#180 Shared Understanding Leads to Data Value That's Outstanding - Interview w/ Chris Dove

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.Chris' LinkedIn: https://www.linkedin.com/in/charles-dove-b4715723/In this episode, Scott interviewed Chris (Charles) Dove, Data Architect at Endava. To be clear, he was only representing his own views.Some key takeaways/thoughts from Chris' point of view:Data used by only one use case in one way is not how you make money by leveraging data, it's too expensive. Set yourself up to reuse data and make sure the organization is aware of what data is available.?Controversial?: Tooling around data, especially metadata, has gotten better. But is it good yet? There are still some major fundamental gaps that seem like basic blocking and tackling around sharing data.Data isn't the point, it's merely a vehicle for exchanging information.Far too often there is an implicit understanding of a taxonomy/shared terms in different business units that is actually incorrect which leads to misunderstandings and mismatched data being treated as the same. But it's not easy to make all aspects of all parts of data explicit and easily understandable, we have to invest and find good ways to do that.!Incrementally Important!: Business people in domains often don't understand their own data because it's embedded in an application. So they only experience their data in a context that is already framed for them by the application. So they don't think about someone else not understanding the data inherently when those others _aren't_ experiencing it through the same application.Getting to a 'good enough' level of documentation is crucial to prevent misuse of data based on misunderstandings. But every organization has to figure out what is good enough and how to get there,...