#256 How to Drive Towards a Data-Driven Culture with Data Mesh - Interview w/ Amy Edwards

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 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.Amy's LinkedIn: https://www.linkedin.com/in/amy-tang-edwards/In this episode, Scott interviewed Amy Edwards, Formerly Director of Analytics and Product at Vista. To be clear, she was only representing her own views on the episode.Some key takeaways/thoughts from Amy's point of view:A potential Northstar metric for how data driven you are: how often are people getting, interpreting, and actioning on the data themselves versus waiting for an analyst to tell them? Hard to measure but it's where you want to head, where more and more people are making the effort to interact with data.If you want a data product/use case to succeed, the absolute most important thing is an engaged consumer stakeholder - someone who really, really wants the data for a use case and how they want it. If someone isn't leaning in, consider not building something for them. Scott note: this sounds controversial but it is reinforced in almost every data mesh conversation I have.Similarly, if you are building a data product, you should make sure you are very aligned with your stakeholder. Don't be a request dumping ground, build iteratively together.To understand your progress towards being data driven, you need to actually measure things to track changes over time, your progress. You almost certainly will not find the perfect aspects to measure at first and what you measure will evolve. But it starts with measuring something.There's nothing wrong with starting with a using a success metric that you know isn't going to be something you focus on when a data product matures. As products go through phases, so too...