Efnisyfirlit

• 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

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