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• Title Page
• Contents
• Preface
• About the Authors
• Part I Decision Making and Analytics: An Overview
• Chapter 1 An Overview of Business Intelligence, Analytics, and Decision Support
• 1.1 Opening Vignette: Magpie Sensing Employs Analytics to Manage a Vaccine Supply Chain Effectively
• 1.2 Changing Business Environments and Computerized Decision Support
• The Business Pressures–Responses–Support Model
• 1.3 Managerial Decision Making
• The Nature of Managers’ Work
• The Decision-Making Process
• 1.4 Information Systems Support for Decision Making
• 1.5 An Early Framework for Computerized Decision Support
• The Gorry and Scott-Morton Classical Framework
• Computer Support for Structured Decisions
• Computer Support for Unstructured Decisions
• Computer Support for Semistructured Problems
• 1.6 The Concept of Decision Support Systems (DSS)
• DSS as an Umbrella Term
• Evolution of DSS into Business Intelligence
• 1.7 A Framework for Business Intelligence (BI)
• Definitions of BI
• A Brief History of BI
• The Architecture of BI
• Styles of BI
• The Origins and Drivers of BI
• A Multimedia Exercise in Business Intelligence
• Application Case 1.1 Sabre Helps Its Clients Through Dashboards and Analytics
• The DSS–BI Connection
• 1.8 Business Analytics Overview
• Descriptive Analytics
• Application Case 1.2 Eliminating Inefficiencies at Seattle Children’s Hospital
• Application Case 1.3 Analysis at the Speed of Thought
• Predictive Analytics
• Application Case 1.4 Moneyball: Analytics in Sports and Movies
• Application Case 1.5 Analyzing Athletic Injuries
• Prescriptive Analytics
• Application Case 1.6 Industrial and Commercial Bank of China (ICBC) Employs Models to Reconfigure It
• Analytics Applied to Different Domains
• Analytics or Data Science?
• 1.9 Brief Introduction to Big Data Analytics
• What Is Big Data?
• Application Case 1.7 Gilt Groupe’s Flash Sales Streamlined by Big Data Analytics
• 1.10 Plan of the Book
• Part I: Business Analytics: An Overview
• Part II: Descriptive Analytics
• Part III: Predictive Analytics
• Part IV: Prescriptive Analytics
• Part V: Big Data and Future Directions for Business Analytics
• 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
• End-of-Chapter Application Case Nationwide Insurance Used BI to Enhance Customer Service
• References
• Chapter 2 Foundations and Technologies for Decision Making
• 2.1 Opening Vignette: Decision Modeling at HP Using Spreadsheets
• 2.2 Decision Making: Introduction and Definitions
• Characteristics of Decision Making
• A Working Definition of Decision Making
• Decision-Making Disciplines
• Decision Style and Decision Makers
• 2.3 Phases of the Decision-Making Process
• 2.4 Decision Making: The Intelligence Phase
• Problem (or Opportunity) Identification
• Application Case 2.1 Making Elevators Go Faster!
• Problem Classification
• Problem Decomposition
• Problem Ownership
• 2.5 Decision Making: The Design Phase
• Models
• Mathematical (Quantitative) Models
• The Benefits of Models
• Selection of a Principle of Choice
• Normative Models
• Suboptimization
• Descriptive Models
• Good Enough, or Satisficing
• Developing (Generating) Alternatives
• Measuring Outcomes
• Risk
• Scenarios
• Possible Scenarios
• Errors in Decision Making
• 2.6 Decision Making: The Choice Phase
• 2.7 Decision Making: The Implementation Phase
• 2.8 How Decisions Are Supported
• Support for the Intelligence Phase
• Support for the Design Phase
• Support for the Choice Phase
• Support for the Implementation Phase
• 2.9 Decision Support Systems: Capabilities
• A DSS Application
• 2.10 DSS Classifications
• The AIS SIGDSS Classification for DSS
• Other DSS Categories
• Custom-Made Systems Versus Ready-Made Systems
• 2.11 Components of Decision Support Systems
• The Data Management Subsystem
• The Model Management Subsystem
• Application Case 2.2 Station Casinos Wins by Building Customer Relationships Using Its Data
• Application Case 2.3 SNAP DSS Helps OneNet MakeTelecommunications Rate Decisions
• The User Interface Subsystem
• The Knowledge-Based Management Subsystem
• Application Case 2.4 From a Game Winner to a Doctor!
