The Analytics Power Hour

#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, 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.

#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 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.

#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!