The Analytics Power Hour

There's a certain type of person who first encounters Excel and, instead of running in terror, leans in and grins. Rob Collie has spent his career-from the Excel team at Microsoft to helping birth Power BI to now running P3 Adaptive—building things for exactly those people. He calls them "Crafters," and his new book, Fair Game: Customizing AI to Your Business Is Easier Than You Think, makes the case that this same crowd (hi, it's us) is uniquely positioned to do something genuinely remarkable with AI. Not because we're developers, not because we've cracked some secret, but because we've always lived on the boundary between the business and the tech-and that's precisely where the real AI work happens. The conversation covers the two "voids" crafters need to jump to go from chatting with Claude to actually building useful custom solutions, why the off-the-shelf AI tools are mostly useless for business purposes (and what to do about it), the faucets-first philosophy for semantic models, and why the developer isn't dead-just moving to the suburbs. Also: Tim built a quiz about his marriage and let his adult children take it. That happened.
This episode is brought to you, in part, by our sponsors, Stape and Prism from Ask-Y.
For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

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.
This episode is brought to you, in part, by our sponsors, Stape and Prism from Ask-Y.
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.
This episode is brought to you, in part, by our sponsors, Stape and Prism from Ask-Y.
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.

What do colors, soup kitchens, and mountain climbing have in common? They're all part of the mental models that have shaped how we think about analytics, and they're exactly the kind of durable wisdom that matters more than ever in an age of AI slop. This campfire-style conversation among the co-hosts reveals the concepts, books, and aha moments that have stuck with us across decades of analytics work. From the magic of randomization to the critical distinction between outputs and outcomes, we share the frameworks that guide our thinking whether we're writing SQL by hand or asking Claude to do it for us. It turns out the most valuable analytics wisdom isn't about tools or techniques—it's about understanding how humans actually make decisions, build trust, and collaborate effectively. Some things never go out of style.

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