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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.

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.

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.

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.

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.

If you didn't have a visceral reaction to the title for this episode, then you are almost certainly not in our target audience. There are few more certain ways to get a room full of analytics folk fired up than to raise the topic of dashboards. Are they where data goes to die, or are they the essential key to unlocking self-service access to actionable insights? Are they both? Is the question irrelevant, because, if they exist to inform business users, aren't they soon going to be replaced by an AI-powered chatbot, anyway? We thought a great way to dig into the topic (and, BTW, we were right) would be to have someone on the show who has co-penned multiple books on the topic. As luck would have it, Andy Cotgreave, one of the co-authors of both 2017's The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios and the imminently releasing  Dashboards That Deliver: How to Design, Develop, and Deploy Dashboards That Work agreed to join us for a lively chat on the topic! This episode's Measurement Bite from show sponsor Recast is a quick explanation of power analysis 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.