With so many types of companies to work for, and analysts being in high demand, we're at a point where many of us find ourselves in the enviable position of being able to pick which company — and which type of company — we want to work for. Oh, bother! That means… we have to choose! In-house? Consultancy? Agency? Product? What's the "best" option? If you already know the answer is, "It depends," then you just might be the perfect fit for consulting! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Mistakes happen. In healthy work environments, not only is that fact acknowledged, it's recognized as an opportunity to learn. That's something JD Long has been thinking about quite a bit over the past few years, and he joined the show for a chat about psychological safety: what it is, why it's important, and different techniques for engendering it. Michael trolled Tim almost immediately, which is: 1) ironic, and 2) slated to be addressed in a blameless post-mortem. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Some would say that, given the breadth and depth of data that is available to businesses these days, a surefire path to business value is to load up a department with smart data scientists, task them with developing a solid machine learning strategy, and then execute that strategy. The people who've said that might take issue with this episode. Cassie Kozyrkov joined the show to discuss decision-making: what it is, how we often frame decisions too narrowly, and the different roles data can play to support the process. And much, much more! 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 podcast episode. That's WHAT it is. But… WHY should you listen to it? Exactly. Or, perhaps, that's exactly WHY! Are you confused? You won't be after checking out our discussion with Jenni Bruckman about the vast and varied world of qualitative research and how it is the perfect partner to quantitative data. Give it a listen, and then let us know WHY you did and WHAT you thought of it! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Hey there, mister. That's a mighty nice multi-touch attribution model you're using there. It would be a shame to see it get mixed up with a media model. Or... would it? What happens if you think about media mix models as a tool that can be combined with experimentation to responsibly measure the incrementality of your marketing (while also still finding a crust of bread in the corner for so-called "click attribution")? According to a 2019 paper published by ThirdLove (which happens to have been Michael's last call on our last episode), that's a pretty nice way to go, and we thought it would be fun to see if we could raise Tim's blood pressure by giving him something to vigorously agree with for once. It was. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

What does neuroscience have to do with the work of the analyst? It turns out that neuroplasticity is to the modern analyst what plastics were to Benjamin Braddock, and it all comes down to Hebb's Law. Or, put another way, successfully working with peers and stakeholders can take some focused effort, some feedback, and some practice, and that's what "coach" James Hayes joined the episode 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 is a system without empathy? What is a show summary without an attempt to overly distill the discussion to the point of sounding like nonsense? On this episode, Hilary Parker (who you may know from the Not So Standard Deviations podcast or elsewhere) joined us to discuss what we can learn from the design process (as in: actual designers) when it comes to analytics and data science. Among other things, that mindset highlights the importance of the analyst empathizing with stakeholders. Tim got very uncomfortable. Michael said he understood Tim's discomfort. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.