#101 H&M's Data Mesh Journey So Far Including Finding Reusability in Interesting Places - Interview w/ Erik Herou

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.Erik's LinkedIn: https://www.linkedin.com/in/erikherou/H&M Career page: https://career.hm.com/In this episode, Scott interviewed Erik Herou, Lead Engineer of the Data Platform at H&M. To be clear, Erik was only representing his own views and perspectives.A few key thoughts/takeaways from Eric's point of view:Data mesh can work well with a product-centric organization strategy as both look to put ownership and product thinking in the hands of the domains.To develop a good data/enablement platform for data mesh, look to work with a number of different types of teams. That way, you can see the persistent/reusable patterns and capabilities to find ways to reduce friction for future data product development/deployment.H&M had an existing cloud data lake that was/is working relatively well for existing use cases. But the team knew it likely wouldn't be able to handle where they wanted to go with many more teams producing data products of much higher quality and potentially sophistication.When implementing data mesh - or any data initiative really - it is easy to fall into the trap of doing things the same way you did before. The "old way" feels safe and it was/is still working relatively well for H&M. So they treated their data mesh implementation as almost a greenfield deploy.Because of the long-term focus on making it low friction and scalable to share data - the consumers will come as you make them more data literate - most of the early data/enablement platform work has been focused on helping data producers. A common pattern in data mesh but your constraints and needs may not match.Erik's team is focused on enabling data producers first specifically so his team doesn't become a bottleneck. It is easy for a platform team doing any part of the individual work to become that bottleneck.Consider how much organizational change you require before starting to create mesh data products. H&M did a large amount of that organizational change, other companies start in their current structure and evolve as they learn more. Both are valid and can work well.Specific to...