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Advanced Analytics Methodologies: Driving Business Value with Analytics

Book Description

Advanced Analytics Methodologies is today's definitive guide to analytics implementation for MBA and university-level business students and sophisticated practitioners. Its expanded, cutting-edge coverage helps readers systematically "jump the gap" between their organization's current analytical capabilities and where they need to be.

Step by step, Michele Chambers and Thomas Dinsmore help readers customize a complete roadmap for implementing analytics that supports unique corporate strategies, aligns with specific corporate cultures, and serves unique customer and stakeholder communities. Drawing on work with dozens of leading enterprises, Michele Chambers and Thomas Dinsmore provide advanced applications and examples not available elsewhere, describe high-value applications from many industries, and help you systematically identify and deliver on your company's best opportunities. They show how to:  

  • Go beyond the Analytics Maturity Model: power your unique business strategy with an equally focused analytics strategy

  • Link key business objectives with core characteristics of your organization, value chain, and stakeholders

  • Take advantage of game changing opportunities before competitors do

  • Effectively integrate the managerial and operational aspects of analytics

  • Measure performance with dashboards, scorecards, visualization, simulation, and more

  • Prioritize and score prospective analytics projects

  • Identify "Quick Wins" you can implement while you're planning for the long-term

  • Build an effective Analytic Program Office to make your roadmap persistent

  • Update and revise your roadmap for new needs and technologies

  • This advanced text will serve the needs of students and faculty studying cutting-edge analytics techniques, as well as experienced analytics leaders and professionals including Chief Analytics Officers; Chief Data Officers; Chief Scientists; Chief Marketing Officers; Chief Risk Officers; Chief Strategy Officers; VPs of Analytics or Big Data; data scientists; business strategists; and many line-of-business executives.

    Table of Contents

    1. About This eBook
    2. Title Page
    3. Copyright Page
    4. Dedication Page
    5. Contents
    6. Foreword
    7. Acknowledgments
    8. About the Authors
    9. 1. Principles of Modern Analytics
      1. Deliver Business Value and Impact
      2. Focus on the Last Mile
      3. Leverage Kaizen
      4. Accelerate Learning and Execution
      5. Differentiate Your Analytics
      6. Embed Analytics
      7. Establish Modern Analytics Architecture
      8. Build on Human Factors
      9. Capitalize on Consumerization
      10. Summary
    10. 2. Business 3.0 Is Here
    11. 3. Why You Need a Unique Analytics Roadmap
      1. Overview
      2. Business Area
      3. Data
      4. Approach
      5. Precision
      6. Algorithms
      7. Embedding
      8. Speed
      9. Summary
    12. 4. Analytics Can Supercharge Your Business Strategy
      1. Overview
      2. Case Studies
      3. Summary
    13. 5. Building Your Analytics Roadmap
      1. Overview
      2. Step 1: Identify Key Business Objectives
      3. Step 2: Define Your Value Chain
      4. Step 3: Brainstorm Analytic Solution Opportunities
      5. Step 4: Describe Analytic Solution Opportunities
      6. Step 5: Create Decision Model
      7. Step 6: Evaluate Analytic Solution Opportunities
      8. Step 7: Establish Analytics Roadmap
      9. Step 8: Evolve Your Analytics Roadmap
      10. Summary
    14. 6. Analytic Applications
      1. Overview
      2. Strategic Analytics
      3. Managerial Analytics
      4. Operational Analytics
      5. Scientific Analytics
      6. Customer-Facing Analytics
      7. Summary
    15. 7. Analytic Use Cases
      1. Overview
      2. Prediction
      3. Explanation
      4. Forecasting
      5. Discovery
      6. Simulation
      7. Optimization
      8. Summary
    16. 8. Predictive Analytics Methodology
      1. Overview: The Modern Analytics Approach
      2. Define Business Needs
      3. Build the Analysis Data Set
      4. Build the Predictive Model
      5. Deploy the Predictive Model
      6. Summary
    17. 9. Predictive Analytics Techniques
      1. Overview
      2. Statistics and Machine Learning
      3. The Impact of Big Data
      4. Supervised and Unsupervised Learning
      5. Linear Models and Linear Regression
      6. Generalized Linear Models
      7. Generalized Additive Models
      8. Logistic Regression
      9. Enhanced Regression
      10. Survival Analysis
      11. Decision Tree Learning
      12. Bayesian Methods
      13. Neural Networks and Deep Learning
      14. Support Vector Machines
      15. Ensemble Learning
      16. Automated Learning
      17. Summary
    18. 10. End User Analytics
      1. Overview
      2. User Personas
      3. Analytic Programming Languages
      4. Business User Tools
      5. Summary
    19. 11. Analytic Platforms
      1. Overview
      2. Distributed Analytics
      3. Predictive Analytics Architecture
      4. Modern SQL Platforms
      5. Summary
    20. 12. Attracting and Retaining Analytics Talent
      1. Overview
      2. Culture
      3. Data Scientist Role
      4. Summary
    21. 13. Organizing Analytics Teams
      1. Overview
      2. Centralized versus Decentralized Analytics Team
      3. Center of Excellence
      4. Chief Data Officer versus Chief Analytics Officer
      5. Lab Team
      6. Analytic Program Office
      7. Summary
    22. 14. What Are You Waiting For? Go Get Started!
    23. A. Unsupervised Learning: Unsupervised Neural Networks
      1. Unsupervised Feed-Forward Architectures
      2. Kohonen’s Self-Organizing Map
      3. Related Neural Network Architectures
      4. Examples and Related Neural Network Models
      5. References
    24. Index