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 actu ..read more

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 ..read more

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! Links to Articles and Other Resources Mentioned in the Show Tiffany Perkins-Munn (Article) Data Scientist: The Sexiest Job of the 21st Century by Thomas H. Davenport ..read more

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. Links to Podcasts and Other Resources Mentioned in the Show (Podcast) APH #179: Teaching Data Nerds How to Work with… People with James Hayes (Podcast) APH #208: Charting Your Path into Data Leadership with Katie B ..read more

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! APH Episodes and Other 2023 Highlights Mentioned in the Show Josh Crowhurst (Conference) SUPERWEEK (C ..read more

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 th ..read more

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 ..read more

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 topic. Her knowledge of the subject is as deep as her passion for it, and both are bordering on the limitless, so it was an incredibly informative chat! Books, Pod ..read more

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! GANs and ..read more

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 m ..read more