https://www.patreon.com/datameshradio (Data Mesh Radio Patreon) - get access to interviews well before they are released Episode list and links to all available episode transcripts (most interviews from #32 on) https://docs.google.com/spreadsheets/d/1ZmCIinVgIm0xjIVFpL9jMtCiOlBQ7LbvLmtmb0FKcQc/edit?usp=sharing (here) Provided as a free resource by DataStax https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB); George Trujillo's contact info: email (george.trujillo@datastax.com) and https://www.linkedin.com/in/georgetrujillo/ (LinkedIn) Transcript for this episode (https://docs.google.com/document/d/17LTNtckPujHjShS_tt7N9_e8omi7uPnkLls25lY6VzA/edit?usp=sharing (link)) provided by Starburst. See their Data Mesh Summit recordings https://www.starburst.io/learn/events-webinars/datanova-on-demand/?datameshradio (here) and their great data mesh resource center https://www.starburst.io/info/distributed-data-mesh-resource-center/?datameshradio (here) In 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 with questions to find the right data to address them and people to also do data spelunking with data they aren't sure what it might tell them. Look to the Library and Information Sciences space for how to approach that. We need data librarians, not data publishers. Data publishers are about putting data on the shelf and serving only the known knowns. Data Librarians are there to help people find the information they need to address more of the unknowns - the value of curiosity in driving incremental valuable insights. There is a major mismatch in most organizations between what insights the business units are producing and the key questions the C-level execs care about. Consider creating a Chief Data Analyst type role to pair with execs to make sure insights are produced to support their initiatives, not just answer their questions as they come up. Think ahead, build ahead. Data teams need to take far more practices from general engineering - not just software engineering - so we learn how to better understand requirements. When requirement gathering, expecting the data consumers to know all of their requirements upfront can lead to data consumers asking for the world and a bad mismatch between asks and needs. Look to new ways to exchange information about requirements including the Japanese Obeya technique. Spend the time to ensure you understand how data consumers will derive value from the information you will share with them. That will give you a better...