As the world turns, a couple of things happen: 1) we grow and learn, and 2) the world changes. On this episode, inspired by a job interview question, the hosts walked through a range of thoughts and beliefs they had at one time that they no longer have today. Analytics intake forms are good…or bad? Analytics centers of excellence are the sign of a mature organization…or they're just one of many potential options? Privacy concerns are something no one really cares about…or they are something everyone cares deeply about? Voices were raised. Light profanity was employed. Laughter ensued.

This episode's Measurement Bite from show sponsor Recast is a brief explanation of statistical significance (and why shorthanding it is problematic…and why confidence intervals are often more practically useful in business than p-values) from Michael Kaminsky.

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Does size matter? When it comes to datasets, the conventional wisdom seems to be a resounding, "Yes!" But what about small datasets? Small- and mid-sized businesses and nonprofits, especially, often have limited web traffic, small email lists, CRM systems that can comfortably operate under the free tier, and lead and order counts that don't lend themselves to "big data" descriptors. Even large enterprises have scenarios where some datasets easily fit into Google Sheets with limited scrolling required. Should this data be dismissed out of hand, or should it be treated as what it is: potentially useful? Joe Domaleski from Country Fried Creative works with a lot of businesses that are operating in the small data world, and he was so intrigued by the potential of putting data to use on behalf of his clients that he's mid-way through getting a Master's degree in Analytics from Georgia Tech! He wrote a really useful article about the ins and outs of small data, so we brought him on for a discussion on the topic!

This episode's Measurement Bite from show sponsor Recast is an explanation of synthetic controls and how they can be used as counterfactuals from Michael Kaminsky!

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Synthetic data: it's a fascinating topic that sounds like science fiction but is rapidly becoming a practical tool in the data landscape. From machine learning applications to safeguarding privacy, synthetic data offers a compelling alternative to real-world datasets that might be incomplete or unwieldy. With the help of Winston Li, founder of Arima, a startup specializing in synthetic data and marketing mix modelling, we explore how this artificial data is generated, where its strengths truly lie, and the potential pitfalls to watch out for! 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.

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.

Is it just us, or are data products becoming all the rage? Is Google Trends a data product that could help us answer that question? What actually IS a data product? And does it even matter that we have a good definition? If any of these questions seem like they have cut and dried answers, then this episode may just convince you that you haven't thought about them hard enough! After all, what is more on-brand for a group of analysts than being thrown a question that seems simple only to dig in to realize that it is more complicated than it appears at first blush? On this episode, Eric Sandosham returned as a guest inspired by a Medium post he wrote a while back so we could all dive into the topic and see what we could figure out! 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.

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.

No matter how simple a metric's name makes it sound, the details are often downright devilish. What is a website visit? What is revenue? What is a customer? Go one level deeper with a metric like customer acquisition cost (CAC) or customer lifetime value (CLV or LTV, depending on how you acronym), and things can get messy in a hurry. In some cases, there are multiple "right" definitions, depending on how the metric is being used. In some cases, there are incentive structures to thumb the definitional scale one way or another. In some cases, a hastily made choice becomes a well-established, yet misguided, norm. In some cases, public companies simply throw their hands up and stop reporting a key metric! Dan McCarthy, Associate Professor of Marketing at the Robert H. Smith School of Business at the University of Maryland, spends a lot of time and thought culling through public filings and disclosures therein trying to make sense of metric definitions, so he was a great guest to have to dig into the topic! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

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. 

Thanks for stopping by. Please get comfortable. We're going to be taking a few notes while you listen, but pay that no mind. Now, what we'd like you to do is listen to the podcast. Oh. And don't worry about that big mirror over there. There may be 2 or 3 or 10 people watching. Wow. We're terrible moderators when it comes to this sort of thing. That's why Els Aerts from AGConsult joined us to discuss user research: what it is, where it should fit in an organization's toolkit, and some tips for doing it well. 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 May 8, 2018.

Have you ever seen a one-man show in the theater? It's awesome. Unless it's terrible. The same can be said for one-person digital analytics teams. It can be awesome, in that you get to, literally, do EVERY aspect of analytics. It can be terrible because, well, you've got to do EVERYTHING, and it's easy for the fun stuff to get squeezed out of the day. On this episode, we head back Down Under for a chat with Moe Kiss, product (and digital) analyst at THE ICONIC. Whether you pronounce "data" as DAY-tuh or DAH-tuh, Moe's perspective will almost certainly motivate you find new ways to push yourself and your organization forward. 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 6, 2016.

In recognition of International Women's Day, and because it's a really important topic, this is a very special episode. The two straight, white, cisgender male co-hosts of this podcast sat this episode out, while Moe took over the mic for an in-depth discussion with Alison Vorsatz from Fairygodboss and Aubrey Blanche from Atlassian about diversity (a term they both try to avoid) in the workplace. If this episode doesn't change your perspective and compel you to action, you are almost certainly not a human being. 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 7, 2019.

