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.

When it comes to simulation, we're all really asking the same question: are we living in one? Alas! We did not tackle that on this episode. Instead, with Julie Hoyer as a guest co-host while Moe is on leave, we were joined by Frances Sneddon, the CTO of Simul8, to dig into some of the nuts and bolts of simulation as a tool for improving processes. It turns out that effectively putting simulations to use means focusing on some of the same foundational aspects of effectively using analytics, data science, or experimentation: clearly defining the problem, tapping into the domain experts to actually understand the process or scenario of focus, and applying some level of "art" to complement the science of the work! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Do you listen to this podcast because you're pretty sure that you're a professional fraud, and you're hoping-hoping-hoping that you will absorb enough knowledge to stay ahead of being exposed as such? Well, stop that negative self-talk! Impostor syndrome is a very real thing, and we've devoted a whole show to digging into it! Julie Hoyer and Val Kroll joined Moe on this International Women's Day episode to discuss the topic. It turns out that there has been a lot of research in the area, there ARE techniques for battling it, and it IS useful to hear how common it is! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

When it comes to data, there are data consumers (analysts, builders and users of data products, and various other business stakeholders) and data producers (software engineers and various adjacent roles and systems). It's all too common for data producers to "break" the data as they add new features and functionality to systems as they focus on the operational processes the system supports and not the data that those processes spawn. How can this be avoided? One approach is to implement "data contracts." What that actually means… is the subject of this episode, which Shane Murray from Monte Carlo joined us to discuss! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

What's more sexy: analytics or innovation? What about combining them! That sounds great, and Thomas Davenport would be so proud if you pulled it off, but the reality is that the idea of innovation through analytics is one thing, while the reality of making it happen is another thing entirely. Dr. Tiffany Perkins-Munn, Head of Marketing Data & Analytics at JPMorgan Chase & Co., joined us for a 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.