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• End-of-Chapter Application Case Logistics Optimization in a Major Shipping Company (CSAV)
• References
• Part II Descriptive Analytics
• Chapter 3 Data Warehousing
• 3.1 Opening Vignette: Isle of Capri Casinos Is Winning with Enterprise Data Warehouse
• 3.2 Data Warehousing Definitions and Concepts
• 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 Analy
• 3.3 Data Warehousing Process Overview
• Application Case 3.2 Data Warehousing Helps MultiCare Save More Lives
• 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 3.3 BP Lubricants Achieves BIGS Success
• Extraction, Transformation, and Load
• 3.6 Data Warehouse Development
• Application Case 3.4 Things Go Better with Coke’s Data Warehouse
• Data Warehouse Development Approaches
• Application Case 3.5 Starwood Hotels & Resorts Manages Hotel Profitability with Data Warehousing
• Additional Data Warehouse Development Considerations
• Representation of Data in Data Warehouse
• Analysis of Data in the Data Warehouse
• OLAP Versus OLTP
• OLAP Operations
• 3.7 Data Warehousing Implementation Issues
• Application Case 3.6 EDW Helps Connect State Agencies in Michigan
• Massive Data Warehouses and Scalability
• 3.8 Real-Time Data Warehousing
• Application Case 3.7 Egg Plc Fries the Competition in Near Real Time
• 3.9 Data Warehouse Administration, Security Issues, and Future Trends
• The Future of Data Warehousing
• 3.10 Resources, Links, and the Teradata University Network Connection
• Resources and Links
• Cases
• Vendors, Products, and Demos
• Periodicals
• Additional References
• The Teradata University Network (TUN) Connection
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• End-of-Chapter Application Case Continental Airlines Flies High with Its Real-Time Data Warehouse
• References
• Chapter 4 Business Reporting, Visual Analytics, and Business Performance Management
• 4.1 Opening Vignette:Self-Service Reporting Environment Saves Millions for Corporate Customers
• 4.2 Business Reporting Definitions and Concepts
• What Is a Business Report?
• Application Case 4.1 Delta Lloyd Group Ensures Accuracy and Efficiency in Financial Reporting
• Components of the Business Reporting System
• Application Case 4.2 Flood of Paper Ends at FEMA
• 4.3 Data and Information Visualization
• Application Case 4.3 Tableau Saves Blastrac Thousands of Dollars with Simplified Information Sharing
• A Brief History of Data Visualization
• Application Case 4.4 TIBCO Spotfire Provides Dana-Farber Cancer Institute with Unprecedented Insight
• 4.4 Different Types of Charts and Graphs
• Basic Charts and Graphs
• Specialized Charts and Graphs
• 4.5 The Emergence of Data Visualization and Visual Analytics
• Visual Analytics
• High-Powered Visual Analytics Environments
• 4.6 Performance Dashboards
• Application Case 4.5 Dallas Cowboys Score Big with Tableau and Teknion
• Dashboard Design
• Application Case 4.6 Saudi Telecom Company Excels with Information Visualization
• 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 Dashboard with Business Users’ Comments
• Present Information in Three Different Levels
• Pick the Right Visual Construct Using Dashboard Design Principles
• Provide for Guided Analytics
• 4.7 Business Performance Management
• Closed-Loop BPM Cycle
• Application Case 4.7 IBM Cognos Express Helps Mace for Faster
• 4.8 Performance Measurement
• Key Performance Indicator (KPI)
• Performance Measurement System
• 4.9 Balanced Scorecards
• The Four Perspectives
• The Meaning of Balance in BSC
• Dashboards Versus Scorecards
• 4.10 Six Sigma as a Performance Measurement System
• The DMAIC Performance Model
• Balanced Scorecard Versus Six Sigma
• Effective Performance Measurement
• Application Case 4.8 Expedia.com’s Customer Satisfaction Scorecard
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• End-of-Chapter Application Case Smart Business Reporting Helps Healthcare Providers Deliver Better C
• References
• Part III Predictive Analytics
• Chapter 5 Data Mining
• 5.1 Opening Vignette: Cabela’s Reels in More Customers withAdvanced Analytics and Data Mining
• 5.2 Data Mining Concepts and Applications
• Application Case 5.1 Smarter Insurance: Infinity P&C ImprovesCustomer Service and Combats Fraud with
• Definitions, Characteristics, and Benefits
