https://www.patreon.com/datameshradio (Data Mesh Radio Patreon) - get access to interviews well before they are released Episode list and links to all available episode transcripts (most interviews from #32 on) https://docs.google.com/spreadsheets/d/1ZmCIinVgIm0xjIVFpL9jMtCiOlBQ7LbvLmtmb0FKcQc/edit?usp=sharing (here) Provided as a free resource by DataStax https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB); George Trujillo's contact info: email (george.trujillo@datastax.com) and https://www.linkedin.com/in/georgetrujillo/ (LinkedIn) Transcript for this episode (https://docs.google.com/document/d/15QaPbgVvALLawq19qe62-CRxVFbawjo1BGOrvt9rX3g/edit?usp=sharing (link)) provided by Starburst. See their Data Mesh Summit recordings https://www.starburst.io/learn/events-webinars/datanova-on-demand/?datameshradio (here) and their great data mesh resource center https://www.starburst.io/info/distributed-data-mesh-resource-center/?datameshradio (here) In 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 those who aren't yet data literate can be extremely valuable. We need to lower the actual bar to leveraging data but also lower the perceived bar to leveraging data. "Metrics + outcomes = value" - without outcomes attached, metrics have no value. Automation is going to be key to many aspects of data mesh. Upskilling people to leverage data will only really pay off if it doesn't mean a large increase in the amount of work to leverage data. User experience is crucial to getting the most value out of your data. Think about your data user experience (DUX) and bring in designers to help optimize the experience and really focus on data as a product thinking. NGS is still trying to find who should own generating and sharing insights on data combined from multiple domains. Is that a centralized insights team? Does that push us too far back towards centralization? It's still early days but those insights are crucial to driving value from data. We will see where new insights come from in data mesh. Will it be more insights from data consumers as they...