This topic was such a big deal that we managed to have no guests, and yet we had five people on the mic! Why? Because this episode doubles as a marker of a shift in the show itself. Beyond that, though, we had a lively discussion about how every business stakeholder professes to being committed to being data driven. That should make every stakeholder super easy to work with, right? And, yet, analysts often find themselves struggling to get on the same page with their counterparts due to the realities of the data: what it can and can't do and how it is most effectively worked with. Not a small topic! There were even pop quizzes (feel free to let us know how you'd score the answers)! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

On the one hand, analysts generally know and accept that part of their responsibility is to not only conduct analyses, but to effectively communicate the results of those analyses to their stakeholders. On the other hand, "communication" can feel like a pretty squishy and nebulous skill. On this episode, Michael, Moe, and Tim tackled that nebulosity (side note: using obscure words is generally not an effective communication tactic). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

To trust something, you need to understand it. And, to understand something, someone often has to explain it. When it comes to AI, explainability can be a real challenge (definitionally, a "black box" is unexplainable)! With AI getting new levels of press and prominence thanks to the explosion of generative AI platforms, the need for explainability continues to grow. But, it's just as important in more conventional situations. Dr. Janet Bastiman, the Chief Data Scientist at Napier, joined Moe and Tim to, well, explain the topic! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

There comes a time in every analyst's career where they consider starting up their own consultancy. Or, if not that, then at least joining an agency or a consultancy. The nature of most businesses is to grow, and with growth comes the potential for an "exit." This episode dives into that world in an attempt to demystify some of the ins and outs of the acquisition of analytics consultancies, from the owners' perspectives, employees' perspectives, and acquiring companies' perspectives. Since these are all perspectives that none of your dear co-hosts really have, Bob Morris, the co-founder and managing partner for Bravery Group, joined us for a discussion of EBITDA, TTM, CIMs, and even aspects of the space that are not captured by acronyms! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

What causes us to keep returning to the topic of causal inference on this show? DAG if we know! Whether or not you're familiar with directed acyclic graphs (or… DAGs) in the context of causal inference, this episode is likely for you! DJ Rich, a data scientist at Lyft, joined us to discuss causality — why it matters, why it's tricky, and what happens when you tackle causally modelling the complexity of a large-scale, two-sided market! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Data gets accessed and used in an organization through a variety of different tools (be they built, bought, or both). That work can be quick and smooth, or it can be tedious and time-consuming. What can make the difference, in modernspeak, is the specifics of the "data products" and "data platforms" being used for those tasks. Those specifics, in turn, often fall on the shoulders of (data) product managers! In this episode, Austin Byrne, Group Product Lead for Data at Canva, joined us for a discussion about the similarities and differences between typical product management and data product management! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

There are only so many hours in a day and only so many days in a year. Logically, then, the best way to grow a career as a data worker is to spend as many hours as possible doing focused data work, right? Well… probably not. In this episode, we dove into generalization versus specialization — what does that even mean, and how should we think about balancing between the two, and how can interests and activities outside of the data work itself actually make us better analysts? Bonus activity: listen for the hosts' overt trolling of Tim to see if they can get him to come off mute in his role as associate producer for the episode. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Do you ever feel like the experiments and analyses you're working on feel a little bit like a trip on a hamster wheel — properly grounded in hypotheses, perhaps, but not necessarily moving the business forward like you'd hoped? On this episode, Matty Wishnow, the author of Listening for Growth: What Startups Need the Most but Hear the Least, joined Moe, Tim, and Val for a discussion about why that may be, and how reframing the work to focus first and foremost on identifying problems (and unmet opportunities) can be useful! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Are you already inwardly groaning a little bit because our latest episode is all about privacy? Yeah. We know. We've been tracking your emotions, along with your first name, your last name, your birthdate, your government ID number, and your household income for the past ten years. Actually, we just bought that last one (but good for you on the career growth front!). Okay. You know we're just joshing you (which makes sense, since producer Josh Crowhurst stepped in as a guest co-host on this episode), and you know that because you trust us! And THAT'S quite the rambling setup for our discussion with Jodi Daniels, the founder and CEO of Red Clover Advisors and co-author of Data Reimagined: Building Trust One Byte at a Time. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

How does one build a strong culture of experimentation at an organization (and what does that even mean)? One way is to spend a few years working at a company that already has such a culture… and then jump ship to another organization that is well on its way! That's (sort of) what our guest, Lukas Vermeer, did when he left booking.com to go to Vista. With Val Kroll guest-co-hosting, we dug into the challenges — organizational, educational, and mindset-al (?) — when it comes to having an organization successfully and appropriately integrate experimentation into their operational ways. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.