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 here Episode 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. Amritha's LinkedIn: https://www.linkedin.com/in/amritha-arun-babu-a2273729/ In this episode, Scott interviewed Amritha Arun Babu Mysore, Manager of Technical Product Management in ML at Amazon. To be clear, she was only representing only own views on the episode. In this episode, we use the phrase 'data product management' to mean 'product management around data' rather than specific to product management for data products. It can apply to data products but also something like an ML model or pipeline which will be called 'data elements' in this write-up. Some key takeaways/thoughts from Amritha's point of view: "As a product manager, it's just part of the job that you have to work backwards from a customer pain point." If you aren't building to a customer pain, if you don't have a customer, is it even a product? Always focus on who you are building a product for, why, and what is the impact. Data product management is different from software product management in a few key ways. In software, you are focused "on solving a particular user problem." In data, you have the same goal but there are often more complications like not owning the source of your data and potentially more related problems to solve across multiple users.In data product management, start from the user journey and the user problem then work back to not only what a solution looks like but also what data you need. What are the sources and then do they exist yet?Product management is about delivering business value. Data product management is no different. Always come back to the business value from addressing the user problem.Even your data cleaning methodology can impact your data. Make sure consumers that care - usually data scientists - are aware of the decisions you've made. Bring them in as early as possible to help you make decisions that work for all.?Controversial?: Try not to over customize your solutions but oftentimes you will still need to really consider the very specific needs of your...