What does Machiavelli have to do with International Women's Day? It turns out, if you read his writings through the lens of the challenges that women face in the workplace, then quite a lot! So much, in fact, that a whole book could be written on the subject. And one was! Machiavelli for Women author Stacey Vanek Smith joins Moe and guest host Julie Hoyer for this women-only episode of the show. If you're a dude and think this show isn't for you, too, then think again! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Remember how we used to have bonus episodes? With International Women's Day coming up in a week, it seemed like a good time to bring them back. Tim is joined by Rehgan Avon, the founder of the organization that runs the DataConnect Conference, which will be June 2-3, 2022 in Columbus, Ohio (and virtually). It's a conference open to everyone to attend, but all of the speakers are women or gender minorities. Tim and Rehgan also discuss the current state of gender diversity in the profession and how it has changed since Rehgan started the organization that became this conference back in 2016. And there's a discount code! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

How do we measure the performance of this podcast? With well-formulated KPIs, of course! With targets set for them. Since Tim is the taskmaster who insists we revisit our KPIs every year, we decided he would be our guest for this show, and Michael and Moe would take turns trying to stump him with impromptu role playing as difficult stakeholders in challenging scenarios. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

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.

Hey, buddy, we've got a good tip for you: buy low, sell high! If you want a more succinct tip, then we've got one word: plastics! If you would like some ACTUAL tips that you might actually want to apply in your day-to-day (or data-to-data) work, then you will have to give this episode a listen. Back by popular demand, we took a meandering walk through some of our go-to tips ("life hacks" if you're in the Bay Area) for productivity, communication, analysis, and more! 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.

We did it! Another year in the books, and 2021 was a bit of a ride. As we do every year, on this episode we reflect a little bit on the podcast and then a lot on the industry: what the major themes of 2021 were, and what we think might be coming in 2022. Google Analytics 4, 3rd party cookies, remote work and Zoom meetings, and even the metaverse! Plus, of course, this is our annual excuse to get our executive producer, Josh Crowhurst, on a mic! 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.

One of our KPIs for the show is to keep the Topic Repeat Rate (TRR) below 1.2%. From carefully monitoring our show dashboard, we had an actionable insight: we could finally revisit episode #002. Conveniently, the topic of that show was dashboards, which explains the self-referential stemwinder of a description of this episode. That show was "a long, long time ago. We can still remember… when the dashboards used to make us smile." 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.

Stop for a minute and think about the highest stakes campaign or test you've ever run. Were you nervous? Now, instead, imagine that you're on an aircraft carrier with a few thousand people on board whose safety you are responsible for, and your team is about to watch 40,000 tons of ordnance detonate (in an environmentally friendly way) right next to the ship... so you can collect data to verify that the various systems are working as expected. On this episode, our guest can't really talk about the former situation, but he can discuss the latter in depth: Capt. Paul Lanzilotta is the commanding officer of the USS Gerald R. Ford, the lead ship in the latest class of U.S. Navy aircraft carriers. Perspective, much? For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