As analysts, we often have unique knowledge of the data, specialized responsibilities for data-related deliverables, and an expectation that we'll be at the ready to dive into high priority requests. What happens, then, when we're out of the office, be that for a planned vacation, for an unexpected illness, or for bringing a new human being into the world? And, what happens if it's that last one and you're also the most beloved co-host of the top-rated explicit analytics podcast? Tune in to this episode to find out, as we used Moe in a dual role of being both a co-host and a guest (again!) to explore the challenges (and opportunities!) of being out of the office. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

You know that sinking feeling: the automated report went out first thing Monday morning, and your Slack messages have been blowing up ever since because revenue flatlined on Saturday afternoon! You frantically start digging in (spilling your coffee in the process!) while you're torn between hoping that it's "just a data issue" (which would be good for the company but a black mark on the data team) and that it's a "real issue with the site" (not good for the business, but at least your report was accurate!). Okay. So, maybe you've never had that exact scenario, but we've all dealt with data breakages occurring in various unexpected nooks and crannies of our data ecosystem. It can be daunting to make a business case to invest in monitoring and observing all the various data pipes and tables to proactively identify data issues. But, as our data gets broader and deeper and more business-critical, can we afford not to? On this episode, we were joined by Barr Moses, co-founder and CEO of Monte Carlo to chat about practical strategies and frameworks for monitoring data and reducing data downtime! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

As we put the awfulness of 2020 in the rearview mirror, we thought it might be fun to look back to another bleak period: the 2007-2008 financial crisis! Why? Because Tim hasn't stopped talking about Subprime Attention Crisis — the Tim Hwang book that draws a parallel between the digital advertising ecosystem and the subprime lending crisis from a decade ago — we decided to all give it a read and then sit down for a discussion with the author. From the opacity brought on by the many moving parts to misaligned incentives to the fact that, well, even more than just the internet is built on digital advertising dollars, it was a fascinating discussion! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Data that tracks what users and customers do is behavioral data. But behavioral science is much more about why humans do things and what sorts of techniques can be employed to nudge them to do something specific. On this episode, behavioral scientist Dr. Lindsay Juarez from Irrational Labs joined us for a conversation on the topic. Nudge vs. sludge, getting uncomfortably specific about the behavior of interest, and even a prompting of our guest to recreate and explain a classic Seinfeld bit! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Google bought Urchin in 2005 and, virtually overnight, made digital analytics available to all companies, no matter how large or how small. Optimizely was founded in January 2010 and had a similar (but lesser) impact on the world of A/B testing. What can we learn from ruminating on the past, the present, and the future (server-side testing! sample ratio mismatch checking! Bayesian approaches!) of experimentation? Quite a bit, if we pull in an industry veteran and pragmatic thinker like Ton Wesseling from Online Dialogue! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

As unlikely as it seemed at many times throughout the year, 2020 actually IS finally drawing to a close, and that means it's time for our annual look back on the year: what happened with the podcast, what happened with the industry, and what happened as the entire world caught fire by way of wood-fuelled, climate-assisted combustion and by virus-induced fevers. In hindsight, there were faint hints of what the rest of the year would bring when our co-hosts and producer were together in person at Superweek in late January, but exactly how upside-down the world went still took them by surprise. One thing stayed constant, though: Tim and Moe continue to be able to talk past each other and violently argue about something about which they, basically, agree. On this episode: cover letters! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

It's the holiday season and, despite Tim's 27-slide deck making a case for why we should do an Airing of Grievances-themed show, we went in another direction. On this episode, we explore a delightful tale that exists at the intersection of "Giving Back to the Community" and "Growing the Analytics Talent Pool." Rob Jackson joined the gang to be peppered with questions about the what, why, and how of his digital marketing social enterprise: WYK Digital. It's an inspiring story of breaking down some of the barriers to digital-focused jobs for underserved youth. And doing so in the middle of a pandemic, no less! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Cookies are getting aggressively expired or blocked outright. Referring site information is getting stripped. Adoption of Brave as a browser is on the rise! Yet, marketers still need to quantify the impact of their investments. What is an analyst to do? Does the answer lie in server-side technical solutions? Well, it's not a bad idea to consider that. But, it's almost certainly not "the answer" to the multi-touch attribution question(s). Arguably, a better solution was one proposed by Jan Baptist van Helmont in 1648: randomized controlled trials. On this episode, data scientist Dr. Joe Sutherland returns to the show to talk about the ins and outs of problem formulation, experimental design, the cost of data, and, ultimately, causal inference. This is one of those rare shows where there actually IS a solution to a problem that vexes analysts and their stakeholders. The trick is really just getting the industry to understand and apply the approach! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We didn't want to have a discussion about Netflix's The Social Dilemma, but, somehow, we just felt compelled to do so. It was almost like we had a generally unlikable character from a TV series about advertisers' attempts to manipulate consumer behavior in the 1950s and 1960s transplanted in triplicate into an AI that was optimizing Netflix's reach and engagement by getting us to talk about the movie. OR, it addresses a very real issue (a...dilemma, even?) in an approachable manner that, if you're like us, has alarmed your friends and relatives. It certainly seemed worth a discussion, so we had one about it! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. 

