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. Contact email: Swimwith[at]gulpdata.com Lauren's LinkedIn: https://www.linkedin.com/in/laurencascio/ Chris' LinkedIn: https://www.linkedin.com/in/censey/ In this episode, Scott interviewed Lauren Cascio, Chief Fish Wrangler, and Chris Ensey, CTO at Gulp Data. From here forward in this write-up, L&C will refer to the combination of Lauren and Chris rather than trying to specifically call out who said which part. Some key takeaways/thoughts from L&C's point of view: ?Controversial?: Many organizations have an incorrect perspective that they mostly have a single type of data that's useful for each use case or need. Typically, their data is useful for many more internal use cases and also to organizations in far different industries.Often, there is a lack of a data sharing culture in many organizations. There isn't anyone that really understands how data flows throughout the organization or especially how it _could_ flow to serve many untapped use cases.There are many people emotionally attached to owning their own data but not in the product sense, they are focused on maintaining control rather than structuring it to be shared. So there are organizational challenges to data sharing in addition to technology.Many organizations have a tough time justifying updating their data infrastructure, leading to more and more challenges with progressing their data journey. It's often hard to point to a tangible ROI on updating the data platform for instance.Far too often, companies and LOBs know they want to analyze some information but they don't really know what they are analyzing it for. Instead of shaping data to make specific decisions, there is a focus on the visualization without a clear action in mind once the data tells them something. Drive towards what you care about and use data to answer those questions, the data doesn't...