#159 Focusing on the Problems - And Business - at Hand in Your Data Tool Selection Process - Interview w/ Brandon Beidel

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.LinkedIn: https://www.linkedin.com/in/brandonbeidel/In this episode, Scott interviewed Brandon Beidel, Director of Product at Red Ventures.Some key takeaways/thoughts from Brandon's point of view:Be willing to change your mind, especially based on new information. Be willing to measure and iterate. It's easy to get attached to tools or tech because they are cool. Don't! Stay objective.It's crucial to align on what problem(s) you are trying to solve and why before moving forward on vendor/tool selection, no matter build versus buy. If it doesn't have a positive return on investment, why do the work?Beware the sunk cost fallacy! It's easy to not want to shut something down that you've spent a lot on. But don't throw good money after bad.When requirement gathering/negotiating, have a 'maniacal focus' on asking "what does this drive for the business?" You can quickly sort the nice-to-haves from the needs and you can have an open and honest conversation about cost/benefit of each aspect of a request.When thinking about maximizing value, there is always one constraint that is the bottleneck. You can optimize other things but they won't drive the value. Find and fix the value bottleneck.A simple two axes framework when thinking about use cases and requirements is value versus complexity. Look for high value low complexity first.Be open and honest in discussions around expected costs of work/tools - which can be considered part of the complexity. The data consumers understand the value and can weigh the return on investment.It's very important to understand data consumers' incentives so you can collaboratively figure out what is best for all...

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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