The Analytics Power Hour

#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 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.

#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 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.  

#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 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!

#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 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.

#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 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.