If you've ever poured months into building a semantic layer only to watch it become shelfware the moment the business pivoted, Jacob Matson has some thoughts. And a metaphor. Your data is a jungle—and a semantic layer is a highway. Great if you need to get somewhere fast and reliably (monthly active users: highway, please). But the interesting business questions? The slicing, the dicing, the nuanced dimensions that actually differentiate your company from its competitors? There's no highway for that. There never will be. Jacob, a developer advocate at MotherDuck with deep roots in accounting and ERP systems, joined Michael, Moe, and Julie to talk through what comes after the semantic layer—or at least alongside it. The conversation covered why the most important parts of any business are precisely the parts that resist being modeled in someone else's framework, why AI is actually pretty good at writing SQL but not so great at remembering what it figured out yesterday, and whether the real job to be done here is less about modeling and more about search. Oh, and the uncomfortable truth that at episode 300, we still don't have a great answer for metric drift. But we've got some really good questions.

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

There are roughly a thousand ways to roll out a new analytics platform, a BI tool migration, or an AI initiative to your organization. Most of them involve a town hall, an email with a link to some training materials, and the quiet hope that everyone figures it out. Most of them also don't really work. On this episode, Yehonatan Schwarzmer joined Michael, Val, and Tim to bring some long-overdue organizational change management thinking into the analytics conversation. Yehonatan has the unusual combination of real-world experience in both change management consulting and data leadership, which makes him exactly the right person to explain why the technical rollout is the easy part. The harder part is understanding that when someone says "this tool doesn't have what I need," they might really be saying "I was the hero in the old system and I don't know who I'll be in the new one." The Kübler-Ross grief model shows up. Psychological safety shows up (reluctantly). And Val's question about who analysts should recruit to help them manage change at scale almost gets answered.

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Picture this: four analytics professionals, one live audience, a bunch of submitted questions, and absolutely no filter when it comes to sharing their real thoughts about AI, stakeholder management, and the state of the industry. That's what you get when the Analytics Power Hour goes live from Marketing Analytics Summit, with Michael, Moe, Tim, and Val fielding everything from, "How do I prove I'm a partner rather than just an order taker?" to "What's your icky threshold with AI?" The conversation ping-ponged from the fundamentals—like why curiosity beats feature checklists when selecting tools—to the controversial, including a heated debate about whether AI-generated meeting notes are helpful productivity boosters or lazy crutches that strip away human editorial judgment. Along the way, they tackled data trust issues, the pressure to show AI efficiency gains, and why trying to nail down the "best" deliverable will just trigger existential musings about what a deliverable even IS! Fair warning: Tim gets triggered by AI hype, Moe calls some industry BS, and everyone agrees that being useful beats being right.

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.