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The Analytics Power Hour: Daily Summaries Delivered
by Michael Helbling, Moe Kiss, Tim Wilson, Val Kroll, and Julie Hoyer
Attend any conference for any topic and you will hear people saying after that the best and most informative discussions happened in the bar after the show. Ready any business magazine and you will find an article saying something along the lines of "Business Analytics is the hottest job category out there, and there is a significant lack of people, process and best practice." In this case the conference was eMetrics, the bar was….multiple, and the attendees were Michael Helbling, Tim Wilson and Jim Cain (Co-Host Emeritus). After a few pints and a few hours of discussion about the cutting edge of digital analytics, they realized they might have something to contribute back to the community. This podcast is one of those contributions. Each episode is a closed topic and an open forum with some combination of Michael, Moe, Tim, Val, and Julie - the goal is for listeners to enjoy listening to them share their thoughts and experiences and, hopefully, take away something to try at work the next day.
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- 5 days ago45 min
#306: Decision Support Has Been the Point All Along
Before there was BI, there were "decision support systems." Somewhere along the way, we seem to have quietly dropped the "decision support" part and just kept the systems. Zohar Strinka, founder of Analytics Strategies and creator of the Meta-Problem Method, joined us to put the point back where it belongs: if nothing is going to be done differently after the analysis, then the analysis had exactly zero effect on the world. But -- and this is the part that's easy to miss -- that does NOT mean marching up to a stakeholder and demanding, "What decision are you going to make?" People don't want a model that hands them the right answer. They want to understand and weigh the trade-offs themselves, which means good decision support looks a lot more like a really well-informed pro/con list than a score. We got into problem spaces, high-yield problems, the cost of being wrong in each direction, why "we have all this data, so the answer must be in here" is really just a person pulverizing a bag of rocks hoping for diamonds, and, ultimately, peanut butter. 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.
- 2 weeks ago1h 4m
#305: Personal Interest + Analytics Chops = Career?
Stephen Follows has spent 15 years turning film-industry curiosity into a career — asking questions like how much movies really earn, why poster colors have shifted over decades, and whether old studio domains are still up for grabs. In this episode, he joined us to talk about building that unusual path, the cautionary tale of TheNumbers.com, and his theory about Sandra Bullock. Go start your passion project. This episode is brought to you, in part, by our sponsors, Prism from Ask-Y and Stape. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.
- Aug 18, 20261h 7m
#304: I Can Haz AI?
Everyone's posting their AI projects online like proud pet owners sharing videos of their cat doing something marginally impressive — cute, occasionally clever, sometimes a little cringe. And the Analytics Power Hour is no different! In this co-hosts-only episode, Tim, Michael, and Julie skip the thought leadership hot takes and just… compare notes. What have they actually built? What broke? What surprised them? From a custom GPT podcast librarian to a full-blown show production app wired up to Neon, Vercel, Resend, and about five other things Michael is only sort of sure he set up correctly, to a Gemini Gem that simulates a client interaction so realistically it raises your blood pressure in a safe environment — there's a lot of ground covered. Plus: why AI-generated communication has a Stevia aftertaste, why deploying AI context across a team is way harder than it looks, and why the LLM will absolutely tell you what you want to hear about your Meta spend if you give it half a chance. 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.
- Aug 4, 20261h 3m
#303: Funnels Assume Progress. Barriers Recognize Reality.
Twenty years of digital marketing created something of a monster: companies poured enormous investment into analytics teams, MarTech stacks, media capabilities, and data infrastructure—and then watched all those functions march off into their respective silos to work really, really hard at producing activity rather than impact. Rusty Rahmer, founder of Starize AI and author of Working As Designed, joined Michael, Julie, and Val to dig into why that happened, why it's still happening, and what it actually takes to flip the shovel over and use the right end. Along the way, Rusty—who Val correctly identified early as a spontaneous analogy machine—explained why customer journey maps on walls are basically the statistical average American life that literally nobody lives, why waffle fries are structurally superior to ridged chips (and what that has to do with cross-functional team design), and why the most important question a marketing leader can ask a room full of executives is also the one most likely to be met with complete silence. 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.
- Jul 21, 20261h 8m
#302: It Was a Dark and Stormy Insight...
Here's a question analysts almost never ask themselves before walking into a room: why am I actually here? Not "because it's the weekly meeting" or "because someone asked me to pull the campaign readout." But WHY—what difference will the information coming out of my mouth make, and for whom? Aleya Harris, bestselling author, TEDx speaker, and strategic storytelling advisor, joined Tim and Moe to dig into exactly this kind of thing—and she did not pull her punches. Analysts are often the smartest people in the room, she says, and that might actually be the problem. The gap between "here's what the data shows" and "here's what we should do about it" is precisely where story lives, and it turns out storytelling isn't some soft, hand-wavy thing marketing people do—it's a repeatable, learnable framework that has been working on human brains for millennia. Plus: a cautionary tale about ranking on page one for the wrong keywords, the tweaking-the-deck death spiral decoded, and an elevator pitch exercise that reveals your carefully crafted slide deck says something completely different than what you actually think. 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.
- Jul 7, 20261h 16m
#301: It Turns Out Analysts Are Natural AI Crafters
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.
- Jun 23, 202658 min
#300: Are Semantic Layers Really Necessary?
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.
- Jun 9, 20261h 0m
#299: AI Can (Help) Build the Dashboard. It Can't Build the Buy-In.
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.
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About The Analytics Power Hour
Attend any conference for any topic and you will hear people saying after that the best and most informative discussions happened in the bar after the show. Ready any business magazine and you will find an article saying something along the lines of "Business Analytics is the hottest job category out there, and there is a significant lack of people, process and best practice." In this case the conference was eMetrics, the bar was….multiple, and the attendees were Michael Helbling, Tim Wilson and Jim Cain (Co-Host Emeritus). After a few pints and a few hours of discussion about the cutting edge of digital analytics, they realized they might have something to contribute back to the community. This podcast is one of those contributions. Each episode is a closed topic and an open forum with some combination of Michael, Moe, Tim, Val, and Julie - the goal is for listeners to enjoy listening to them share their thoughts and experiences and, hopefully, take away something to try at work the next day.
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