• Application Case 5.2 Harnessing Analytics to Combat Crime:Predictive Analytics Helps Memphis Police
• How Data Mining Works
• Data Mining Versus Statistics
• 5.3 Data Mining Applications
• Application Case 5.3 A Mine on Terrorist Funding
• 5.4 Data Mining Process
• Step 1: Business Understanding
• Step 2: Data Understanding
• Step 3: Data Preparation
• Step 4: Model Building
• Application Case 5.4 Data Mining 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
• Application Case 5.5 2degrees Gets a 1275 Percent Boost in ChurnIdentification
• Association Rule Mining
• 5.6 Data Mining Software Tools
• Application Case 5.6 Data Mining Goes to Hollywood: PredictingFinancial Success of Movies
• 5.7 Data Mining Privacy Issues, Myths, and Blunders
• Data Mining and Privacy Issues
• Application Case 5.7 Predicting Customer Buying Patterns—TheTarget Story
• Data Mining Myths and Blunders
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• End-of-Chapter Application Case Macys.com Enhances ItsCustomers’ Shopping Experience with Analytic
• References
• Chapter 6 Techniques for Predictive Modeling
• 6.1 Opening Vignette: Predictive Modeling Helps BetterUnderstand and Manage Complex MedicalProcedure
• 6.2 Basic Concepts of Neural Networks
• Biological and Artificial Neural Networks
• Application Case 6.1 Neural Networks Are Helping to Save Lives inthe Mining Industry
• Elements of ANN
• Network Information Processing
• Neural Network Architectures
• Application Case 6.2 Predictive Modeling Is Powering the PowerGenerators
• 6.3 Developing Neural Network–Based Systems
• The General ANN Learning Process
• Backpropagation
• 6.4 Illuminating the Black Box of ANN with SensitivityAnalysis
• Application Case 6.3 Sensitivity Analysis Reveals Injury SeverityFactors in Traffic Accidents
• 6.5 Support Vector Machines
• Application Case 6.4 Managing Student Retention with PredictiveModeling
• Mathematical Formulation of SVMs
• Primal Form
• Dual Form
• Soft Margin
• Nonlinear Classification
• Kernel Trick
• 6.6 A Process-Based Approach to the Use of SVM
• Support Vector Machines Versus Artificial Neural Networks
• 6.7 Nearest Neighbor Method for Prediction
• Similarity Measure: The Distance Metric
• Parameter Selection
• Application Case 6.5 Efficient Image Recognition andCategorization with kNN
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• End-of-Chapter Application Case Coors Improves Beer Flavorswith Neural Networks
• References
• Chapter 7 Text Analytics, Text Mining, and Sentiment Analysis
• 7.1 Opening Vignette: Machine Versus Men on Jeopardy!: TheStory of Watson
• 7.2 Text Analytics and Text Mining Concepts andDefinitions
• Application Case 7.1 Text Mining for Patent Analysis
• 7.3 Natural Language Processing
• Application Case 7.2 Text Mining Improves Hong KongGovernment’s Ability to Anticipate and Address
• 7.4 Text Mining Applications
• Marketing Applications
• Security Applications
• Application Case 7.3 Mining for Lies
• Biomedical Applications
• Academic Applications
• Application Case 7.4 Text Mining and Sentiment Analysis HelpImprove Customer Service Performance
• 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 TextMining
• 7.6 Text Mining Tools
• Commercial Software Tools
• Free Software Tools
• Application Case 7.6 A Potpourri of Text Mining Case Synopses
• 7.7 Sentiment Analysis Overview
• Application Case 7.7 Whirlpool Achieves Customer Loyalty andProduct Success with Text Analytics
• 7.8 Sentiment Analysis Applications
• 7.9 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 Document
• 7.10 Sentiment Analysis and Speech Analytics 359How Is It Done?
• Application Case 7.8 Cutting Through the Confusion: Blue CrossBlue Shield of North Carolina Uses Nex
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• End-of-Chapter Application Case BBVA Seamlessly Monitorsand Improves Its Online Reputation
• References
• Chapter 8 Web Analytics, Web Mining, and Social Analytics
• 8.1 Opening Vignette: Security First Insurance Deepens Connection with Policyholders
• 8.2 Web Mining Overview
• 8.3 Web Content and Web Structure Mining
• Application Case 8.1 Identifying Extremist Groups with Web Linkand Content Analysis
• 8.4 Search Engines
• Anatomy of a Search Engine
• 1. Development Cycle
• Web Crawler
• Document Indexer
• 2. Response Cycle
• Query Analyzer
• Document Matcher/Ranker
• How Does Google Do It?
