The Book Look: Data Strategies for Data Governance

What makes a data book great? Our time is valuable, so a good data book should be concise and practical. It should show us how to do something, step by step, so we can apply the techniques to reinforce and always remember. The experiences of the author should shine through in every chapter. It should also contain at least a small amount of theory, ideally in the form of powerful figures depicting frameworks and approaches.

Data Strategies for Data Governance,” by Marilu Lopez, contains all of these characteristics. Lopez does not waste words, yet her style is conversational and easy to follow. Her practical, agile, and easy-to-communicate (PAC) Data Strategy approach is described in detail, one step at a time. She shares many of her own experiences, adding credibility and depth to the content. And the figures on frameworks and approaches are extremely impactful.

Peter Aiken, President of DAMA International, summarized it best in his endorsements for the book: “There is a wealth of material in this very dense book of extremely useful tips, techniques, and guidance. Probably the most difficult aspect of data strategy formulation centers on the challenge of meaningfully engaging various stakeholders in the necessary dialogs required to take your organization’s data and applying it meaningfully in support of the organizational strategy. The method described provides all the guidance you will need.”

This book shows you how to succeed in data governance by producing an enterprise-level data strategy.

Here is a subset of the book’s introduction, used with permission from Technics Publications:

Most books with “Data Strategy” in their titles focus on strategies for Data Analytics and Big Data. Reviewing the outline of the available books in the market, I noticed the typical pattern is to talk about Data Strategy from a philosophical point of view, describing WHAT it is and WHY it is essential. Some books talk about how to execute a Data Strategy, but no book I have found presents a step-by-step and artifact-supported method on HOW to define Data Strategies, which is what this book will do. This book will present The Data Strategy PAC Method (Pragmatic, Agile, and Communicable – in the sense of readily communicated). I presented this method at a high level in international forums like Dataversity EDW 2021, DGIQ 2021, EDW Digital 2022, and EDW Digital 2023, with excellent and positive feedback from attendees. Now I want to share that methodology in detail with the wider Data Management community.

The Data Strategy PAC Method focuses on three interdependent concepts:

Data Strategy is the highest-level guidance available to an organization, focusing data-related activities on articulated data goal achievements and providing direction and specific guidance when faced with a stream of decisions or uncertainties. (Aiken & Harbour, 2017)

Data Management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles. (DAMA International, 2017)

Data Governance is the exercise of authority, control, and shared decision-making (planning, monitoring, and enforcement) over the management of data assets. (DAMA International, 2017)

The Data Management Building (Figure 1) depicts how these concepts relate to each other. The building represents an organization. Data Management, with all its functions surrounded by the core function, Data Governance, represents the foundation of the building. The apartments on each floor of the building represent the organizational units. The grounded building is due to the robustness of the foundation complemented by the extensions of Data Governance through Data Stewards living on each floor and Data Policies to be followed by the inhabitants of each apartment. Four pillars complement the building structure to prevent collapse: the Data Governance Operating Model, the Data Architecture Operating Model, the Metadata Management Operating Model, and the Data Quality Operating Model.

Based on the Data Strategies Framework (see Figures 2 and 3), Data Strategy is the master guide to constructing the building from the foundation to the roof. The Data Strategies canvases are the blueprints to communicate this to the construction workers. The Data Leader (e.g., Chief Data Officer, Data Governance Lead) is the construction site manager. Through the pages of this book, you will find detailed explanations of the Data Strategies Framework, the canvases used to document Data Strategies, and the specific steps to follow to create a continuous process to produce and maintain the Data Strategies.

I dedicated half of my 32-year corporate life in the financial sector to Data Management-related topics. During those years, I faced different Data Strategies. I did not know what to include in a Data Strategy, but I could tell that they were incomplete and not wholly aligned with business priorities. When I “retired” in 2019, I wanted to keep my mind active, so I started my journey as a Data Management consultant and trainer. My first assignment was defining a Data Strategy. I had no idea how to do it, so I searched the Internet for specific methods. I did not find exactly what I wanted, but I was inspired to develop new ideas.

Learn more about the book here.

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Steve Hoberman

Steve Hoberman

Steve Hoberman has trained more than 10,000 people in data modeling since 1992. Steve is known for his entertaining and interactive teaching style (watch out for flying candy!), and organizations around the globe have brought Steve in to teach his Data Modeling Master Class, which is recognized as the most comprehensive data modeling course in the industry. Steve is the author of nine books on data modeling, including the bestseller Data Modeling Made Simple. Steve is also the author of the bestseller, Blockchainopoly. One of Steve’s frequent data modeling consulting assignments is to review data models using his Data Model Scorecard® technique. He is the founder of the Design Challenges group, Conference Chair of the Data Modeling Zone conferences, director of Technics Publications, and recipient of the Data Administration Management Association (DAMA) International Professional Achievement Award. He can be reached at

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