As analysts, it can be easy to get so focused on the data that we lose sight of the imperative that we answer meaningful questions (aka: validating relevant hypotheses). On this episode, we sat down with Wil Reynolds, co-founder and accidental lead generator for SEER Interactive, for a discussion that turned out to be about curiosity and the power of trying to prove yourself wrong (and being willing to invest the time to do so!). In the end, we concluded that Wil has always been a "data person," even if he doesn't necessarily see himself as such. That is... actually kinda' profound! 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 worked in a large organization where the data team(s) are perfectly structured to deliver efficient, harmonious, and meaningful results to the business with 'nary a gap nor a redundancy? If you answered "yes," then we'll go ahead and report you to HR for being a LIAR! From high growth startups to staid enterprises, figuring out how to organize the data and data-adjacent teams is always chock full of tradeoffs. And that's the topic of this episode. 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 thought, "you know, it would be interesting to take my analytical knowledge and just totally run an organization based on what the data says?" Yeah. Us, either. That's terrifying! But, that's exactly what our guest on this episode did. Ben Lindbergh, along with his stathead-in-crime (aka, co-author) Sam Miller, took over the management of a minor league baseball team in 2015, and the result was The Only Rule Is It Has to Work: Our Wild Experiment Building a New Kind of Baseball Team. How does that apply to analytics in the business world? In a surprising number of ways, it turns out! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Data is everywhere and it's simply not going away. Plenty of people do seem to ignore it to their peril, but if we are trying to make sense of the world, making good sense of data is absolutely critical. In business we call it data literacy, and, truthfully, it is a mandatory skill set for almost anyone. Data and understanding data might have a set of rules, and it seems like not everyone is committed to playing by those rules. Sometimes even our own brains get in on the act of hiding what the data actually means from us. And that's the subject of this episode with Financial Times columnist, BBC presenter, and Data Detective / How to Make the World Add Up author Tim Harford. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We're baaaaaaack…! Shorter show name, a rebrand, some minor formatting and structural updates, but still "Moe Kiss with a couple of guys who listeners can't keep straight." On this episode, we talk for a little bit about what we've been doing while we were on hiatus and then dive into a topic that only Cassie Kozyrkov has dared to deeply explore before: the distinction between analysts, statisticians, data engineers, ML engineers...and data charlatans. Well, really just the first two. But, Cassie('s content) has made numerous appearances on the show, so it seemed like high time that we dug into some of her ideas. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Once upon a time, there was an analyst. And that analyst had some data. She used that data to do some analysis, and from that analysis she realized she had some recommendations she could make to her organization. This was the point where our intrepid analyst reached a metaphorical fork in Communication Road: would she hastily put all of her thoughts together quickly in a slide deck with charts and graphs and bullets, or would she pause, step back, and craft a true data story? Well, if she listened to this episode of the podcast with presentation legend Nancy Duarte, author of five award-winning books (the most recent one — DataStory: Explain Data and Inspire Action Through Story — being the main focus of this episode) she would do the latter, and her story would have a happy ending indeed! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

This episode originally aired on December 17, 2019.

We can watch (sort of) what users do on our sites. That's web analytics. We can ask them how they felt about the experience. That's voice of the customer. But, can we (and should we?) actually analyze their emotional reactions? On this episode, Michael and Tim sat down with Dr. Liraz Margalit, Head of Digital Behavioral Research at Clicktale, to bend their brains a bit around that very topic. And, they left the discussion thinking differently about conversion rates, and even realizing that scroll tracking might just have a valuable application! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

This episode originally aired on June 20, 2017.

Do you model professionally? Would you like to? Or, are you uncertain. These are the topics of this episode: Bayesian statistician (among other official roles that are way less fun to say) Dr. Elea Feit joined the gang to discuss how we, as analysts, think about data put it to use. Things got pretty deep, included the exploration of questions such as, "If you run a test that includes a holdout group, is that an A/B test?" This episode ran a little long, but our confidence level is quite high that you will be totally fine with that. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. 

This episode originally aired on March 13, 2018.

What's in a job title? that which we call a senior data scientist by any other job title would model as predictively… This, dear listener, is why the hosts of this podcast crunch data rather than dabble in iambic pentameter. With sincere apologies to William Shakespeare, we sat down with Maryam Jahanshahi to discuss job titles, job descriptions, and the research, experiments, and analysis that she has conducted as a research scientist at Datapeople (formerly TapRecruit), specifically relating to data science and analytics roles. The discussion was intriguing and enlightening! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Did you hear the one about the Harvard-educated economist who embraced her inner wiring as a lateral thinker to explore topics ranging from HIV/AIDS in Africa to the impact of Hepatitis B on male-biased sex ratios in China to the range of advice and dicta doled out by doctors and parents and in-laws and friends about what to do (and not do!) during pregnancy? It's a data-driven tale if ever there was one! Emily Oster, economics professor at Brown University and bestselling author of Expecting Better and Cribsheet, joined the show to chat about what happens when the evidence (the data!) doesn't match conventional wisdom, and strategies for presenting and discussing topics where that's the case. Plus causal inference! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. 

This episode originally aired on October 22, 2019.

Is your organization customer-centric? Does your product team dive into the demographics of your customers to figure out what features will make them as happy as possible? If so, then you're doing it all wrong! Perhaps. On this episode, the gang chats with Dr. Peter Fader (@faderp) from The Wharton School and Zodiac Metrics, about putting customer lifetime value (CLV) front and center when it comes to developing and executing marketing strategies. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. This episode originally aired on August 29, 2017.