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.

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.

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.

Have you ever built a data-related "thing" — a dashboard, a data catalog, an experimentation platform, even — only to find that, rather than having the masses race to adopt it and use it on a daily basis, it gets an initial surge in usage… and then quietly dies? That's sorta' the topic of this episode. Except that's a pretty clunky and overly narrow summary. Partly, because it's a hard topic to summarize. But, data as a product and data products are the topic, and Eric Weber, the data scientist behind the From Data to Product newsletter, joined us for a discussion that we've been trying to make happen for months. It was worth the wait! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Do analysts make things more complicated than they need to be, or is the data representing a complex world, so that is just the nature of the beast? Or is it both? Stakeholders yearn for simple answers to simple questions, but the road to delivering meaningful results seems paved with potholes of statistical complexity, data nuances, and messy tooling. What is a business to do? Frederik Werner from DHL joined Michael and Tim for a discussion that definitively determined that, well, the topic is…complicated! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Here at the Analytics Power Hour, we have a very clear delineation of who owns what when it comes to the show production. And ownership is the topic of this episode. It's possible that the owner of the episode description feels like this is an awfully touchy-feely topic, but said owner also knows that teamwork means going along with the majority when it comes to show topics. I guess that's joint ownership? Can that work? Sadly, that, specifically, was not discussed, but the show definitely earned its explicit rating with this episode! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Our podcast junkie co-host heard the following statement on another podcast a while back when he was out for a jog: "I actually think the word 'uncertainty' is used in English in a very different way than the word 'uncertainty' is used in statistics." He almost ran into a tree (causation is unclear: he's not known for his gross motor skills, which may have been a confounder). Not only is that quote, essentially, the theme for this episode, but the person who said it, Dr. Rebecca Goldin from George Mason University, was our guest! And we are absolutely CERTAIN that it was every bit as enlightening a discussion as it was a fun one! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We try not to navel gaze too much on this show, but our 200th episode felt like just enough of a milestone that we could do a mid-year "look back, look forward" show with a 7-year range. And we tracked down our original Commonwealth representative to join us for that discussion. Did we (first) party (cookie) like it was 1999? Maybe not, but that's the sort of reference you get with Jim Cain, the founder of Napkyn Analytics, and a co-founder of this very podcast! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Multi-touch attribution, media mix modeling, matched market testing. Are these the three Ms of marketing measurement (Egad! The alliteration continues!)? Seriously. What's with all the Ms here? Has anyone ever used experimentation to build a diminishing return curve for the impact of a media measurement technique based on how far along in the alphabet the letter of that technique is? Is "M" optimal?! Trust us. You will look back on this description after listening to this episode with John Wallace from LiftLab and find it… at least mildly amusing. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We've always said that the genesis of this podcast was the lobby bar of analytics conferences across multiple continents, and this year's Marketing Analytics Summit in Las Vegas was a reminder of our roots on that front. All three co-hosts made the trip to Caesars Palace for the event. Moe presented on bringing a product mindset to analytics (by "presented on," we mean "workshopped content for a future podcast episode"), and the closing keynote was a recording of the show in front of a live (and thoughtful and engaged) audience. Give it a listen, and it will almost be like you were there! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.