#32 Applying a Historical Lens to Data Mesh - Interview w/ Azmath Pasha

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 (info gated) Azmath's LinkedIn: https://www.linkedin.com/in/azmathpasha/In this episode, Scott interviews Azmath Pasha, member of the Forbes Technology Council, who has 25+ years in implementing large-scale IT projects including at CapGemini and Paradigm Technology.Azmath gave his 3 key measures for data value: cost savings, business value (e.g. driving new initiatives), and data reuse. For data mesh, the long-term value is in the second two but for Azmath, a PoC could be better served focusing on cost savings as it is easier to track and faster to realize.They dove into the concept of data discovery with human interaction, not purely an online experience. Similar to event storming for discovering your domain events (see DDD for Data episodes), discovery as a purely tool-based experience is always likely to be somewhat lacking. Scott was intrigued about this as that aspect of data discovery hasn't been widely discussed.To Azmath, the data product experience, part of what Zhamak calls 'the experience plane', is crucial. It is much harder to drive buy-in if your product is hard to use / has a bad user experience. Azmath's other crucial aspects to getting a data mesh (or any large scale data project) implementation right included: staying tool agnostic so you can remain "future proof"; supporting data producers to reduce time to delivery, especially initial delivery; and looking at your architecture and tool investments over a 5 year time horizon, not just for the short to medium-term.Azmath wrapped up by saying we are entering a new era of using data, we must democratize the data and also look to new metrics for evaluating the business value of data.Data Mesh Radio is hosted by Scott Hirleman. If you want to connect with Scott, reach out to him on LinkedIn: https://www.linkedin.com/in/scotthirleman/If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see

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Interviews with data mesh practitioners, deep dives/how-tos, anti-patterns, panels, chats (not debates) with skeptics, "mesh musings", and so much more. Host Scott Hirleman (founder of the Data Mesh Learning Community) shares his learnings - and those of the broader data community - from over a year of deep diving into data mesh. Each episode contains a BLUF - bottom line, up front - so you can quickly absorb a few key takeaways and also decide if an episode will be useful to you - nothing worse than listening for 20+ minutes before figuring out if a podcast episode is going to be interesting and/or incremental ;) Hoping to provide quality transcripts in the future - if you want to help, please reach out! Data Mesh Radio is also looking for guests to share their experience with data mesh! Even if that experience is 'I am confused, let's chat about' some specific topic. Yes, that could be you! You can check out our guest and feedback FAQ, including how to submit your name to be a guest and how to submit feedback - including anonymously if you want - here: https://docs.google.com/document/d/1dDdb1mEhmcYqx3xYAvPuM1FZMuGiCszyY9x8X250KuQ/edit?usp=sharing Data Mesh Radio is committed to diversity and inclusion. This includes in our guests and guest hosts. If you are part of a minoritized group, please see this as an open invitation to being a guest, so please hit the link above. If you are looking for additional useful information on data mesh, we recommend the community resources from Data Mesh Learning. All are vendor independent. https://datameshlearning.com/community/ You should also follow Zhamak Dehghani (founder of the data mesh concept); she posts a lot of great things on LinkedIn and has a wonderful data mesh book through O'Reilly. Plus, she's just a nice person: https://www.linkedin.com/in/zhamak-dehghani/detail/recent-activity/shares/ Data Mesh Radio is provided as a free community resource by DataStax. If you need a database that is easy to scale - read: serverless - but also easy to develop for - many APIs including gRPC, REST, JSON, GraphQL, etc. all of which are OSS under the Stargate project - check out DataStax's AstraDB service :) Built on Apache Cassandra, AstraDB is very performant and oh yeah, is also multi-region/multi-cloud so you can focus on scaling your company, not your database. There's a free forever tier for poking around/home projects and you can also use code DAAP500 for a $500 free credit (apply under payment options): https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio