#201 Choose Your Blast Radius and Other Lessons Learned Across 10s of Data Mesh Implementations - Interview w/ Vanya Seth

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.Vanya's LinkedIn: https://www.linkedin.com/in/vanyaseth1809/In this episode, Scott interviewed Vanya Seth, Head of Technology for Thoughtworks India and Global 'Data Mesh Guild' Lead for Thoughtworks. To be clear, Vanya was only representing her own views on the episode.Some key takeaways/thoughts from Vanya's point of view:Data mesh is at a similar inflection point to where microservices was a decade ago. Let's not relearn all the hard lessons they already learned. We should adapt/contextualize to data of course but we can skip a lot of the anti-patterns. Similarly, many people are stuck thinking "there's no way that could work" regarding data mesh like they were when people suggested development and operations be combined in DevOps. It's understandable - it's hard to imagine a post monolithic world when all you've known is monoliths.?Controversial?: We should try hard to prevent creating the fear of missing out (FOMO) for those not doing data mesh. If data mesh isn't right for your org, especially if it isn't right at this time, that's perfectly okay. Don't take on the overhead cost of data mesh if it won't bring more value than cost. Scott note: PREACH!?Controversial?: Some CDOs or CAOs, their organizations don't really get the value of data so they are implementing data mesh to try to prove out value and make their mark. That can obviously create issues if their organizations aren't ready.A few indicators an org is ready for data mesh (see below for expanded context): A) data/AI investments are not delivering the promised/expected returns and/or it's hard to point to the value delivered in general from data/AI investments; B) the organization is attempting...