Do you know someone who works remotely? Wait. What's that? Oh. It's 2020. I guess a better question would be: do you know any analysts who are NOT working remotely? But, that's not the question we ask on this episode. Some companies—and we're thinking agencies and consultancies here just to have a little focus—were corporate office-less from their founding, and those are the sorts of companies we interrogate on this episode. Laura Stude co-founded one such company—surefoot—so we sat down with her to explore the why, the how, and the opportunities and challenges therein. Employee-led remote dumpling-making lessons, anyone? Tune in to hear a lively discussion from many angles, many (most?) of which made Tim very uncomfortable. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

What is data culture? And, more importantly, what is the optimal ratio of agar and the ideal temperature of the corporate petri dish to make a data culture thrive? Moe, Michael, and Tim put their various experiences under the organizational microscope and examined various solutions in the name of (data) scientific discovery! If only organizations were as controllable as a chemistry lab! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Do you long for the days when your mother could ask you, "Now, what do you actually do for your job?" and "all" you had to do was explain websites and digital analytics? The "analyst" is now a role that can be defined an infinite number of ways in its breadth and depth. Is the analyst who is starting to do data transformations to create clean views still an analyst? Or is she a data engineer? A data scientist? On this episode, we explore the idea of an "analytics engineer" with Claire Carroll from Fishtown Analytics who, while she did not coin the term, can certainly be credited with its growth as a concept. And there is a brief but intense spat about the role of "analytics translator," which Claire sat out, but observed with bemusement. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Did curiosity kill the cat? Perhaps. A claim could be made that a LACK of curiosity can (and should!) kill an analyst's career! On this episode, Dr. Debbie Berebichez, who, as Tim noted, sorta' pegs out on the extreme end of the curiosity spectrum, joined the show to explore the subject: the societal norms that (still!) often discourage young women from exploring and developing their curiosity; exploratory data analysis as one way to spark curiosity about a data set; the (often) misguided expectations of "the business" when it comes to analytics and data science (and the imperative to continue to promote data literacy to combat them), and more! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

How does a Bayesian tell what time it is? She starts with an estimated time as her prior and then makes a video for TikTok. If you've ever made a joke like that and then realized your audience might need a little statistical education in order to appreciate how hilarious it is (or, perhaps, what the probability is that it's hilarious), then this episode is for you. The Chatistician (and the creator of the #statstiktok hashtag), Chelsea Parlett-Pelleriti, joined the show to talk about tactics for making statistics accessible, both to ourselves and to others! Humor and thoughtfulness were both normally distributed throughout the discussion. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We finally did it: devoted an entire episode to AI. And, of course, by devoting an episode entirely to AI, we mean we just had GPT-4o generate a script for the entire show, and we just each read our parts. It's pretty impressive how the result still sounds so natural and human and spontaneous. It picked up on Tim's tendency to get hot and bothered, on Moe's proclivity for dancing right up to the edge of oversharing specific work scenarios, on Michael's knack for bringing in personality tests, on Val's patience in getting the whole discussion to get back on track, and on Julie being a real (or artificial, as the case may be?) Gem. Even though it includes the word "proclivity," this show overview was entirely generated without the assistance of AI. And yet, it's got a whopper of a hallucination: the episode wasn't scripted at all! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Once every four years in the United States, there is this thing called a "presidential election." It's a pretty boring affair, in that there is so much harmony amongst the electorate, and the two main candidates are pretty indistinguishable when it comes to their world views, policy ideas, and temperaments. But, despite the blandness of the contest, digging in to how the professionals go about forecasting the outcome is an intriguing topic. It turns out that forecasting, be it of the political or the marketing variety, is chock full of considerations like data quality, the quantification of uncertainty, and even () the opportunity to run simulations! On this episode, we sat down with G. Elliott Morris, creator of The Crosstab newsletter and a member of the political forecasting team for The Economist, to chat about the ins and outs of predicting the future with a limited set of historical data and a boatload of uncertainty. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Do you know someone who always seems to have read the latest books and can cite concepts and ideas and authors and titles in any situation? Do you hate that person? Honestly, so do we. But that didn't stop us from recording an episode that, potentially, will grate on your nerves in such a way that you have to draw on your inner grit (Grit: The Power of Passion and Perseverance by Angela Duckworth) to get through it. But, with luck, there will be some good ideas that make it into your long-term memory (Brain Rules: 12 Principles for Surviving and Thriving at Work, Home, and School by John Medina), and it will be information delivered in a gender-neutral manner, unlike so much of the world (Invisible Women: Exposing Data Bias in a World Designed for Men by Caroline Criado-Perez). Give it a shot, though. It may help you become a better leader in your organization (Dare to Lead by Brené Brown).

Unfortunately, we lost some of this episode (even our recording platform was tired of hearing about books?). We know what we talked about then, even if we have no audio record, so we've included those books in the show notes as well. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Analytics is hard (so they say... but we're not going to open THAT can of worms). Do you know what's harder? Managing analysts! I mean, they're always asking, "Why?" Sometimes, they even ask it five times! They can wind up, you know, analyzing whatever you're asking them to do! On this episode, special guest Moe Kiss (you may know her as a co-host of this podcast) joined Michael and Tim to dig into the ins and outs of the analyst/manager relationship. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.