#113 Data Governance In Action: What Does Good Governance Look Like in Data Mesh - Interview w/ Shawn Kyzer and Gustavo Drachenberg

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.Gustavo Drachenberg's LinkedIn: https://www.linkedin.com/in/gusdrach/Shawn Kyzer's LinkedIn: https://www.linkedin.com/in/shawn-kyzer-msit-mba-b5b8a4b/Data Governance In Action: What Does Good Governance Look Like in Data Mesh - Interview w/ Shawn Kyzer and Gustavo DrachenbergIn this episode, Scott interviewed Shawn Kyzer, Principal Data Engineer, and Gustavo Drachenberg, Delivery Lead at Thoughtworks. Both have worked on multiple data mesh engagements including with Glovo starting 2+ years ago.From here forward in this write-up, S&G will refer to Shawn and Gustavo rather than trying to specifically call out who said which part.Some key takeaways/thoughts from Shawn and Gustavo's point of view:It's very easy for centralized governance to become a bottleneck. Make sure any central governance team/board that is making decisions has a way to quickly work through backlog through good delegation. Not every decision needs deep scrutiny from top management. To do federated governance right, you need to enable the enforcement - or often more appropriately the application - of policies through the platform wherever possible. Take the burden off the engineers to comply with your governance standards/requirements.Domains should have the freedom to apply policies to their data products in a way that best benefits the data product consumers. So if there are data quality standard policies, the data product should adhere to the standard for measuring completeness as an aspect of data quality but might be optimized for something other than completeness.The cost of getting anything "wrong" in data previously has been quite high because of how rigid things have been - the cost of change was high. But with data mesh, we are finding new ways to lower the cost of change. So it is okay to start with policies that aren't complete and will evolve as you move along.If you have an existing centralized governance board, that will sometimes make moving to federated governance ... challenging at best ... so you will need a top-down mandate to...