#115 Understanding the Data Value Chain - Your Key to Deriving Value from Data - Interview w/ Marisa Fish

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 hereMarisa's LinkedIn: https://www.linkedin.com/in/marisafish/Obeya method: https://obeya-association.com/what-is-an-obeya/MIT course on "The Science of Intelligence": https://cbmm.mit.edu/education/courses/science-intelligenceJohn Duncan paper on brains executing series of programs: https://web.mit.edu/9.s915/www/classes/duncan.pdfIn this episode, Scott interviewed Marisa Fish, Director of Information Management at American National Bank. To be clear, Marisa was only representing her own views on the episode.Some key takeaways/thoughts from Marisa's point of view:Understanding your data value supply chain - the way you derive and deliver value from your data - should be the crux of data and analytics work. The data value supply chain breaks down into sharing the data itself, sharing analytical insights about the data, and managing the data. All three are crucial to creating value from your data.Intentionality is crucial - instead of being reactive, stop and ask what are we trying to accomplish and what value will it drive. Then you will focus much more on high value-impact work.Similarly, think about system engineering work as "mission engineering" - what is your mission in doing your work? Does the work you are prioritizing serve the mission?When sharing information, start from: what is the point, what am I trying to drive with this information exchange? Are you trying to share one person's way of thinking or insights or give others the capability to derive their own insights from the new information? Both are very valid and useful but it's easy to talk past each other if you're not on the same page.So much of the way most organizations work with data is about the known knowns - the data consumer knows what data they want and what questions they want to answer with the data. We need to enable people...