#126 Evolving from Data Projects to Data As a Product - A Data Platform Six Years in the Making - Interview w/ Blanca Mayayo and Pablo Alvarez Doval
Data Mesh Radio - Podcast autorstwa Data as a Product Podcast Network
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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.Blanca's LinkedIn: https://www.linkedin.com/in/blancamayayo/Pablo's LinkedIn: https://www.linkedin.com/in/pablodoval/In this episode, Scott interviewed Blanca Mayayo - Product Manager, Data Platforms - and Pablo Alvarez Doval - Lead of Data Platforms and Principal Data Architect - at Plain Concepts. From here forward in this write-up, B&P will refer to Blanca and Pablo rather than trying to specifically call out who said which part.Some key takeaways/thoughts from B&P's point of view:It's easy to fall into adding fit-for-purpose capabilities to your data platform but don't. Stay focused on managing your platform as a product - all aspects of it have lifecycles - and you can't try to fit every use case, especially before there is a need.If transformation, especially data transformation, is not tied to the business strategy, that is a major recipe for failure. You likely won't deliver good business outcomes."Beware the proof of concept" - too many try to do a proof without the actual concept. What are you trying to prove and how will you decide/measure if you proved it?You can have everything necessary for a data initiative to succeed lined up - the sponsors, the will, the budget - and still fail. Nothing is 100%.Three common data initiative failure modes: 1) focusing only on the technology aspect and not does it meet needs and can we maintain and pay for it; 2) only treating it as an urgent tactical needs instead of playing into the broader data strategy; and 3) not considering how to actually do change management.Your platform is a...