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Monetizing your data : a guide to turning data into profit driving strategies and solutions /
Using a proven methodology developed in the field, this invaluable book gives you the analytical tools, methods, and techniques to transform data you already have into information into insights that drive winning decisions. --
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons,
[2017]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright
- Contents
- Preface
- Acknowledgments
- About the Authors
- Section I: Introduction
- Chapter 1: Introduction
- Decisions
- Analytical Journey
- Solving the Problem
- The Survey Says...
- How to Use This Book
- Let's Start
- Chapter 2: Analytical Cycle: Driving Quality Decisions
- Analytical Cycle Overview
- Hierarchy of Information User
- Next Steps
- Chapter 3: Decision Architecture Methodology: Closing the Gap
- Methodology Overview
- Discovery
- Decision Analysis
- Monetization Strategy
- Agile Analytics
- Enablement
- Summary
- Section II: Decision Analysis
- Chapter 4: Decision Analysis: Architecting Decisions
- Category Tree
- Question Analysis
- Key Decisions
- Data Needs
- Action Levers
- Success Metrics
- Category Tree Revisited
- Summary
- Section III: Monetization Strategy
- Chapter 5: Monetization Strategy: Making Data Pay
- Business Levers
- Monetization Strategy Framework
- Decision Analysis and Agile Analytics
- Competitive and Market Information
- Summary
- Chapter 6: Monetization Guiding Principles: Making It Solid
- Quality Data
- Be Specific
- Be Holistic
- Actionable
- Decision Matrix
- Grounded in Data Science
- Monetary Value
- Confidence Factor
- Measurable
- Motivation
- Organizational Culture
- Drives Innovation
- Chapter 7: Product Profitability Monetization Strategy: A Case Study
- Background
- Business Levers
- Discovery
- Decide
- Data Science
- Monetization Framework Requirements
- Decision Matrix
- Section IV: Agile Analytics
- Chapter 8: Decision Theory: Making It Rational
- Decision Matrix
- Probability
- Prospect Theory
- Choice Architecture
- Cognitive Bias
- Chapter 9: Data Science: Making It Smart
- Metrics
- Thresholds
- Trends and Forecasting
- Correlation Analysis
- Segmentation
- Cluster Analysis.
- Velocity
- Predictive and Explanatory Models
- Machine Learning
- Chapter 10: Data Development: Making It Organized
- Data Quality
- Dirty Data, Now What?
- Data Types
- Data Organization
- Data Transformation
- Summary
- Chapter 11: Guided Analytics: Making It Relevant
- So, What?
- Guided Analytics
- Summary
- Chapter 12: User Interface (UI): Making It Clear
- Introduction to UI
- The Visual Palette
- Less Is More
- With Just One Look
- Gestalt Principles of Pattern Perception
- Putting It All Together
- Summary
- Chapter 13: User Experience (UX): Making It Work
- Performance Load
- Go with the Flow
- Modularity
- Propositional Density
- Simplicity on the Other Side of Complexity
- Summary
- Section V: Enablement
- Chapter 14: Agile Approach: Getting Agile
- Agile Development
- Riding the Wave
- Agile Analytics
- Summary
- Chapter 15: Enablement: Gaining Adoption
- Testing
- Adoption
- Summary
- Chapter 16: Analytical Organization: Getting Organized
- Decision Architecture Team
- Decision Architecture Roles
- Subject Matter Experts
- Analytical Organization Mindset
- Section VI: Case Study
- Case Study: Michael Andrews Bespoke
- Discovery
- Decision Analysis Phase
- Monetization Strategy, Part I
- Agile Analytics
- Monetization Strategy, Part II
- Guided Analytics
- Closing
- Bibliography
- Index
- EULA.