@AnalyticsHour

The podcast where @tgwilson, @mymo, @moemkiss, @valkroll, & @JulieMHoyer discuss the analytics issues of the day, with producing by @joshcrowhurst

analyticshour.io
Joined December 2014
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  • Following2.7K
  • Followers3.4K
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One in sixteen people collide with Excel and go "ooh." @robcolli3, from @P3Adaptive, says those people are exactly the ones who'll thrive in the AI era. We discussed why on our latest episode! analyticshour.io/2026/07/07/…

#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 building things for exactly those people

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Everyone wants to build the semantic layer. Nobody wants to maintain it. @matsonj joined us for episode 300 to ask: do we even need one? analyticshour.io/2026/06/23/…

#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—...

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AI can build the dashboard. It cannot build the buy-in. Yehonatan Schwarzmer joined us to talk change management, resistance, and why the town hall + FAQ email isn't cutting it. analyticshour.io/2026/06/09/…

#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 don’t really work. Yehonatan Schwarzmer joins us on...

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Listeners' questions, our answers, live audience reactions. We covered AI skepticism, data trust issues, and why efficiency metrics make us twitch. analyticshour.io/2026/05/26/…

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

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,...

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Trust is hard to build and easy to break. Outcomes vs outputs. The magic of randomization. Just a few gems (NOT the Gemini kind) from our co-hosts-only episode about analytics wisdom that never goes out of style. analyticshour.io/2026/05/12/…

#297: Durable Wisdom in an Age of AI Slop

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...

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"Far better an approximate answer to the right question than an exact answer to the wrong question." So sayeth John Tukey! @arikf breaks down why analysts often need to value accuracy over precision, trust their intuition, and more! analyticshour.io/2026/04/28/…

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

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 research teams and analytics teams have in common? More than they think… and less collaboration than they should. Stefanie Zammit from @BangOlufsen joined us to dig into why! analyticshour.io/2026/04/14/…

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

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...

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Love it or hate it, AI is a must-adopt technology. As an organization, what are some effective ways to motivate analysts and other data workers to drive that adoption? John Lovett from @seerinteractive joined us to discuss! analyticshour.io/2026/03/31/…

#294: Adapting an Analytics Team to an AI World

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...

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In theory, every data tool in our totally mod data stack is the "best" tool. Is the best way to figure out which one that is would be to do an exhaustive feature comparison, right? Meh. Not really. Jason Packer joined us to chat about better approaches. analyticshour.io/2026/03/17/…

#293: Tool Selection and the Unhelpfulness of Feature Comparisons

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...

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As Kevin McCallister warned us: just because the house is standing doesn’t mean it’s under control. 🏠🤖 Our International Women’s Day episode features Aubrey Blanche on AI hype, agency, and phantom value. analyticshour.io/2026/03/03/…

#292: AI Without Adult Supervision with Aubrey Blanche

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...

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What's the data work that happens in the metaphorical dark alleys of an organization? And, should some of that work be brought out into the light of day? That's what we tackled on our latest episode! analyticshour.io/2026/02/17/…

#291: The Data Work that Lives in the Shadows

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....

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If an experiment or an analysis turns up "nothing," was it still an experiment or analysis worth doing? If it's considered through a robust lens of "learning," then it might be! Mårten Schultzberg from Spotify joined us to discuss! analyticshour.io/2026/02/03/…

#290: Always Be Learning

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,...

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An analysis without business context is like an Oreo without filling. An analyst without business acumen is like a hamburger with no bun. And that's the topic of our latest episode. analyticshour.io/2026/01/20/…

#289: The Imperative of Developing Business Acumen

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...

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MCP is where it's AT. Right? For now, at least, it's a standard (sort of) that helps connect deterministic and non-deterministic systems. Maybe? @sredfern joined us to chat try to bring some clarity to the subject! analyticshour.io/2026/01/06/…

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

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...

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Don't look now, folks, but the first quarter of the 21st century is almost in the books! And when a new year looms, we look back (and a little bit ahead)! At least for the last 12 months. analyticshour.io/2025/12/23/…

#287: 2025 Year in Review

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...

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Have you rolled out semantic layers across all of your platforms yet? WELL, WHY NOT!!! They're the hottest thing (AI excepted)! @BIScorecard from @thoughtspot joined us to break 'em down! analyticshour.io/2025/12/09/…

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

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...

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Do you think of Bayesian methods as being a relatively new phenomenon? If so, this episode with @Mike_Kaminsky will definitely get you to update your priors! analyticshour.io/2025/11/25/…

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

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...

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One sign of a good analyst, arguably, is that they're willing to update their priors when new information (or their personal develop) suggests that it's prudent. We recorded a whole episode about that! analyticshour.io/2025/11/11/…

#284: I Used to Think…But Not Any More

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...

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As the business world has developed a zombie-like obsession with the neverending pursuit of big data, we decided it would be useful to take a shallow dive into small data with @joedom from @countryfriedc! analyticshour.io/2025/10/28/…

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

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...

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Have you ever created your own dataset? Have you ever done it by listening to 1,100+ songs? @cdallarivamusic has, and our chat with him about the what, why, and how was a lot of fun. Plus, it had lots of useful parallels to the analyst's day-to-day work! analyticshour.io/2025/10/14/…

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

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...

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