#305: Personal Interest + Analytics Chops = Career?

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/

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/

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/

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/

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.

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.

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.

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.

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.

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.