From theory to action: Data governance made practical

While artificial intelligence integration dominates tech headlines, many companies struggle to become truly data-driven.

In her first book, “Building Data Governance from the Ground Up,” (publisher, library access) Lauren Maffeo’s comprehensive guide navigates readers through the intricacies of building a successful data governance framework. This practical guide empowers decision-makers to implement data governance across their organization. Packed with examples, tasks and reflection prompts, it guides readers through building a framework and monitoring data production. (Stay tuned for our Q&A with the author that touches on the pitfalls for not having a data governance strategy, working at scale and six key points for an effective governance strategy.)

The dream of integrating AI often collides with data reality: Maffeo exposes why individuals downplay their crucial role in data governance when it should become everyone’s responsibility. As the quantity of data explodes, governance becomes mandatory. And, as ethical concerns mount, big tech has lost trust: Maffeo draws attention to the COVID aftermath, where tech giants became among the least trusted organizations by consumers. She urges comprehensive data governance for businesses and consumers alike.

This book emphasizes the creation of a data-sharing culture, which is closely aligned with Open Source principles. Maffeo emphasizes the significance of cross-departmental collaboration, transparency and sharing. The author stresses the importance of shared ownership and encourages a “bottom-up” approach rather than a “top-down” one to foster a new culture of integrating data into decision-making. Data governance should become a concern across the org chart, from sales and marketing to IT and data architects.

Maffeo also emphasizes the importance of allocating ownership. Expanding on the importance of data ownership, Maffeo suggests “data stewards,” who function as strategic decision-makers within specific business domains, as advisors with a focus on unique knowledge with varying degrees of technical expertise.

One of the key recommendations: identifying subject experts to determine appropriate metadata for data and creating summary descriptions for datasets. This facilitates collaboration with technical experts like data engineers for implementing these standards.

Establishing a culture of security awareness then becomes crucial, advocating for a risk-aware approach, where data governance is designed to keep data secure through a multi-step framework. This approach spans across departments, ensuring transparency and integration of security concerns into the organizational environment.

While Maffeo’s practical approach empowers decision-makers with actionable steps, it might come at the cost of depth. The focus on practicality could leave complexities unexplored, especially for those lacking robust expertise. This potential trade-off is crucial to consider, as a purely practical understanding might not suffice in all contexts.

Maffeo leverages her expertise to propose a holistic approach. It goes beyond implementing governance and fostering a new culture of data ownership and management. Transparency and ethical principles, aligned with open source values, ensure trust for both users and internal teams. The book’s numerous examples provide a solid foundation for organizations to build their own data governance journey, from the ground up.

In addition to the book, check out her articles on the topic and a Data Governance class that focuses on building a data-driven culture.