#280: Dashboards Must Die! Long Live Dashboards! with Andy Cotgreave

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.

What is "process" in analytics? On the one hand, it can be seen as a detailed sequence of minutia by which anything that needs to be repeated in the world of analytics gets carried out in a structured and consistent manner. On the other hand, that’s the sort of definition that strikes terror and rage in the hearts of many souls. Some of those souls are co-hosts of this podcast. Even the more process-oriented co-hosts bristle at such a definition (but for different reasons). So, what ARE some of the core processes in analytics? And, what is the appropriate balance between establishing a prescriptive structure and leaving sufficient flexibility to allow human judgment to adapt a process to fit specific situations? Those are the sorts of questions tackled on this episode, which was released on time with all of its underlying component parts thanks to a reasonably robust…process. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Imagine a world where business users simply fire up their analytics AI tool, ask for some insights, and get a clear and accurate response in return. That’s the dream, isn’t it? Is it just around the corner, or is it years away? Or is that vision embarrassingly misguided at its core? The very real humans who responded to our listener survey wanted to know where and how AI would be fitting into the analyst’s toolkit, and, frankly, so do we! Maybe they (and you!) can fire up ol’ Claude and ask it to analyze this episode with Juliana Jackson from the Standard Deviation podcast and Beyond the Mean Substack to find out! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Did you know that, upon closer inspection, many a statistical test will reveal that "it's just a linear model" (#IJALM)? That wound up being a key point that our go-to statistician, Chelsea Parlett-Pelleriti, made early and often on this episode, which is the next installment in our informally recurring series of shows digging into specific statistical methods. The method for this episode? ANOVA! As a jumping off point to think about how data works—developing intuition about mean and variance (and covariates) while dipping our toes into F-statistics, family-wise error rates (FWER), and even a little Tukey HSD—ANOVA’s not too shabby! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Product managers for BI platforms have it easy. They "just" need to have the dev team build a tool that gives all types of users access to all of the data they should be allowed to see in a way that is quick, simple, and clear while preventing them from pulling data that can be misinterpreted. Of course, there are a lot of different types of users—from the C-level executive who wants ready access to high-level metrics all the way to the analyst or data scientist who wants to drop into a SQL flow state to everyone in between. And sometimes the tool needs to provide structured dashboards, while at other times it needs to be a mechanism for ad hoc analysis. Maybe the product manager’s job is actually…impossible? Past Looker CAO and current Omni CEO Colin Zima joined this episode for a lively discussion on the subject! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

It’s a process few people genuinely enjoy, but it’s one which we all find ourselves going through periodically in our careers: landing a new job. We grabbed MajorData himself, Albert Bellamy, for a wide-ranging discussion about the ins and outs of that process: LinkedIn invitation etiquette (and, more importantly, effectiveness), how networking is like spousal communication (!), the usefulness of reducing the mental load required of recruiters and hiring managers, and much, much more! You might just want to drop and do twenty push-ups by the end of the episode! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

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, founder of Arima, 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.

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? 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 returned as a guest inspired by a Medium post he wrote a while back 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.

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, 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.

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 from Irrational Labs 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! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.