Acknowledgments
Preface
Introduction
- Why Did I Write This Book?
- Who Is This Book For?
- How Is This Book Written?
- How to Read This Book?
- Systems Thinking for Data Strategy
- How Technical Does a Data Strategist Need to be?
- Defining Data Strategy With StratOps Principles
Part I: Due Diligence
Overview
Alignment with Business Strategy
- Uncovering the Business Strategy and Goals
- Steering Group Formation
Current State Analysis: Discovering Where We Stand
- Systems Audit
- Data Maturity Assessment
Gap Analysis: Looking at the Road Ahead
- Competitor Analysis
- Ambition Level Setting
Summary
Part II: Design
Overview
- Data Strategy Analogies
- The Influence Cascade
Use Cases: Designing Data Products
- Ideation
- Feasibility Study
- Prioritization
Data Architecture and Technology: Establishing Foundations
- Target Data Architecture
- Target Technology
Data Governance: Managing Data Assets at Scale
Operating Model: Setting up the Organization for Success
- Data Team Models
- Center of Excellence
- Change Management
Roadmap: Preparing for Delivery
- Preparing a Budget
- Planning Ahead With a Timeline
Summary
Part III: Delivery
Overview
Soft Agile: Moving Fast Without Breaking Too Much
- Soft Agile Theory
- The Implementation Forest
Lean Data: Eliminating Waste
- Lean Data Theory
- The Knowledge Factory
DataOps: Methods for Value Delivery
- Team Data Science Process
- Data Platform
- MLOps
- Templating and Documentation
- Kanban
- Scrum
- Shotgun MVP
- Closing the Loop
- Sources of Waste
- Heuristics
Impact Assessment: Measuring Our Success
Portfolio Management: A 360-Degree View
Summary
Interviews
- Nicolas Averseng
- Noah Gift
- June Dershewitz
- Martin Szugat
- Amadeus Tunis
- Tom Davenport
- Stephanie Wagenaar
- Doug Laney
- Alexander Thamm
Conclusion
Appendix
- List of Acronyms and Abbreviations
- Architecture and Technology Definitions
- Ethics and Privacy Checklist
- Data Job Roles
- Example of Definition of Done
- Example Design Document