#208 Making Prioritization a Priority and Focusing on Delivering Value - Interview w/ Srinivas Paluri

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.Srinivas' LinkedIn: https://www.linkedin.com/in/spaluri/In this episode, Scott interviewed Srinivas Paluri, CTO at Rentbase. Srinivas was previously part of a data mesh implementation as the Senior Director of Data Engineering at Zillow.Some key takeaways/thoughts from Srinivas' point of view:Data mesh advice to former self #1: Ambiguity is inevitable. Don’t be afraid of ambiguity - it's often better than binary thinking - but also be as clear as possible on responsibilities even if the target outcome is ambiguous. Clear responsibilities at least drive things forward.Data mesh advice to former self #2: Involve product management way earlier. Every product owner needs to understand product ownership, prioritization, and what is the value to the business. And then communicate the value of the work to the business. If that value's not clear, why are you doing the work?Data mesh advice to former self #3: Create a small team, maybe 5-6 people, focused on enabling new domains to learn how to own their own data and create data products. Scott note: see episode 48 for how ITV is implementing this patternPrioritization - and communication around prioritization - is probably one of your most useful tools in data mesh. If you get good at that, teams will often buy-in more quickly. Data producers see changing priorities, not more work. Consumers have a clear understanding of what work is done when and why instead of silence or a link to a Confluence or JIRA page.Good data mesh product management isn't only focused at the data product level or the platform. You need to look at the bigger