• Application Case 8.2 IGN Increases Search Traffic by 1500 Percent
• 8.5 Search Engine Optimization
• Methods for Search Engine Optimization
• Application Case 8.3 Understanding Why Customers Abandon Shopping Carts Results in $10 Million Sales
• 8.6 Web Usage Mining (Web Analytics)
• Web Analytics Technologies
• Application Case 8.4 Allegro Boosts Online Click-Through Rates by 500 Percent with Web Analysis
• Web Analytics Metrics
• Web Site Usability
• Traffic Sources
• Visitor Profiles
• Conversion Statistics
• 8.7 Web Analytics Maturity Model and Web Analytics Tools
• Web Analytics Tools
• Putting It All Together—A Web Site Optimization Ecosystem
• A Framework for Voice of the Customer Strategy
• 8.8 Social Analytics and Social Network Analysis
• Social Network Analysis
• Social Network Analysis Metrics
• Application Case 8.5 Social Network Analysis HelpsTelecommunication Firms
• Connections
• Distributions
• Segmentation
• 8.9 Social Media Definitions and Concepts
• How Do People Use Social Media?
• Application Case 8.6 Measuring the Impact of Social Media at Lollapalooza
• 8.10 Social Media Analytics
• Measuring the Social Media Impact
• Best Practices in Social Media Analytics
• Application Case 8.7 eHarmony Uses Social Media to Help Take the Mystery Out of Online Dating
• Social Media Analytics Tools and Vendors
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• End-of-Chapter Application Case Keeping Students on Track with Web and Predictive Analytics
• References
• Part IV Prescriptive Analytics
• Chapter 9 Model-Based Decision Making: Optimization and Multi-Criteria Systems
• 9.1 Opening Vignette: Midwest ISO Saves Billions by Better Planning of Power Plant Operations and Ca
• 9.2 Decision Support Systems Modeling
• Application Case 9.1 Optimal Transport for ExxonMobil Downstream Through a DSS
• Current Modeling Issues
• Application Case 9.2 Forecasting/Predictive Analytics Proves to Bea Good Gamble for Harrah’s Chero
• 9.3 Structure of Mathematical Models for Decision Support
• The Components of Decision Support Mathematical Models
• The Structure of Mathematical Models
• 9.4 Certainty, Uncertainty, and Risk
• Decision Making Under Certainty
• Decision Making Under Uncertainty
• Decision Making Under Risk (Risk Analysis)
• Application Case 9.3 American Airlines UsesShould-Cost Modeling to Assess the Uncertainty of Bidsfor
• 9.5 Decision Modeling with Spreadsheets
• Application Case 9.4 Showcase Scheduling at Fred Astaire East Side Dance Studio
• 9.6 Mathematical Programming Optimization
• Application Case 9.5 Spreadsheet Model Helps Assign Medical Residents
• Mathematical Programming
• Linear Programming
• Modeling in LP: An Example
• Implementation
• 9.7 Multiple Goals, Sensitivity Analysis, What-If Analysis,and Goal Seeking
• Multiple Goals
• Sensitivity Analysis
• What-If Analysis
• Goal Seeking
• 9.8 Decision Analysis with Decision Tables and Decision Trees
• Decision Tables
• Decision Trees
• 9.9 Multi-Criteria Decision Making With Pairwise Comparisons
• The Analytic Hierarchy Process
• Application Case 9.6 U.S. HUD Saves the House by Using AHP for Selecting IT Projects
• Tutorial on Applying Analytic Hierarchy Process Using Web-HIPRE
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• End-of-Chapter Application Case Pre-Positioning of Emergency Items for CARE International
• References
• Chapter 10 Modeling and Analysis: Heuristic Search Methods and Simulation
• 10.1 Opening Vignette: System Dynamics Allows FluorCorporation to Better Plan for Project and Change
• 10.2 Problem-Solving Search Methods
• Analytical Techniques
• Algorithms
• Blind Searching
• Heuristic Searching
• Application Case 10.1 Chilean Government Uses Heuristics to Make Decisions on School Lunch Providers
• 10.3 Genetic Algorithms and Developing GA Applications
• Example: The Vector Game
• Terminology of Genetic Algorithms
• How Do Genetic Algorithms Work?
