#107 Focusing on Outcomes and Building Brave Teams in Data - Interview w/ Gretchen Moran

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.Gretchen's LinkedIn: https://www.linkedin.com/in/gretchenmoran/NGS' current openings: https://ngs.wd1.myworkdayjobs.com/ngs_external_career_siteIn this episode, Scott interviewed Gretchen Moran, the Senior Director, Data Products at the National Geographic Society (NGS; the non-profit arm of National Geographic).Some key takeaways/thoughts from Gretchen's point of view:NGS is a bit unique in that they don't have a widely deployed data architecture so they do not have a lot of habits to unlearn. Starting with a greenfield means likely more training and learning/experimenting will be required but at least no institutional unlearning.To move forward with data mesh, organizations must be able to embrace change - and the pain that it will inevitably bring - and embrace ambiguity. You need to move forward and figure it out together but also be okay with failure as a learning experience as you test what works for your organization.To win the hearts and minds of data producers, show them what high-quality data can mean for the organization and their domain/role. Work closely with them, understand their context, hold their hand to bring them along and align them to the vision of data mesh.It's easier to drive buy-in widely if you find the organizational influencers and win them over. It is the domino effect in practice. Partner closely with the influencers early on to drive your initiative forward.For NGS, they are working with a single initial data producing team for their proof of value. The data mesh world seems to be split a bit between working with one or two to three teams in the initial proof of value stage."Any technology effort is still a people effort."We have yet to learn how to leverage the knowledge and context of people without data knowledge in general in the data and analytics space. This is what data mesh tries to unlock but we are still figuring out how to do it well.It's very easy to intimidate people with data. We need to make tech and especially data much less intimidating to push broader adoption. The business context of

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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