The Analytics Power Hour

1. #310: A Multi-Stop Tour of AI in Data and Analytics

   10.11.20261 Std. 5 Min.Folge 310Als Nächstes

   It can be hard to hear an AI story these days without wondering how much of it is sensationalized. So, we went looking for some reality on the ground. Moe rounded up four of her colleagues from Canva—Christian …

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   It can be hard to hear an AI story these days without wondering how much of it is sensationalized. So, we went looking for some reality on the ground. Moe rounded up four of her colleagues from Canva—Christian Sullivan, Nina Kumagai, Jack O'Hagan, and Flynn Alexander—and we made a series of short stops with each of them: an agent that investigates metric alerts (and cut the mean time to investigate from about two and a half days to about two hours), a prompt analytics platform prototyped during an AI Discovery Week (after a year of privacy and legal groundwork), a skill that synthesizes what a company has learned across its experiments (neutral results included!), and an LLM serving as the "resident expert" interface to a Markov chain model of user journeys. A common thread? Each of them found that the AI worked a lot better once they took some of the work away from it. And, no, AI did not get final say on the episode title, despite its insistence that a certain Antipodean phrase would land just fine.

   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: https://analyticshour.io/2026/11/10/310-a-multi-stop-tour-of-ai-in-data-and-analytics/

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2. #309: Interviewing in a Dynamically Evolving Job Market

   27.10.202656 Min.Folge 309Als Nächstes

   Here is an interview question for you: tell us about a time you knew exactly how to do something, and then completely failed to explain it to three strangers in a conference room. If an example sprang to mind …

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   Here is an interview question for you: tell us about a time you knew exactly how to do something, and then completely failed to explain it to three strangers in a conference room. If an example sprang to mind immediately, this episode is for you. It was prompted by a note from a listener who is confident in his ability to do the work and less confident in his ability to talk about it under pressure. So, we talked about it (under very little pressure). Topics covered: thinking out loud, saying "I haven't come across that, but I'd love to understand it" instead of "I don't know," having your behavioral examples ready before someone asks for them, building a sprawling document of everything you've ever done so an LLM can grill you on it, and asking questions at the end that you actually want answered. Consensus: it's good to demonstrate that you can use AI effectively. No consensus: whether or not you should bring an iced coffee with you. He did not ask about the iced coffee. We covered it anyway.

   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: https://analyticshour.io/2026/10/27/309-interviewing-in-a-dynamically-evolving-job-market/

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3. #308: AI Changes Everything! Analytical Fundamentals Still Apply!

   13.10.20261 Std. 1 Min.Folge 308Als Nächstes

   How do you know whether AI is actually making your organization better? Counting prompts, sessions, and token spend is easy. Figuring out whether any of that activity is driving business outcomes is...harder …

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   How do you know whether AI is actually making your organization better? Counting prompts, sessions, and token spend is easy. Figuring out whether any of that activity is driving business outcomes is...harder. Daniela Matinho, Marketing Science Senior Strategist at LinkedIn, wrote a Medium post on that very question, and it helped shape this discussion about what AI is (and isn't) changing about the work of the analyst. As Daniela pointed out, if you tell a client you did something in two days instead of a week because of AI, the client doesn't care. They care about the result! From the smart-but-junior agent that needs constant supervision, to the analyst's shift toward being more of a storyteller, to the unglamorous-but-essential work of data quality and governance, we covered a lot of ground. Being faster is great. Staying influential is better.

   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: https://analyticshour.io/2026/10/13/308-ai-changes-everything-analytical-fundamentals-still-apply/

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4. #307: AI Does (and Does Not) Work for Data Storytelling

   29.09.202647 Min.Folge 307Als Nächstes

   There's a version of this episode where AI replaces the data visualization expert entirely, and there's a version where it's useless without one. Neither is quite right, according to Cole Nussbaumer Knaflic of …

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   There's a version of this episode where AI replaces the data visualization expert entirely, and there's a version where it's useless without one. Neither is quite right, according to Cole Nussbaumer Knaflic of Storytelling with Data, who joined Tim, Moe, and Julie to sort out where AI genuinely earns its keep in data storytelling and where it's just producing a shinier first draft of the same shitty slide. Cole-admittedly a skeptic-turned-convert who once joked she'd retire before having to deal with any of this-walks through why the humans who benefit most from AI are the ones who already have the foundation to know when to ignore it, why "let me look at some options" beats "give me the final chart" as a prompt, and why the analog, pencil-and-paper parts of the process might be the most valuable friction in the whole workflow. Bring your own opinions on whether Claude should ever be handed "the rules" and told to just go.

