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Monetizing Your Data

Book Description

Transforming data into revenue generating strategies and actions

Organizations are swamped with data—collected from web traffic, point of sale systems, enterprise resource planning systems, and more, but what to do with it? Monetizing your Data provides a framework and path for business managers to convert ever-increasing volumes of data into revenue generating actions through three disciplines: decision architecture, data science, and guided analytics. There are large gaps between understanding a business problem and knowing which data is relevant to the problem and how to leverage that data to drive significant financial performance. Using a proven methodology developed in the field through delivering meaningful solutions to Fortune 500 companies, this book gives you the analytical tools, methods, and techniques to transform data you already have into information into insights that drive winning decisions. Beginning with an explanation of the analytical cycle, this book guides you through the process of developing value generating strategies that can translate into big returns. The companion website, www.monetizingyourdata.com, provides templates, checklists, and examples to help you apply the methodology in your environment, and the expert author team provides authoritative guidance every step of the way.

This book shows you how to use your data to:

  • Monetize your data to drive revenue and cut costs
  • Connect your data to decisions that drive action and deliver value
  • Develop analytic tools to guide managers up and down the ladder to better decisions

Turning data into action is key; data can be a valuable competitive advantage, but only if you understand how to organize it, structure it, and uncover the actionable information hidden within it through decision architecture and guided analytics. From multinational corporations to single-owner small businesses, companies of every size and structure stand to benefit from these tools, methods, and techniques; Monetizing your Data walks you through the translation and transformation to help you leverage your data into value creating strategies.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Dedication
  5. Preface
  6. Acknowledgments
  7. About the Authors
  8. Section I: Introduction
    1. Chapter 1: Introduction
      1. Decisions
      2. Analytical Journey
      3. Solving the Problem
      4. The Survey Says…
      5. How to Use This Book
      6. Let's Start
    2. Chapter 2: Analytical Cycle: Driving Quality Decisions
      1. Analytical Cycle Overview
      2. Hierarchy of Information User
      3. Next Steps
    3. Chapter 3: Decision Architecture Methodology: Closing the Gap
      1. Methodology Overview
      2. Discovery
      3. Decision Analysis
      4. Monetization Strategy
      5. Agile Analytics
      6. Enablement
      7. Summary
  9. Section II: Decision Analysis
    1. Chapter 4: Decision Analysis: Architecting Decisions
      1. Category Tree
      2. Question Analysis
      3. Key Decisions
      4. Data Needs
      5. Action Levers
      6. Success Metrics
      7. Category Tree Revisited
      8. Summary
  10. Section III: Monetization Strategy
    1. Chapter 5: Monetization Strategy: Making Data Pay
      1. Business Levers
      2. Monetization Strategy Framework
      3. Decision Analysis and Agile Analytics
      4. Competitive and Market Information
      5. Summary
    2. Chapter 6: Monetization Guiding Principles: Making It Solid
      1. Quality Data
      2. Be Specific
      3. Be Holistic
      4. Actionable
      5. Decision Matrix
      6. Grounded in Data Science
      7. Monetary Value
      8. Confidence Factor
      9. Measurable
      10. Motivation
      11. Organizational Culture
      12. Drives Innovation
    3. Chapter 7: Product Profitability Monetization Strategy: A Case Study
      1. Background
      2. Business Levers
      3. Discovery
      4. Decide
      5. Data Science
      6. Monetization Framework Requirements
      7. Decision Matrix
  11. Section IV: Agile Analytics
    1. Chapter 8: Decision Theory: Making It Rational
      1. Decision Matrix
      2. Probability
      3. Prospect Theory
      4. Choice Architecture
      5. Cognitive Bias
    2. Chapter 9: Data Science: Making It Smart
      1. Metrics
      2. Thresholds
      3. Trends and Forecasting
      4. Correlation Analysis
      5. Segmentation
      6. Cluster Analysis
      7. Velocity
      8. Predictive and Explanatory Models
      9. Machine Learning
    3. Chapter 10: Data Development: Making It Organized
      1. Data Quality
      2. Dirty Data, Now What?
      3. Data Types
      4. Data Organization
      5. Data Transformation
      6. Summary
    4. Chapter 11: Guided Analytics: Making It Relevant
      1. So, What?
      2. Guided Analytics
      3. Summary
    5. Chapter 12: User Interface (UI): Making It Clear
      1. Introduction to UI
      2. The Visual Palette
      3. Less Is More
      4. With Just One Look
      5. Gestalt Principles of Pattern Perception
      6. Putting It All Together
      7. Summary
    6. Chapter 13: User Experience (UX): Making It Work
      1. Performance Load
      2. Go with the Flow
      3. Modularity
      4. Propositional Density
      5. Simplicity on the Other Side of Complexity
      6. Summary
  12. Section V: Enablement
    1. Chapter 14: Agile Approach: Getting Agile
      1. Agile Development
      2. Riding the Wave
      3. Agile Analytics
      4. Summary
    2. Chapter 15: Enablement: Gaining Adoption
      1. Testing
      2. Adoption
      3. Summary
    3. Chapter 16: Analytical Organization: Getting Organized
      1. Decision Architecture Team
      2. Decision Architecture Roles
      3. Subject Matter Experts
      4. Analytical Organization Mindset
  13. Section VI: Case Study
    1. Case Study: Michael Andrews Bespoke
      1. Discovery
      2. Decision Analysis Phase
      3. Monetization Strategy, Part I
      4. Agile Analytics
      5. Monetization Strategy, Part II
      6. Guided Analytics
      7. Closing
  14. Bibliography
  15. Index
  16. End User License Agreement