Front Cover -- Implementing Analytics -- Copyright Page -- Contents -- Acknowledgments -- Author Biography -- Introduction -- Organization of Book -- Part 1 -- Part 2 -- Part 3 -- Audience -- 1 Concept -- 1 Defining Analytics -- The Hype -- The Challenge of Definition -- Definition 1: Business Value Perspective -- Definition 2: Technical Implementation Perspective -- Analytics Techniques -- Algorithm versus Analytics Model -- Forecasting -- Descriptive Analytics -- Clustering -- Predictive Analytics -- Prediction versus Forecasting -- Prediction Methods -- Regression -- Data Mining or Machine Learning -- Text Mining -- Decision Optimization -- Conclusion of Definition -- 2 Information Continuum -- Building Blocks of the Information Continuum -- Theoretical Foundation in Data Sciences -- Tools, Techniques, and Technology -- Skilled Human Resources -- Innovation and Need -- Information Continuum Levels -- Search and Lookup -- Implementation -- Challenges -- Counts and Lists -- Implementation -- Challenges -- Operational Reporting -- Implementation -- Challenges -- Summary Reporting -- Implementation -- Challenges -- Historical (Snapshot) Reporting -- Implementation -- Challenges -- Metrics, KPIs, and Thresholds -- Implementation -- Challenges -- Analytical Applications -- Implementation -- Challenges -- Analytics Models -- Implementation -- Challenges -- Decision Strategies -- Implementation -- Challenges -- Monitoring and Tuning-Governance -- Implementation -- Challenges -- Summary -- 3 Using Analytics -- Healthcare -- Emergency Room Visit -- Analytics Solution -- Patients with the Same Disease -- Analytics Solution -- Customer Relationship Management -- Customer Segmentation -- Analytics Solution -- Propensity to Buy -- Analytics Solution -- Human Resource -- Employee Attrition -- Analytics Solution -- Resumé Matching -- Analytics Solution.

Consumer Risk -- Borrower Default -- Analytics Solution -- Insurance -- Probability of a Claim -- Analytics Solution -- Telecommunication -- Call Usage Patterns -- Analytics Solution -- Higher Education -- Admission and Acceptance -- Analytics Solution -- Manufacturing -- Predicting Warranty Claims -- Analytics Solution -- Analyzing Warranty Claims -- Analytics Solution -- Energy and Utilities -- The New Power Management Challenge -- Analytics Solution -- Fraud Detection -- Benefits Fraud -- Analytics Solution -- Credit Card Fraud -- Analytics Solution -- Patterns of Problems -- How Much Data -- Performance or Derived Variables -- 2 Design -- 4 Performance Variables and Model Development -- Performance Variables -- What are Performance Variables? -- Reasons for Creating Performance Variables -- What If No Pattern is Reliably Available? -- Benefit of Using Performance Variables -- Creating Performance Variables -- Grain -- Range -- Spread -- Designing Performance Variables -- Discrete versus Continuous -- Nominal versus Ordinal -- Atomic versus Aggregate -- Working Example -- Model Development -- What is a Model? -- Model and Characteristics in Predictive Modeling -- Model and Characteristics in Descriptive Modeling -- Model Validation and Tuning -- Predictive Model Validation -- Validation Approaches: Parallel Run -- Validation Approaches: Retrospective Processing -- Champion-Challenger: A Culture of Constant Innovation -- 5 Automated Decisions and Business Innovation -- Automated Decisions -- Decision Strategy -- Business Rules in Business Operations -- Expert Business Rules -- Quantitative Business Rules -- Decision Automation and Business Rules -- Joint Business and Analytics Sessions for Decision Strategies -- Examples of Decision Strategy -- Retail Bank -- Decision Variables and Cutoffs -- Insurance Claims.

