Application Case 7.4: Understanding Quality and Reliability of Healthcare Support Information on Twi
• 7.5. 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
• 7.6. Big Data Vendors and Platforms
• IBM InfoSphere BigInsights
• Application Case 7.5: Using Social Media for Nowcasting the Flu Activity
• Teradata Aster
• Application Case 7.6: Analyzing Disease Patterns from an Electronic Medical Records Data Warehouse
• 7.7. Big Data and Stream Analytics
• Stream Analytics versus Perpetual Analytics
• Critical Event Processing
• Data Stream Mining
• 7.8. Applications of Stream Analytics
• e-Commerce
• Telecommunications
• Application Case 7.7: Salesforce Is Using Streaming Data to Enhance Customer Value
• Law Enforcement and Cybersecurity
• Power Industry
• Financial Services
• Health Sciences
• Government
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• Chapter 8: Future Trends, Privacy and Managerial Considerations in Analytics
• 8.1. Opening Vignette: Analysis of Sensor Data Helps Siemens Avoid Train Failures
• 8.2. Internet of Things
• Application Case 8.1: SilverHook Powerboats Uses Real-Time Data Analysis to Inform Racers and Fans
• Application Case 8.2: Rockwell Automation Monitors Expensive Oil and Gas Exploration Assets
• IoT Technology Infrastructure
• RFID Sensors
• Fog Computing
• IoT Platforms
• Application Case 8.3: Pitney Bowes Collaborates with General Electric IoT Platform to Optimize Produ
• IoT Start-Up Ecosystem
• Managerial Considerations in the Internet of Things
• 8.3. 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
• 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
• MD Anderson Cancer Center Utilizes Cognitive Computing Capabilities of IBM Watson to Give Better Tre
• Public School Education in Tacoma, Washington, Uses Microsoft Azure Machine Learning to Predict Scho
• Dartmouth-Hitchcock Medical Center Provides Personalized Proactive Healthcare Using Microsoft Cortan
• Mankind Pharma Uses IBM Cloud Infrastructure to Reduce Application Implementation Time by 98%
• Gulf Air Uses Big Data to Get Deeper Customer Insight
• Chime Enhances Customer Experience Using Snowflake
• 8.4. Location-Based Analytics for Organizations
• Geospatial Analytics
• Application Case 8.4: Indian Police Departments Use Geospatial Analytics to Fight Crime
• Application Case 8.5: Starbucks Exploits GIS and Analytics to Grow Worldwide
• Real-Time Location Intelligence
• Application Case 8.6: Quiznos Targets Customers for Its Sandwiches
• Analytics Applications for Consumers
• 8.5. Issues of Legality, Privacy, and Ethics
• Legal Issues
• Privacy
• Collecting Information about Individuals
• Mobile User Privacy
• Homeland Security and Individual Privacy
• Recent Technology Issues in Privacy and Analytics
• Who Owns Our Private Data?
• Ethics in Decision Making and Support
• 8.6. Impacts of Analytics in Organizations: An Overview
• New Organizational Units
• Redesign of an Organization through the Use of Analytics
• Analytics Impact on Managers’ Activities, Performance, and Job Satisfaction
• Industrial Restructuring
• Automation’s Impact on Jobs
• Unintended Effects of Analytics
• 8.7. Data Scientist as a Profession
• Where Do Data Scientists Come From?
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• References
• Glossary
• Index
• Back Cover