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.

We've been on a bit of a streak of culture and career discussions, which means we want to assure you that Tim is not actually tied up in a basement with no access to our content calendar. Actually, in this episode, Tim plopped down on the therapy couch as a vessel for the wisdom of Moe and Michael about structured techniques for analysts to chart the best paths for their careers. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Happy new year! We're not really resolution-making types, but the incrementing of the annum is a good time to take a breath and think about some ways we might want to approach our work differently. On this episode, we took a pretty big swing at "culture" — sitting down with Aaron Dignan, founder of Murmur, author of Brave New Work (and host of the eponymous podcast) — to discuss some of the ways modern organizations are, well, broken! From there, to the analysts within those organizations, to frameworks and approaches for getting to a better way of working, it was a brain-stretching way to kick off 2023! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

It's that one-time-of-the-year when we do a little bit of navel-gazing, a little bit of prognostication, and, when the year is a year like 2022, a little more cursing than usual. Not only did the podcast hit a fairly meaningless vanity metric milestone this year, but we also maintained our explicit rating! Executive producer Josh Crowhurst joined us to look back on the podcast and the analytics industry in 2022, as well as to do a little bit of crystal ball gazing into 2023 and beyond! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

You've got some solid experience under your belt, and you're starting to feel like you're ready to move into a data leadership role. What does that even mean? Shifting your keystrokes from SQL to slide decks? Maybe (but maybe not). Katie Bauer, Head of Data at GlossGenius, has held multiple data leadership roles over the course of her career, and she penned a thoughtful post on the various tactics she employed to find a role that is a good fit. She wrote the post so that she wouldn't have to keep repeating herself when data folks in her network reached out for advice. But that didn't stop this podcast from reaching out to record a lively discussion on the topic! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

As analysts, we conduct analysis on behalf of the business to (hopefully) provide them with clear and objective information to help with making decisions. We use visualizations of data and, when we're really hitting our stride, we even tell data stories. So, how does that compare to mainstream journalism and the stories they tell, especially when there is data that can be visualized in support of the story or the analysis? There could be no better guest than Philip Bump, long-time columnist for The Washington Post, author of the How to Read This Chart weekly newsletter, and author of a soon-to-be-published book about the baby boom generation! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Ethics in AI is a broad, deep, and tough subject. It's also, arguably, one of the most important subjects for analysts, data scientists, and organizations overall to deliberately and determinedly tackle as a standard part of how they do work. On this episode, Renée Cummings, Professor of Practice in Data Science and Data Activist in Residence at the University of Virginia (among many other roles), joined us for a discussion of the subject. Her knowledge of the topic is as deep as her passion for it, and both are bordering on the limitless, so it was an incredibly informative chat! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

So, you finally took that recruiter's call, and then you made it through the initial phone screen. You weren't really expecting that to happen, but now you're facing an actual interview! It sounds intense and, yet, you're not sure what to expect or how to prepare for it. Flash cards with statistical concepts? A crash course in Python? LinkedIn stalking of current employees of the company? Maybe. We asked Jay Feng from Interview Query to join us to discuss strategies and tactics for data scientists and analyst interviews, and we definitely wanted to hire him by the time we were done! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.