#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