• Limitations of Genetic Algorithms
• Genetic Algorithm Applications
• 10.4 Simulation
• Application Case 10.2 Improving Maintenance Decision Making in the Finnish Air Force Through Simulat
• Application Case 10.3 Simulating Effects of Hepatitis B Interventions
• Major Characteristics of Simulation
• Advantages of Simulation
• Disadvantages of Simulation
• The Methodology of Simulation
• Simulation Types
• Monte Carlo Simulation
• Discrete Event Simulation
• 10.5 Visual Interactive Simulation
• Conventional Simulation Inadequacies
• Visual Interactive Simulation
• Visual Interactive Models and DSS
• Application Case 10.4 Improving Job-Shop Scheduling DecisionsThrough RFID: A Simulation-Based Assess
• Simulation Software
• 10.6 System Dynamics Modeling
• 10.7 Agent-Based Modeling
• Application Case 10.5 Agent-Based Simulation Helps Analyze Spread of a Pandemic Outbreak
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• End-of-Chapter Application Case HP Applies Management Science Modeling to Optimize Its Supply Chain
• References
• Chapter 11 Automated Decision Systems and Expert Systems
• 11.1 Opening Vignette: InterContinental Hotel Group Uses Decision Rules for Optimal Hotel Room Rates
• 11.2 Automated Decision Systems
• Application Case 11.1 Giant Food Stores Prices the EntireStore
• 11.3 The Artificial Intelligence Field
• 11.4 Basic Concepts of Expert Systems
• Experts
• Expertise
• Features of ES
• Application Case 11.2 Expert System Helps in Identifying SportTalents
• 11.5 Applications of Expert Systems
• Application Case 11.3 Expert System Aids in Identification of Chemical, Biological, and Radiological
• Classical Applications of ES
• Newer Applications of ES
• Areas for ES Applications
• 11.6 Structure of Expert Systems
• Knowledge Acquisition Subsystem
• Knowledge Base
• Inference Engine
• User Interface
• Blackboard (Workplace)
• Explanation Subsystem (Justifier)
• Knowledge-Refining System
• Application Case 11.4 Diagnosing Heart Diseases by Signal Processing
• 11.7 Knowledge Engineering
• Knowledge Acquisition
• Knowledge Verification and Validation
• Knowledge Representation
• Inferencing
• Explanation and Justification
• 11.8 Problem Areas Suitable for Expert Systems
• 11.9 Development of Expert Systems
• Defining the Nature and Scope of the Problem
• Identifying Proper Experts
• Acquiring Knowledge
• Selecting the Building Tools
• Coding the System
• Evaluating the System
• Application Case 11.5 Clinical Decision Support System for Tendon Injuries
• 11.10 Concluding Remarks
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• End-of-Chapter Application Case Tax Collections Optimization for New York State
• References
• Chapter 12 Knowledge Management and Collaborative Systems
• 12.1 Opening Vignette: Expertise Transfer System to Train Future Army Personnel
• 12.2 Introduction to Knowledge Management
• Knowledge Management Concepts and Definitions
• Knowledge
• Explicit and Tacit Knowledge
• 12.3 Approaches to Knowledge Management
• The Process Approach to Knowledge Management
• The Practice Approach to Knowledge Management
• Hybrid Approaches to Knowledge Management
• Knowledge Repositories
• 12.4 Information Technology (IT) in Knowledge Management
• The KMS Cycle
• Components of KMS
• Technologies That Support Knowledge Management
• 12.5 Making Decisions in Groups: Characteristics, Process,Benefits, and Dysfunctions
• Characteristics of Groupwork
• The Group Decision-Making Process
• The Benefits and Limitations of Groupwork
• 12.6 Supporting Groupwork with Computerized Systems
• An Overview of Group Support Systems (GSS)
• Groupware
• Time/Place Framework
• 12.7 Tools for Indirect Support of Decision Making
• Groupware Tools
• Groupware
• Collaborative Workflow
• Web 2.0
• Wikis
• Collaborative Networks
• 12.8 Direct Computerized Support for Decision Making:From Group Decision Support Systems to Group Su
• Group Decision Support Systems (GDSS)
• Group Support Systems
• How GDSS (or GSS) Improve Groupwork
• Facilities for GDSS
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• End-of-Chapter Application Case Solving Crimes by Sharing Digital Forensic Knowledge
• References
• Part V Big Data and Future Directions for Business Analytics
• Chapter 13 Big Data and Analytics
• 13.1 Opening Vignette: Big Data Meets Big Science at CERN
• 13.2 Definition of Big Data
• The Vs That Define Big Data
• Application Case 13.1 Big Data Analytics Helps Luxottica ImproveIts Marketing Effectiveness
• 13.3 Fundamentals of Big Data Analytics
• Business Problems Addressed by Big Data Analytics
• Application Case 13.2 Top 5 Investment Bank Achieves Single Source of Truth
• 13.4 Big Data Technologies
• MapReduce
• Why Use MapReduce?
