#54 Data Mesh Evaluation and Implementation Insights - Interview w/ Steven Nooijen and Guillermo Sánchez

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 hereGoDataDriven Data Mesh Webinar: https://godatadriven.com/topic/webinar-data-mesh-9-feb-2022-thanks/GoDataDriven Self-Service Whitepaper (info-gated): https://godatadriven.com/topic/data-democratization-whitepaper/Steven's LinkedIn: https://www.linkedin.com/in/stevennooijen/Guillermo's LinkedIn: https://www.linkedin.com/in/guillermo-s%C3%A1nchez-dionis/In this episode, Scott interviewed two people from the European data consultancy GoDataDriven - Steven Nooijen, Head of Strategy, and Guillermo Sánchez, Analytics Engineering Tech Lead. Guillermo started off by talking about how for the last ~3 years, he was seeing the data engineering team as the bottleneck before data mesh came onto the scene. For Steven, they were seeing lots of companies that were building out data platforms, especially data lakes, and then not really getting the promised benefits so data mesh made sense. All agreed data mesh is not right for every company and then mentioned some good signs that an organization should consider data mesh. Guillermo pointed to a lot of the usual suspects: size of company, size of data team, how many data consuming teams do you have, how many data sources do you have, etc. He then gave a specific example: if you have a data analyst in a consuming domain that has to wait more than 1 week for data, there is a bottleneck somewhere. Is it centralization? Not sure but time to investigate and that might be where you start to consider data mesh. Steven gave the example of an even earlier indicator that bottlenecks are occurring: teams start to hire their own data people rather than leverage the central team. Guillermo also pointed to the rise of consuming teams getting direct data access from producing teams instead of going through the data team.Guillermo made a very crucial point: data mesh is really about interfaces. People talk about data...