The Analytics Power Hour

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 V...

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 ...

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, ...

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 d...

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 thin...

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 orde...

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 randomizati...

What do diamond ring shopping, Uber pricing psychology, and active user metrics gone wrong have in common? They all highlight our complicated relationship with precision versus accuracy—and how that relationship can either build or destroy trust in our data. Arik Friedman from Atlassian joins us to unpack why being "about right" often beats being "exactly wrong," and why your nagging feeling that something's off might be a us...

Research and analytics: are they more like peanut butter and chocolate, or more like oil and water? On this episode, we dig into the surprisingly common (and surprisingly unfortunate) divide between these two disciplines with Stefanie Zammit, Global Director of Analytics and Insights at Bang & Olufsen. Stefanie has spent her career bridging the qual and quant worlds, and she makes a compelling case that the best insights come f...

AI is moving fast. But so is life. AI is widely recognized as a must-adopt technology, but how and where are data workers expected to find the time for that?! Organizations are struggling to find effective ways to productively drive healthy adoption of AI: What is it they expect their workers to do with AI? Is it purely an efficiency driver, or should they expect other avenues of value creation to be pursued? What guardrails need t...

The one rule about the Analytics Power Hour is that we don't talk about specific tools. But that doesn't mean we won't talk about tool SELECTION! Jason Packer recently released the second edition of Google Analytics Alternatives, (also available on Amazon) and his approach in the book is very much not an RFP-like "check which features your tool offers" system. And his rationale for that seems just as applicable (to us, at least!) f...

As Kevin McCallister once taught us: just because the house is still standing doesn't mean everything's under control. Everyone's racing to adopt AI, but has anyone actually read the fine print? For this year's International Women's Day episode, we are joined by Aubrey Blanche to unpack the hype, the hidden tradeoffs, and the quiet ways teams are giving up agency in the name of "productivity." We explore how data and tech teams are...

We know what the work of the data practitioner is, right? It's everything from managing data ingestion to data governance to report development to experimental design to basic and advanced analytics. It's writing (or vibe-writing?) SQL or Python or R while also being adept at whatever data stack—no matter how modern—is at hand. Of course, it's a lot more, too! And that's the topic of this episode: the unofficial, often ...

From a professional development perspective, you should always be learning: listening to podcasts, reading books, connecting with internal colleagues, following useful people on Medium and LinkedIn, and so on. Did we mention listening to podcasts? Well, THIS episode of THIS podcast is not really about that kind of learning. It's more about the sort of organizational learning that experimentation and analytics is suppos...

That darn data. It's so complicated and fragmented and gap-filled and noisy that no amount of time is ever enough to truly get to the bottom of all of its complexity. As a result, it's pretty easy to fill all of our time handling as much of that underlying data messiness as possible. At what cost, though? It's easy for the analyst's connection to the business to suffer as they get mired (too) deeply in the data and lose sight of th...

If there's one thing that we absolutely knew would be coming along with the increased interest and use of AI, it would be… more acronyms! And, along with the acronyms, we pretty much could predict that we see a lot of online flexing through casual dropping of said acronyms as though they're deeply understood by everyone who's anyone. We tackled one such acronym on this episode: MCP! That's "model context protocol" for those ...

It's the most…won…derful…tiiiiime…of the year! And by that, we mean it's the time of the year when we sit back, look at each other, and ask, "Where did all the time go?!" We brought back a very special someone for this episode as we collectively reflected on the year—show highlights (and what about those shows have stuck with us), industry reflections, and a little shameless shilling f...

Semantic layers are having something of a moment, but they're not actually new as a concept. Ever since the first database table was designed with cryptic field names that no business user could possibly understand, there's been a need for some form of mapping and translation. Should every company be considering employing a semantic layer? Is the idea of a single, comprehensive semantic layer within an organization a monolithic con...

Before you listen to this episode, can you quantify how useful you expect it to be? That's a prior! And "priors" is a word that gets used a lot in this discussion with Michael Kaminsky as we try to demystify the world of Bayesian statistics. Luckily, you can just listen to the episode once and then update your expectation—no need to simulate listening to the show a few thousand times or crunch any numbers whatsoever. The most...