The Analytics Power Hour

Synthetic data: it's a fascinating topic that sounds like science fiction but is rapidly becoming a practical tool in the data landscape. From machine learning applications to safeguarding privacy, synthetic data offers a compelling alternative to real-world datasets that might be incomplete or unwieldy. With the help of Winston Li [https://www.linkedin.com/in/winstonl/], founder of Arima [https://www.arimadata.com], a startup specializing in synthetic data and marketing mix modelling, we explore how this artificial data is generated, where its strengths truly lie, and the potential pitfalls to watch out for! 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/2025/06/24/274-real-talk-about-synthetic-data-with-winston-li/].

Is it just us, or are data products becoming all the rage? Is Google Trends a data product that could help us answer that question [https://trends.google.com/trends/explore?date=all&geo=US&q=data%20product&hl=en]? What actually IS a data product? And does it even matter that we have a good definition? If any of these questions seem like they have cut and dried answers, then this episode may just convince you that you haven't thought about them hard enough! After all, what is more on-brand for a group of analysts than being thrown a question that seems simple only to dig in to realize that it is more complicated than it appears at first blush? On this episode, Eric Sandosham [https://www.linkedin.com/in/eric-sandosham-ph-d-5699a51/] returned as a guest inspired by a Medium post he wrote a while back [https://eric-sandosham.medium.com/pivoting-to-data-products-95b467ef0a79] so we could all dive into the topic and see what we could figure out! 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/2025/06/10/273-data-products-are-assets-platforms-warehouses-infrastructure-oh-dear-with-eric-sandosham/].

No matter how simple a metric's name makes it sound, the details are often downright devilish. What is a website visit? What is revenue? What is a customer? Go one level deeper with a metric like customer acquisition cost (CAC) or customer lifetime value (CLV or LTV, depending on how you acronym), and things can get messy in a hurry. In some cases, there are multiple "right" definitions, depending on how the metric is being used. In some cases, there are incentive structures to thumb the definitional scale one way or another. In some cases, a hastily made choice becomes a well-established, yet misguided, norm. In some cases, public companies simply throw their hands up and stop reporting a key metric! Dan McCarthy [https://www.linkedin.com/in/danielmcc/], Associate Professor of Marketing at the Robert H. Smith School of Business at the University of Maryland, spends a lot of time and thought culling through public filings and disclosures therein trying to make sense of metric definitions, so he was a great guest to have to dig into the topic! 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/2025/05/27/272-when-the-metric-is-calculated-and-complex-with-dan-mccarthy/].

Data that tracks what users and customers do is behavioral data. But behavioral science is much more about why humans do things and what sorts of techniques can be employed to nudge them to do something specific. On this episode, behavioral scientist Dr. Lindsay Juarez [https://www.linkedin.com/in/lindsay-juarez-10b100a9/] from Irrational Labs [https://irrationallabs.com/] joined us for a conversation on the topic. Nudge vs. sludge, getting uncomfortably specific about the behavior of interest, and even a prompting of our guest to recreate and explain a classic Seinfeld bit [https://www.youtube.com/watch?v=2SdTn7ZY_1A]! 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/2025/05/13/271-it-might-be-irrational-but-lets-talk-behavioral-science-with-lindsay-juarez/].

We finally did it: devoted an entire episode to AI. And, of course, by devoting an episode entirely to AI, we mean we just had GPT-4o generate a script for the entire show, and we just each read our parts. It's pretty impressive how the result still sounds so natural and human and spontaneous. It picked up on Tim's tendency to get hot and bothered, on Moe's proclivity for dancing right up to the edge of oversharing specific work scenarios, on Michael's knack for bringing in personality tests, on Val's patience in getting the whole discussion to get back on track, and on Julie being a real (or artificial, as the case may be?) Gem. Even though it includes the word "proclivity," this show overview was entirely generated without the assistance of AI. And yet, it’s got a whopper of a hallucination: the episode wasn’t scripted at all! 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/2025/04/29/270-ai-and-the-analyst-weve-got-it-all-figured-out/].