   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: https://analyticshour.io/2026/09/29/307-ai-does-and-does-not-work-for-data-storytelling/

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5. #306: Decision Support Has Been the Point All Along

   15.09.202646 Min.Folge 306Als Nächstes

   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 …

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   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.

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6. #305: Personal Interest + Analytics Chops = Career?

   01.09.20261 Std. 4 Min.Folge 305Als Nächstes

   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 …

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   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.

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7. #304: I Can Haz AI?

   18.08.20261 Std. 8 Min.Als Nächstes

   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 …

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   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.

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8. #303: Funnels Assume Progress. Barriers Recognize Reality.

   04.08.20261 Std. 4 Min.Als Nächstes

   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 …

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   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.

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9. #302: It Was a Dark and Stormy Insight...

   21.07.20261 Std. 8 Min.Als Nächstes

   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 …

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   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.

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10. #301: It Turns Out Analysts Are Natural AI Crafters

    07.07.20261 Std. 17 Min.Als Nächstes

    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 …

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    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.

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11. #300: Are Semantic Layers Really Necessary?

    23.06.202658 Min.Als Nächstes

    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 …

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    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.

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12. #299: AI Can (Help) Build the Dashboard. It Can't Build the Buy-In.

    09.06.20261 Std. 1 Min.Als Nächstes

    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 …

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    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.

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13. #298: Listener Questions Answered Live from Marketing Analytics Summit!

    26.05.202652 Min.Als Nächstes

    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 …

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    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.

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14. #297: Durable Wisdom in an Age of AI Slop

    12.05.20261 Std. 6 Min.Als Nächstes

    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 …

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    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.

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15. #296: Avoiding Major Oopsies: Twyman's Law, Intuition, and Valuing Accuracy Over Precision

    28.04.20261 Std. 4 Min.Als Nächstes

    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 …

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    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 useful insight in and of itself. From the discipline of documenting assumptions to the art of knowing when to round your numbers, we tackle the very human challenge of working with data that's supposed to be objective but rarely is. Plus, we explore Twyman's Law (if data looks too good to be true, it probably is) and why sometimes your intuition is your last line of defense against embarrassing mistakes.

    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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16. #295: Research and Analytics: the Peanut Butter and Chocolate of Data?

    14.04.20261 Std. 9 Min.Als Nächstes

    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 …

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    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 from putting both methodologies to work on the same business problems. From the "never ask a survey question you already have the answer to" rule to why personas are usually terrible (spoiler: it's not the clustering, it's the storytelling), we explore how organizations can break down the silos between research and analytics teams. Turns out, the fear of the unknown and a bunch of fancy terminology might be keeping us from some pretty powerful insights. Also, apparently 100% soundproof rooms are absolutely terrifying. 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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17. #294: Adapting an Analytics Team to an AI World

    31.03.20261 Std. 8 Min.Als Nächstes

    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 …

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    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 to be in place? What incentive structures are (and are not) effective when it comes encouraging team members to take the AI plunge? One tactic that is definitely effective is to have leaders who are excited, engaged, and transparent as they get their hands dirty. And, boy, did the algorithm deliver one of those to us in the form of John Lovett, VP of Analytics at SEER Interactive, for this discussion!

    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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18. #293: Tool Selection and the Unhelpfulness of Feature Comparisons

    17.03.20261 Std. 6 Min.Als Nächstes

    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 …

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    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!) for any data platform selection, be it a digital/product analytics platform, a BI tool, database or storage infrastructure, or, well, you name it! Ultimately, the challenge is how to go about getting a reasonably strong understanding of the philosophy and historical roots of each platform being considered and then marrying that up with the foundational priorities and needs of the organization. Is that a lot harder than a feature checklist? Yes. But them's the breaks.

