#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...

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Interviews with data mesh practitioners, deep dives/how-tos, anti-patterns, panels, chats (not debates) with skeptics, "mesh musings", and so much more. Host Scott Hirleman (founder of the Data Mesh Learning Community) shares his learnings - and those of the broader data community - from over a year of deep diving into data mesh. Each episode contains a BLUF - bottom line, up front - so you can quickly absorb a few key takeaways and also decide if an episode will be useful to you - nothing worse than listening for 20+ minutes before figuring out if a podcast episode is going to be interesting and/or incremental ;) Hoping to provide quality transcripts in the future - if you want to help, please reach out! Data Mesh Radio is also looking for guests to share their experience with data mesh! Even if that experience is 'I am confused, let's chat about' some specific topic. Yes, that could be you! You can check out our guest and feedback FAQ, including how to submit your name to be a guest and how to submit feedback - including anonymously if you want - here: https://docs.google.com/document/d/1dDdb1mEhmcYqx3xYAvPuM1FZMuGiCszyY9x8X250KuQ/edit?usp=sharing Data Mesh Radio is committed to diversity and inclusion. This includes in our guests and guest hosts. If you are part of a minoritized group, please see this as an open invitation to being a guest, so please hit the link above. If you are looking for additional useful information on data mesh, we recommend the community resources from Data Mesh Learning. All are vendor independent. https://datameshlearning.com/community/ You should also follow Zhamak Dehghani (founder of the data mesh concept); she posts a lot of great things on LinkedIn and has a wonderful data mesh book through O'Reilly. Plus, she's just a nice person: https://www.linkedin.com/in/zhamak-dehghani/detail/recent-activity/shares/ Data Mesh Radio is provided as a free community resource by DataStax. If you need a database that is easy to scale - read: serverless - but also easy to develop for - many APIs including gRPC, REST, JSON, GraphQL, etc. all of which are OSS under the Stargate project - check out DataStax's AstraDB service :) Built on Apache Cassandra, AstraDB is very performant and oh yeah, is also multi-region/multi-cloud so you can focus on scaling your company, not your database. There's a free forever tier for poking around/home projects and you can also use code DAAP500 for a $500 free credit (apply under payment options): https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio