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Implementing Analytics

Book Description

Implementing Analytics demystifies the concept, technology and application of analytics and breaks its implementation down to repeatable and manageable steps, making it possible for widespread adoption across all functions of an organization. Implementing Analytics simplifies and helps democratize a very specialized discipline to foster business efficiency and innovation without investing in multi-million dollar technology and manpower. A technology agnostic methodology that breaks down complex tasks like model design and tuning and emphasizes business decisions rather than the technology behind analytics.

  • Simplifies the understanding of analytics from a technical and functional perspective and shows a wide array of problems that can be tackled using existing technology

  • Provides a detailed step by step approach to identify opportunities, extract requirements, design variables and build and test models. It further explains the business decision strategies to use analytics models and provides an overview for governance and tuning

  • Helps formalize analytics projects from staffing, technology and implementation perspectives

  • Emphasizes machine learning and data mining over statistics and shows how the role of a Data Scientist can be broken down and still deliver the value by building a robust development process

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Acknowledgments
  6. Author Biography
  7. Introduction
    1. Organization of Book
    2. Audience
  8. Part 1: Concept
    1. Chapter 1. Defining Analytics
      1. The Hype
      2. The Challenge of Definition
      3. Analytics Techniques
      4. Conclusion of Definition
    2. Chapter 2. Information Continuum
      1. Building Blocks of the Information Continuum
      2. Information Continuum Levels
      3. Summary
    3. Chapter 3. Using Analytics
      1. Healthcare
      2. Customer Relationship Management
      3. Human Resource
      4. Consumer Risk
      5. Insurance
      6. Telecommunication
      7. Higher Education
      8. Manufacturing
      9. Energy and Utilities
      10. Fraud Detection
      11. Patterns of Problems
  9. Part 2: Design
    1. Chapter 4. Performance Variables and Model Development
      1. Performance Variables
      2. Model Development
      3. Champion–Challenger: A Culture of Constant Innovation
    2. Chapter 5. Automated Decisions and Business Innovation
      1. Automated Decisions
      2. Decision Strategy
      3. Decision Automation and Intelligent Systems
      4. Strategy Evaluation
      5. Champion–Challenger Strategies
    3. Chapter 6. Governance: Monitoring and Tuning of Analytics Solutions
      1. Analytics and Automated Decisions
      2. Audit and Control Framework
  10. Part 3: Implementation
    1. Chapter 7. Analytics Adoption Roadmap
      1. Learning from Success of Data Warehousing
      2. The Pilot
    2. Chapter 8. Requirements Gathering for Analytics Projects
      1. Purpose of Requirements
      2. Requirements: Historical Perspective
      3. Requirements Extraction
    3. Chapter 9. Analytics Implementation Methodology
      1. Centralized versus Decentralized
      2. Building on the Data Warehouse
      3. Methodology
    4. Chapter 10. Analytics Organization and Architecture
      1. Organizational Structure
      2. Technical Components in Analytics Solutions
    5. Chapter 11. Big Data, Hadoop, and Cloud Computing
      1. Big Data
      2. Hadoop
      3. Cloud Computing (For Analytics)
  11. Conclusion
    1. Objective 1: Simplification
    2. Objective 2: Commoditization
    3. Objective 3: Democratization
    4. Objective 4: Innovation
  12. References
  13. Index