Decision Strategy in Descriptive Models -- Decision Automation and Intelligent Systems -- Learning versus Applying -- Strategy Integration Methods -- ETL to the Rescue -- Strategy Evaluation -- Retrospective Processing -- Reprocessing -- Champion-Challenger Strategies -- Business Process Innovation -- 6 Governance: Monitoring and Tuning of Analytics Solutions -- Analytics and Automated Decisions -- The Risk of Automated Decisions -- Monitoring Layer -- Audit and Control Framework -- Organization and Process -- Audit Datamart -- Unique Features of Audit Datamart -- Control Definition -- Best-Practice Controls -- Expert Controls -- Analytical Controls -- Reporting and Action -- 3 Implementation -- 7 Analytics Adoption Roadmap -- Learning from Success of Data Warehousing -- Lesson 1: Simplification -- Lesson 2: Quick Results -- Lesson 3: Evangelize -- Lesson 4: Efficient Data Acquisition -- Lesson 5: Holistic View -- Lesson 6: Data Management -- The Pilot -- Business Problem -- Management Attention and Champion -- The Project -- Existing Technology -- Existing Skills -- Source System Analysis -- Data Modeling -- ETL Design, Development, and Execution -- Metadata Management and Data Governance -- Job Scheduling and Error Handling -- Database Management and Query Tuning -- Reporting and Analysis -- Results, Roadshow, and Case for Wider Adoption -- Problem Statement -- Data and Value -- Roadshow -- Wider Adoption -- 8 Requirements Gathering for Analytics Projects -- Purpose of Requirements -- Requirements: Historical Perspective -- Calculations -- Process Automation -- Analytical and Reporting Systems -- Analytics and Decision Strategy -- Requirements Extraction -- Problem Statement and Goal -- Working Example from Consumer Credit -- Data Requirements -- Data Requirements Methodology -- Model and Decision Strategy Requirements.

Business Process Integration Requirements -- 9 Analytics Implementation Methodology -- Centralized versus Decentralized -- Centralized Approach -- Decentralized Approach -- A Hybrid Approach -- Building on the Data Warehouse -- Methodology -- Requirements -- Analysis -- Problem Statement and Goal-Analysis -- Profiling and Data-Analysis -- Syntactic Profiling -- Semantic Profiling -- Model and Decision Strategy-Analysis -- Operational Integration-Analysis -- Audit and Control-Analysis -- Design -- Data Warehouse Extension -- Analytics Variables -- Base Variables -- Performance Variables -- Model Characteristics -- Decision Variables -- Analytics Datamart -- Decision Strategy -- Operational Integration -- Strategy Firing Event -- Input Data -- Output Decision -- Analytics Governance-Audit and Control -- Implementation -- Deployment -- Execution and Monitoring -- 10 Analytics Organization and Architecture -- Organizational Structure -- BICC Organization Chart -- Roles and Responsibilities -- ETL -- Data Architecture -- Business Analysts -- Analytics -- Analytics Modeling -- Analytics Technology -- Decision Strategy -- Information Delivery -- Skills Summary -- Analytics Analyst -- Analytics Architect -- Analytics Specialist -- Technical Components in Analytics Solutions -- Analytics Datamart -- Base Analytics Data -- Performance Variables -- Reporting Variables -- Third-Party Variables -- Aggregate Variables -- Model and Characteristics -- Model Execution, Audit, and Control -- 11 Big Data, Hadoop, and Cloud Computing -- Big Data -- Velocity -- Variety -- Volume -- Big Data Implementation Challenge -- Controlling the Size -- Applying the Information Continuum -- Hadoop -- Hadoop Technology Stack -- Data Sources -- Hadoop Data Store -- Data Processing -- Data Access -- User Applications (User Experience) -- Hadoop Solution Architecture.

Hadoop as an ETL Engine -- Hadoop as an Analytical Engine -- Big Data and Hadoop-Working Example -- Cloud Computing (For Analytics) -- Disintegration in Cloud Computing -- Analytics in Cloud Computing -- Conclusion -- Objective 1: Simplification -- Objective 2: Commoditization -- Objective 3: Democratization -- Objective 4: Innovation -- References -- Index.