While we don’t often call it out explicitly, the driving force behind much of what and how much data we collect is driven by a "just in case" mentality: we don't know exactly HOW that next piece of data will be put to use, but we better collect it to minimize the potential for future regret about NOT collecting it. Data collection is an optionality play—we strive to capture "all the data" so that we have as many potential options as possible for how it gets crunched somewhere down the road. On this episode, we explored the many ways this deeply ingrained and longstanding mindset is problematic, and we were joined by the inimitable Matt Gershoff from Conductrics for the discussion! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Broadly writ, we’re all in the business of data work in some form, right? It’s almost like we’re all swimming around in a big data lake, and our peers are swimming around it, too, and so are our business partners. There might be some HiPPOs and some SLOTHs splashing around in the shallow end, and the contours of the lake keep changing. Is lifeguarding…or writing SQL…or prompt engineering to get AI to write SQL…or identifying business problems a job or a skill? Does it matter? Aren’t we all just trying to get to the Insights Water Slide? Katie Bauer, Head of Data at Gloss Genius and thought-provoker at Wrong But Useful, joined Michael, Julie, and Val for a much less metaphorically tortured exploration of the ever-shifting landscape in which the modern data professional operates. Or swims. Or sinks? For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We're seeing the title "Analytics Engineer" continue to rise, and it’s in large part due to individuals realizing that there's a name for the type of work they've found themselves doing more and more. In today's landscape, there's truly a need for someone with some Data Engineering chops with an eye towards business use cases. We were fortunate to have the one of the co-authors of The Fundamentals of Analytics Engineering, Dumky de Wilde, join us to discuss the ins and outs of this popular role! Listen in to hear more about the skills and responsibilities of this role, some fun analogies to help explain to your grandma what AE's do, and even tips for individuals in this role for how they can communicate the value and impact of their work to senior leadership! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

A claim: in the world of business analytics, the default/primary source of data is real world data collected through some form of observation or tracking. Occasionally, when the stakes are sufficiently high and we need stronger evidence, we'll run some form of controlled experiment, like an A/B test. Contrast that with the world of healthcare, where the default source of data for determining a treatment's safety and efficacy is a randomized controlled trial (RCT), and it's only been relatively recently that real world data (RWD) -- data available outside of a rigorously controlled experiment -- has begun to be seen as a useful complement. On this episode, medical statistician Lewis Carpenter, Director of Real World Evidence (there's an acronym for that, too: RWE!) at Arcturis, joined Tim, Julie, and Val for a fascinating compare and contrast and caveating of RWD vs. RCTs in a medical setting and, consequently, what horizons that could broaden for the analyst working in more of a business analytics role. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

How good are humans at distinguishing between human-generated thoughts and AI-generated…thoughts? Could doing an extremely unscientific exploration of the question also generate some useful discussion? We decided to dig in and find out with a show recorded in front of a live audience at Marketing Analytics Summit in Phoenix! With Michael in the role of Peter Sagal, Julie, Tim, and Val went head-to-GPU by answering a range of analytics-oriented questions. Two co-hosts delivered their own answers, and one co-host delivered ChatGPT's, and the audience had to figure out which was which. Plus, a bit of audience Q&A, which included Michael channeling his inner Charlie Day! This episode also features the walk-on music that was written and performed live by Josh Silverbauer (no relation to Josh Crowhurst, the producer of this very podcast who also wrote and recorded the show's standard intro music; what is it about guys named Josh?!). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Application Programming Interfaces (APIs) are as pervasive as they are critical to the functioning of the modern world. That personalized and content-rich product page with a sub-second load time on Amazon? That's just a couple-hundred API calls working their magic. Every experience on your mobile device? Loaded with APIs. But, just because they're everywhere doesn't mean that they spring forth naturally from the keystrokes of a developer. There's a lot more going on that requires real thought and planning, and the boisterous arrival of AI to mainstream modernity has made the role of APIs and their underlying infrastructure even more critical. On this episode, Moe, Julie, and Tim dug into the fascinating world with API Maven Marco Palladino, the co-founder and CTO at Kong, Inc. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Professional development is a big topic—way more than just thinking about what job you want in five years and setting milestones along the way. Thankfully we had Helen Crossley, Senior Director of Marketing Science at Meta, join Michael, Moe, and Val to dive deep into this topic! We explored how to set really good, meaningful goals, the challenges across each stage from junior analyst to leader, and how to give great feedback. We also spent quite a bit of time discussing the new challenges that becoming a first-time manager presents and, hopefully, some helpful tips and thought exercises to help out our listeners who are or are about to be faced with this challenge. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

From running a controlled experiment to running a linear regression. From eyeballing a line chart to calculating the correlation of first differences. From performing a cluster analysis because that’s what the business partner asked for to gently probing for details on the underlying business question before agreeing to an approach. There are countless analytical methodologies available to the analyst, but which one is best for any given situation? Simon Jackson from Hypergrowth Data joined Moe, Julie, and Tim on the latest episode to try to get some clarity on the topic. We haven’t figured out which methodology to use to analyze whether we succeeded, so you’ll just have to listen and judge for yourself. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.