• Hadoop
• How Does Hadoop Work?
• Hadoop Technical Components
• Hadoop: The Pros and Cons
• NoSQL
• Application Case 13.3 eBay’s Big Data Solution
• 13.5 Data Scientist
• Where Do Data Scientists Come From?
• Application Case 13.4 Big Data and Analytics in Politics
• 13.6 Big Data and Data Warehousing
• Use Case(s) for Hadoop
• Use Case(s) for Data Warehousing
• The Gray Areas (Any One of the Two Would Do the Job)
• Coexistence of Hadoop and Data Warehouse
• 13.7 Big Data Vendors
• Application Case 13.5 Dublin City Council Is Leveraging Big Datato Reduce Traffic Congestion
• Application Case 13.6 Creditreform Boosts Credit Rating Quality with Big Data Visual Analytics
• 13.8 Big Data and Stream Analytics
• Stream Analytics Versus Perpetual Analytics
• Critical Event Processing
• Data Stream Mining
• 13.9 Applications of Stream Analytics
• e-Commerce
• Telecommunications
• Application Case 13.7 Turning Machine-Generated Streaming Data into Valuable Business Insights
• Law Enforcement and Cyber Security
• Power Industry
• Financial Services
• Health Sciences
• Government
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• End-of-Chapter Application Case Discovery Health Turns Big Data into Better Healthcare
• References
• Chapter 14 Business Analytics: Emerging Trends and Future Impacts
• 14.1 Opening Vignette: Oklahoma Gas and Electric Employs Analytics to Promote Smart Energy Use
• 14.2 Location-Based Analytics for Organizations
• Geospatial Analytics
• Application Case 14.1 Great Clips Employs Spatial Analytics to Shave Time in Location Decisions
• A Multimedia Exercise in Analytics Employing Geospatial Analytics
• Real-Time Location Intelligence
• Application Case 14.2 Quiznos Targets Customers for Its Sandwiches
• 14.3 Analytics Applications for Consumers
• Application Case 14.3 A Life Coach in Your Pocket
• 14.4 Recommendation Engines
• 14.5 Web 2.0 and Online Social Networking
• Representative Characteristics of Web 2.0
• Social Networking
• A Definition and Basic Information
• Implications of Business and Enterprise Social Networks
• 14.6 Cloud Computing and BI
• Service-Oriented DSS
• Data-as-a-Service (DaaS)
• Information-as-a-Service (Information on Demand) (IaaS)
• Analytics-as-a-Service (AaaS)
• 14.7 Impacts of Analytics in Organizations: An Overview
• New Organizational Units
• Restructuring Business Processes and Virtual Teams
• The Impacts of ADS Systems
• Job Satisfaction
• Job Stress and Anxiety
• Analytics’ Impact on Managers’ Activities and Their Performance
• 14.8 Issues of Legality, Privacy, and Ethics
• Legal Issues
• Privacy
• Recent Technology Issues in Privacy and Analytics
• Ethics in Decision Making and Support
• 14.9 An Overview of the Analytics Ecosystem
• Analytics Industry Clusters
• Data Infrastructure Providers
• Data Warehouse Industry
• Middleware Industry
• Data Aggregators/Distributors
• Analytics-Focused Software Developers
• Reporting/Analytics
• Predictive Analytics
• Prescriptive Analytics
• Application Developers or System Integrators: Industry Specific or General
• Analytics User Organizations
• Analytics Industry Analysts and Influencers
• Academic Providers and Certification Agencies
• Chapter Highlights
• Key Terms
• Questions for Discussion
• Exercises
• End-of-Chapter Application Case Southern States Cooperative Optimizes Its Catalog Campaign
• References
• Glossary
• Index
• A
• B
• C
• D
• E
• F
• G
• H
• I
• J
• K
• L
• M
• N
• O
• P
• Q
• R
• S
• T
• U
• V
• W
• X
• Y