Catalog & Cocktails: The Honest, No-BS Data Podcast

Juan and Tim's Friday rant covers a lot of ground, from Juan's TED takeaways on AI's unprecedented speed and what it means for humanity, to the uncomfortable shift data teams need to make: work and decisions are the point, not pipelines and gold layers. They dig into what Medallion Architecture 2.0 looks like (feedback loops, insights to action, agent governance), why organizational design theory applies directly to agent swarms, and what library science can teach us about the future data stack. The thread running through all of it: the humans who thrive in this moment won't be the ones who build the most, but the ones with taste.
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Data teams spend enormous energy building pipelines, platforms, and governance frameworks but often skip the most fundamental step: truly understanding what people are actually asking for. In this episode, Juan and Tim sit down with data librarians Jenna Jordan and Amalia Child to explore why library science may be the missing lens for data work.
At the heart of the conversation is the reference interview, a structured technique librarians use to uncover a user's "true information need," which almost never matches the first question they ask. From establishing trust and listening without judgment, to asking open-ended questions and verifying whether the need was actually met, the reference interview offers a rigorous, repeatable framework for anyone serving data users.
If you've ever wondered why data projects deliver less value than expected, this episode will reframe the problem entirely and give you a practical toolkit to start closing the gap.
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Data teams obsess over pipelines and platforms but often skip the most fundamental step: truly understanding what people are actually asking for. We chat with data librarians Jenna Jordan and Amalia Child who share a framework for exactly that; it's called the reference interview, and it might be the most practical toolkit data teams have never used.
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There's a gap at the heart of most organisations and data hasn't filled it. Pete Williams, an experienced data leader, has spent years watching companies build sophisticated data capabilities, only to see insight stall before it reaches action. In this episode, we diagnose why: organisations still make decisions along traditional vertical lines, while data sits in a horizontal layer that was never given real authority. AI doesn't solve this misalignment, it exposes it! Pete makes a compelling case for why fixing this structural void is the defining challenge for data leadership today.
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This is the takeaway episode with Pete Williams, an experienced data leader, who argues that organisations still make decisions along traditional vertical lines, while data sits in a horizontal layer that was never given real authority. Pete makes a compelling case for why fixing this structural void is the defining challenge for data leadership today.
See omnystudio.com/listener for privacy information.