#293 Adapting Product Management to Data - Finding the Customer Pain and the Value - Interview w/ Amritha Arun Babu Mysore

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.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...

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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