#138 Getting Started with Data Mesh by Collaborating with the Business - Interview w/ Darshana Thakker

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.Darshana's LinkedIn: https://www.linkedin.com/in/darshana-thakker/In this episode, Scott interviewed Darshana Thakker, Architecture Director at BCG Platinion.Some key takeaways/thoughts from Darshana's point of view:Data can be the enabler to achieving your business goals but only if you actually tie your data work to your business strategy and goals.Speaking data jargon when talking to the business stakeholders makes it hard to actually communicate. Focus on speaking to business outcomes and business value.When selecting your first use case(s) for data mesh, evaluate domains on four different metrics: business value, capabilities, eagerness, and feasibility. There isn't a golden formula but it's likely some domains/use cases will rise to the top pretty quickly. Look to - as best as you can - quantify the incremental business value from something like data mesh. That can be at the micro level - the single use case - or the macro level. But getting specific instead of "let's be data-driven" will lead to better buy-in and partnering with the business side.You need to prepare to evolve your architecture. You can't have things set in stone. But you also can't just throw things against the wall and see what sticks. You need a balance between overly rigid and overly flexible.If your centralized data function isn't a bottleneck, isn't the cause of your data challenges, data mesh probably isn't right for your organization - or at least it doesn't directly address your challenges. Time between identifying useful data and making it reliably available is a good place to look to measure if the centralized team is your bottleneck.A good buy-in driving...