#241 Data Product Success Metrics - A Kinda Deep Dive - Mesh Musings 51

Key summary points:At the start, it's more important to start measuring than it is to measure the right things. Do NOT let analysis paralysis hold you back.Similarly, your success metric measurement framework will probably suck to start. Oh well, get to measuring.Create a framework and tooling/platform capabilities - where necessary/useful - to make measuring and reporting against success metrics simple. That framework should be about defining the metrics and especially how to measure, not what success looks like for individual data products.Use fitness functionsGood metrics to consider in order of usefulness: user satisfaction, user value, data quality, time to business decision, delivery to expectations, time to update (can be squishy), and usagePlease Rate and Review us on your podcast app of choice!Sign up for Data Mesh Understanding's free roundtable and introduction programs here: 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 if you want to chat data mesh.If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf