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. Benny's LinkedIn: https://www.linkedin.com/in/bennybenford/ Iulia's LinkedIn: https://www.linkedin.com/in/iuliavarvara/ Nailya's LinkedIn: https://www.linkedin.com/in/nailya-sabirzyanova-5b724310b/ Stefan's LinkedIn: https://www.linkedin.com/in/stefan-zima-650229b7/ In this episode, guest host Benny Benford, Founder and CEO at Datent - a data transformation focused consultancy/community - and guest of episode #244 facilitated a discussion with Iulia Varvara, Advisory Consultant in Digital and Organizational Transformation at Thoughtworks (guest of episode #268), Nailya Sabirzyanova, Digitalization Manager at DHL (guest of a soon-to-be-released episode), and Stefan Zima, Data Transformation Lead at Raiffeisen Bank International AG (guest of episode #270). As per usual, all guests were only reflecting their own views. The topic for this panel was transformation when it comes to data and data mesh in general but especially understanding how organizational transformation must play a large part in a data mesh implementation to be successful. And that transformation is not simply making changes, it is making _lasting_ changes. Organizational transformation is a crucial aspect of doing data mesh even if it's not spoken about all that often. Scott note: I wanted to share my takeaways rather than trying to reflect the nuance of the panelists' views individually. Scott's Top Takeaways: Transformation means changing something. We aren't starting from scratch. You have to consider the starting points, not only the target end points - and in data mesh, there isn't really an end. Every organization's transformation starting point, whether a data mesh transformation or otherwise, will be unique so adjust your...