#228 Keeping Your Eyes on the Prize: The Data Value Chain - Interview w/ Tina Albrecht

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.Tina's LinkedIn: https://www.linkedin.com/in/ch....ristina-albrecht-69a this episode, Scott interviewed Tina Albrecht, Lead Coach for Data-Driven Transformation at Exxeta.Some key takeaways/thoughts from Tina's point of view:Always start from your value chain - how do you actually generate value from data work? Any process or other tool you attempt to leverage that isn't focused on improving your data value chains will likely be ineffective in generating value. And why do data work if not to generate value?Your two most likely reasons you are losing value in your value chain are lack of clear ownership/responsibility and bottlenecks. Look to regularly assess both.When measuring if things are good enough, generally the DORA KPIs are good measures of data process maturity. But also look at two aspects: A) how happy are people (from customers, decision takers up to the team) with the current process. Satisfaction is a great measuring stick because it is highly correlated to effectiveness. And B) how much effectiveness is lost to bottlenecks and constraints.The two ways most data mesh implementations seem to be going wrong are a misinterpretation of Team Topologies and lack of teams owning responsibilities. On the first, there are often breakdowns in how teams collaborate together and on the second, we need the platform team to own enabling domains but the domains keep trying to push work back to the central platform team.It's important to regularly assess if aspects of your data transformation are good enough for now. But it's also very important - and easy to lose sight of - how are your teams feeling during the transformation. If you significantly improve capabilities but everyone is miserable, will they l