#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