#153 Federated Data Governance Through Changing Minds and Hearts - Interview w/ Mariana Hebborn, PhD

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/Please Rate and Review us on your podcast app of choice!If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereEpisode list and links to all available episode transcripts here.Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.Mariana's LinkedIn: https://www.linkedin.com/in/mariana-hebborn-phd-118035117/In this episode, Scott interviewed Mariana Hebborn, Lead of Data Governance for the Healthcare Sector at Merck Group Germany (not Merck, the pharmaceutical company).Some key takeaways/thoughts from Mariana's point of view:It's crucial to answer why are you doing data governance. Is it for improving data quality? Better data security? Know what you are trying to achieve to best focus your efforts.Make it easy for people to understand how and why to share their knowledge with the rest of the organization. The people mindset really is the most important aspect of successful digital and/or data transformation.Most everyone knows we need to go to federated data governance but the big question is how. How can we do it safely? How can we evolve? It isn't a simple switch we can flip.To drive buy-in for moving from a centralized data governance approach, we need to show the benefits of federated - when done well - versus a monolithic approach.At the end of the day, governance is about conversation and missioning - why should you care about governance? What value will it drive for your organization? Answer those questions first.We need to find ways to organize closer to the source to capture far more domain knowledge when sharing data. Centralized teams just can't understand the context in a large and complex organization.?Controversial?: Most data access should be to packaged insights - the computational result - rather than raw data itself. Most people consuming information want the insights, not the raw data.We need to take learnings from operations