    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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19. #292: AI Without Adult Supervision with Aubrey Blanche

    03.03.20261 Std. 4 Min.Als Nächstes

    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 …

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    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 uniquely prepared and positioned to ask better questions, measure what really matters, and avoid letting the AI teenager run the house. Learn more about "phantom value" and why faster isn't always better… or even cheaper!

    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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20. #291: The Data Work that Lives in the Shadows

    17.02.20261 Std. 3 Min.Als Nächstes

    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 …

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    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 unheralded, but often quite important "shadow work" of the analyst—the myriad tasks required to effectively glue together all the data work that occurs out in broad daylight to enable the data to truly be useful at driving the business forward.

    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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21. #290: Always Be Learning

    03.02.20261 Std. 6 Min.Als Nächstes

    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 …

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    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 supposed to deliver. How does a brand stay ahead of their competitors? One surefire way is to get smarter about their customers at a faster rate than their competitors do. But what does that even mean? Is it a learning to discover that the MVP of a hot new feature…doesn't look to be moving the needle at all? Our guest, Mårten Schultzberg from Spotify, makes a compelling case that it is! And the co-hosts agree. But it's tricky.

    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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22. #289: The Imperative of Developing Business Acumen

    20.01.20261 Std. 10 Min.Als Nächstes

    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 …

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    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 the broader business needs. In this episode, the gang had a chat about business acumen—what it is, how to develop it, and why it's a must-have for any data or analytics role.

    This episode's Measurement Bite from show sponsor Recast is a brief explanation of identifiability—what it is and how to check for it using simulation—from Michael Kaminsky!

    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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23. #288: Our LLM Suggested We Chat about MCP. Kinda' Meta, No?

    06.01.20261 Std. 1 Min.Als Nächstes

    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 …

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    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 who like their acronyms written out, and Sam Redfern joined us to help us wrap our heads around the topic. You see, MCP is kinda' like some other more familiar acronyms like API and XML. But, it's also like… fingers? Sam's enthusiasm and explanation certainly had us ready to dive in!

    This episode's Measurement Bite from show sponsor Recast is an explanation of model robustness from Michael Kaminsky!

    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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24. #287: 2025 Year in Review

    23.12.20251 Std. 1 Min.Als Nächstes

    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 …

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    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 for Tim's book (are you still short on a few stocking stuffers? Order now…!).

    This episode's Measurement Bite from show sponsor Recast is a brief explanation of Granger causality (and how it's NOT actually a causal measure!) from Michael Kaminsky!

    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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25. #286: Metrics Layers. Data Dictionaries. Maybe It's All Semantic (Layers)? With Cindi Howson

    09.12.202556 Min.Als Nächstes

    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 …

    Shownotes

    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 concept that is doomed to fail? These questions and more get bandied about on this episode, where we were joined by industry legend Cindi Howson, Chief Data & AI Strategy Officer at Thoughtspot.

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

    This episode's Measurement Bite from show sponsor Recast is an explanation of multicollinearity from Michael Kaminsky!

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26. #285: Our Prior Is That Many Analysts Are Confounded by Bayesian Statistics

    25.11.20251 Std. 7 Min.Als Nächstes

    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 …

    Shownotes

    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 important takeaway is that you'll know you've achieved Bayesian clarity when you come to realize that human beings are naturally Bayesian, and the underlying principles behind Bayesian statistics are inherently intuitive.

    This episode's Measurement Bite from show sponsor Recast is a brief explanation of statistical significance (and why shorthanding it is problematic…and why confidence intervals are generally more practically useful in business than p-values) from Michael Kaminsky!

    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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27. #284: I Used to Think...But Not Any More

    11.11.20251 Std. 8 Min.Als Nächstes

    As the world turns, a couple of things happen: 1) we grow and learn, and 2) the world changes. On this episode, inspired by a job interview question, the hosts walked through a range of thoughts and beliefs …

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    As the world turns, a couple of things happen: 1) we grow and learn, and 2) the world changes. On this episode, inspired by a job interview question, the hosts walked through a range of thoughts and beliefs they had at one time that they no longer have today. Analytics intake forms are good…or bad? Analytics centers of excellence are the sign of a mature organization…or they're just one of many potential options? Privacy concerns are something no one really cares about…or they are something everyone cares deeply about? Voices were raised. Light profanity was employed. Laughter ensued.

