Elements of Data Strategy
Elements of Data Strategy
A Framework for Data and AI-Driven Transformation
About the Book
A new challenge emerges as the world moves further into deploying data and AI-powered solutions. Leaders begin to recognize that such efforts can hardly come to fruition without a plan: the missing piece called data strategy.
Designing it falls into the hands of a new role - the data strategist. Managing such complex work requires a blend of skills across disciplines and applying them throughout an organization. This book defines the elements needed, their relationships, and blueprints for conducting the required activities.
This work's core is the 3D model - Due Diligence, Design, and Delivery. These three phases ensure the data strategy is informed, complete, and operational. The book is also supported by interviews and advice from data strategy leaders from organizations such as Amazon, Publicis Sapient, Duke University, Deloitte, and others.
About the Author
Reader Testimonials
Thomas Varekamp
Senior Management Consultant at Capgemini Invent
The frameworks and solutions shown are extremely helpful. It really helps in understanding the topic and provides me with tools that I can use myself. A very helpful book for data strategy consultants that want to help businesses get more value out of their data. It describes the tools and frameworks that can be used to really dig down into how data can further support business operations. 11/10 would recommend it!
Afke Schouten
Head of Data and AI Strategy at Xebia
The book is a great overview of all the things one should consider when building a Data Strategy. I highly recommend this book for anyone building their first Data Strategy. I cannot wait for more deep dives on the specific topics!
Table of Contents
- 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
- Notes
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