#258 Data Mesh on Hard Mode: Learnings From Airtel's Early Data Mesh Journey - Interview w/ Sid Shah

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 hereEpisode 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 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.Sid's LinkedIn: https://www.linkedin.com/in/siddharthin/In this episode, Scott interviewed Sid Shah, Head of Product Data and Analytics at Airtel, a large India telecom operator. To be clear, he was only representing his own views on the episode.Before we jump in, it's important to note that Airtel are doing data mesh on 'hard mode'. Because of regulatory requirements/restrictions, they are all on-prem. That means extra challenges when it comes to securing compute resources.Some key takeaways/thoughts from Sid's point of view:Sometimes you have stop/start in data transformation. It's okay, you can rebuild your momentum. It's okay to try to move when you aren't 100% sure.One important thing about data mesh is that it can validate that you aren't the only organization facing a lot of the common challenges that come with high scale and business complexity/velocity around data. You are not alone. Scott note: there's a reason 500+ companies are doing data mesh :)Similarly, you can leverage the stories of other organizations on a mesh journey to get buy-in internally. Their stories can help you explain to your organization that others are using this approach, that this isn't just a problem in your organization.Many data issues in a large organization can probably be traced back to poor ownership in some form or fashion. Can the teams who should own data - the ones who know it best - even own the data if they wanted to? Do they have the tooling and capabilities?What data related pains are universal to your organization? Does data mesh target any of those? Those universal...