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Elements of Data Strategy

A Framework for Data and AI-Driven Transformation

This book provides a holistic and complete view of the data strategy elements, enabling the practitioner at every step.

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About

About

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.

Author

About the Author

Boyan Angelov

Boyan Angelov is a data strategist with broad experience applying systems thinking approaches to complex data projects. 

Contents

Table of Contents

Acknowledgments

Preface

Introduction

  1. Why Did I Write This Book?
  2. Who Is This Book For?
  3. How Is This Book Written?
  4. How to Read This Book?
  5. Systems Thinking for Data Strategy
  6. How Technical Does a Data Strategist Need to be?
  7. Defining Data Strategy With StratOps Principles

Part I: Due Diligence

Overview

Alignment with Business Strategy

  1. Uncovering the Business Strategy and Goals
  2. Steering Group Formation

Current State Analysis: Discovering Where We Stand

  1. Systems Audit
  2. Data Maturity Assessment

Gap Analysis: Looking at the Road Ahead

  1. Competitor Analysis
  2. Ambition Level Setting

Summary

Part II: Design

Overview

  1. Data Strategy Analogies
  2. The Influence Cascade

Use Cases: Designing Data Products

  1. Ideation
  2. Feasibility Study
  3. Prioritization

Data Architecture and Technology: Establishing Foundations

  1. Target Data Architecture
  2. Target Technology

Data Governance: Managing Data Assets at Scale

Operating Model: Setting up the Organization for Success

  1. Data Team Models
  2. Center of Excellence
  3. Change Management

Roadmap: Preparing for Delivery

  1. Preparing a Budget
  2. Planning Ahead With a Timeline

Summary

Part III: Delivery

Overview

Soft Agile: Moving Fast Without Breaking Too Much

  1. Soft Agile Theory
  2. The Implementation Forest

Lean Data: Eliminating Waste

  1. Lean Data Theory
  2. The Knowledge Factory

DataOps: Methods for Value Delivery

  1. Team Data Science Process
  2. Data Platform
  3. MLOps
  4. Templating and Documentation
  5. Kanban
  6. Scrum
  7. Shotgun MVP
  8. Closing the Loop
  9. Sources of Waste
  10. Heuristics

Impact Assessment: Measuring Our Success

Portfolio Management: A 360-Degree View

Summary

Interviews

  1. Nicolas Averseng
  2. Noah Gift
  3. June Dershewitz
  4. Martin Szugat
  5. Amadeus Tunis
  6. Tom Davenport
  7. Stephanie Wagenaar
  8. Doug Laney
  9. Alexander Thamm

Conclusion

Appendix

  1. List of Acronyms and Abbreviations
  2. Architecture and Technology Definitions
  3. Ethics and Privacy Checklist
  4. Data Job Roles
  5. Example of Definition of Done
  6. Example Design Document

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