#282 Not Sweating the Small Stuff in Data Mesh - Interview w/ Mandeep Kaur

Please Rate and Review us on your podcast app of choice!Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/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. Get in touch with Scott on LinkedIn.Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.Mandeep's LinkedIn: https://www.linkedin.com/in/kaurmandeep80/In this episode, Scott interviewed Mandeep Kaur, Enterprise Information Architect at Nordea Asset Management. To be clear, she was only representing her own views on the episode.Nordea has been on their data mesh journey for a while and Mandeep has been trying to figure out best practices for the hundreds - thousands - of micro decisions in a journey. So how do we get comfortable with making so many calls?Some key takeaways/thoughts from Mandeep's point of view:"1) don't overthink it; 2) bring value out as soon as possible; [and] 3) evolution before completion."The micro decisions in data mesh do matter, give them some thought. But it's important to simply get some perspective from the people who should know best and move forward. That can be from people inside or outside your organization but think about the blast radius of getting something wrong before you fix it. Most times it's smaller than you'd expect.Your first question when considering data mesh: what value am I trying to get out of it? Think about what are the target value propositions and what does it do for the business if this is successful. If you don't have good answers, should you do data mesh?The answers to the 'what value' question of your own mesh journey above should drive your strategy, where you should focus early and what will measure your success. And every organization will have different answers.?Controversial?: There's a LOT of overthinking in most data mesh implementations 😅 come back to your anchoring points around ownership/accountability, product thinking, value proposition, etc. What's important? You can try something and see if it works and change it if it doesn't, don't get caught in analysis paralysis.Relatedly, always focus on the value proposition. If you are delivering value, you can improve the other aspects as you move along and learn to do aspects of your journey better.There's a major challenge in abstract communication, especially about

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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