    This episode's Measurement Bite from show sponsor Recast is a brief explanation of statistical significance (and why shorthanding it is problematic…and why confidence intervals are often more practically useful in business than p-values) from Michael Kaminsky.

    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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28. #283: Good Things (Can) Come in Small Datasets with Joe Domaleski

    28.10.20251 Std. 13 Min.Als Nächstes

    Does size matter? When it comes to datasets, the conventional wisdom seems to be a resounding, "Yes!" But what about small datasets? Small- and mid-sized businesses and nonprofits, especially, often have …

    Shownotes

    Does size matter? When it comes to datasets, the conventional wisdom seems to be a resounding, "Yes!" But what about small datasets? Small- and mid-sized businesses and nonprofits, especially, often have limited web traffic, small email lists, CRM systems that can comfortably operate under the free tier, and lead and order counts that don't lend themselves to "big data" descriptors. Even large enterprises have scenarios where some datasets easily fit into Google Sheets with limited scrolling required. Should this data be dismissed out of hand, or should it be treated as what it is: potentially useful? Joe Domaleski from Country Fried Creative works with a lot of businesses that are operating in the small data world, and he was so intrigued by the potential of putting data to use on behalf of his clients that he's mid-way through getting a Master's degree in Analytics from Georgia Tech! He wrote a really useful article about the ins and outs of small data, so we brought him on for a discussion on the topic!

    This episode's Measurement Bite from show sponsor Recast is an explanation of synthetic controls and how they can be used as counterfactuals from Michael Kaminsky!

    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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29. #282: Using (and Creating!) Data to Understand Pop Culture with Chris Dalla Riva

    14.10.20251 Std. 8 Min.Als Nächstes

    Data does not just magically spring into existence. Someone, somewhere, has to decide what data gets created and the rules for its creation. We would claim that this often starts as a pretty simple exercise …

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    Data does not just magically spring into existence. Someone, somewhere, has to decide what data gets created and the rules for its creation. We would claim that this often starts as a pretty simple exercise, and then, over time, that simplicity balloons to be pretty complex! What if, for instance, you decided to listen to every #1 song on the Billboard Hot 100 going back to its inception in 1958? You may start by just capturing the song name, the artist, and the week(s) it was the #1 song. But, before you know it, you may find that you're adding in artist details…and songwriter details…and producer details…and genre details…and instrumentation details, and your dataset has 105 columns! But, oh, the questions that dataset could answer! And that's exactly the dataset that our guest for this episode, Chris Dalla Riva, created. He uses it (with a range of supplemental datasets) for his pieces in his Substack, Can't Get Much Higher, as well as the underlying raw material for his upcoming book, Uncharted Territory: What Numbers Tell Us about the Biggest Hit Songs and Ourselves. While the underlying material was music, the parallels to more staid business data were many when it comes to the underlying processes and challenges for doing that work!

    This episode's Measurement Bite from show sponsor Recast is an explanation of the miracle of randomization when it comes to addressing unobserved confounders from Michael Kaminsky!

    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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30. #281: Analytics: The View from the Corner Office with Anna Lee

    30.09.20251 Std. 6 Min.Als Nächstes

    From spreadsheets to strategy: what does data look like from the CEO's chair? For this episode, we sat down with Anna Lee, CEO of Flybuys and former CFO/COO of THE ICONIC, to get her view on data-led leadership …

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    From spreadsheets to strategy: what does data look like from the CEO's chair? For this episode, we sat down with Anna Lee, CEO of Flybuys and former CFO/COO of THE ICONIC, to get her view on data-led leadership and what great looks like in data and analytics. Discover how Anna's journey from finance to the corner office has shaped her approach to leveraging evidence for strategic decision-making. From productive curiosity, to informed pragmatism, and how data teams can build trust with leadership, this is a candid conversation about analytics from the top down. Whether you're embedded in a squad or building the next big data platform, this one's for anyone who's ever wondered what it takes to truly influence the C-suite!

    This episode's Measurement Bite from show sponsor Recast is an overview of the fundamental problem of causal inference from Michael Kaminsky!

    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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