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.

From spreadsheets to strategy: what does data look like from the CEO's chair? For this episode, we sat down with Anna Lee, CEO of Flybuys and former CFO/COO of THE ICONIC, to get her view on data-led leadership and what great looks like in data and analytics. Discover how Anna's journey from finance to the corner office has shaped her approach to leveraging evidence for strategic decision-making. From productive curiosity, to informed pragmatism, and how data teams can build trust with leadership, this is a candid conversation about analytics from the top down. Whether you're embedded in a squad or building the next big data platform, this one's for anyone who's ever wondered what it takes to truly influence the C-suite! This episode's Measurement Bite from show sponsor Recast is an overview of the fundamental problem of causal inference 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.

If you didn't have a visceral reaction to the title for this episode, then you are almost certainly not in our target audience. There are few more certain ways to get a room full of analytics folk fired up than to raise the topic of dashboards. Are they where data goes to die, or are they the essential key to unlocking self-service access to actionable insights? Are they both? Is the question irrelevant, because, if they exist to inform business users, aren't they soon going to be replaced by an AI-powered chatbot, anyway? We thought a great way to dig into the topic (and, BTW, we were right) would be to have someone on the show who has co-penned multiple books on the topic. As luck would have it, Andy Cotgreave, one of the co-authors of both 2017's The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios and the imminently releasing  Dashboards That Deliver: How to Design, Develop, and Deploy Dashboards That Work agreed to join us for a lively chat on the topic! This episode's Measurement Bite from show sponsor Recast is a quick explanation of power analysis 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.

What is "process" in analytics? On the one hand, it can be seen as a detailed sequence of minutia by which anything that needs to be repeated in the world of analytics gets carried out in a structured and consistent manner. On the other hand, that's the sort of definition that strikes terror and rage in the hearts of many souls. Some of those souls are co-hosts of this podcast. Even the more process-oriented co-hosts bristle at such a definition (but for different reasons). So, what ARE some of the core processes in analytics? And, what is the appropriate balance between establishing a prescriptive structure and leaving sufficient flexibility to allow human judgment to adapt a process to fit specific situations? Those are the sorts of questions tackled on this episode, which was released on time with all of its underlying component parts thanks to a reasonably robust…process. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Imagine a world where business users simply fire up their analytics AI tool, ask for some insights, and get a clear and accurate response in return. That's the dream, isn't it? Is it just around the corner, or is it years away? Or is that vision embarrassingly misguided at its core? The very real humans who responded to our listener survey wanted to know where and how AI would be fitting into the analyst's toolkit, and, frankly, so do we! Maybe they (and you!) can fire up ol' Claude and ask it to analyze this episode with Juliana Jackson from the Standard Deviation podcast and Beyond the Mean Substack to find out!

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

Do you cringe at the mere mention of the word, "insights"? What about its fancier cousin, "actionable insights"? We do, too. As a matter of fact, on this episode, we discovered that Moe has developed an uncontrollable reflex: any time she utters the word, her hands shoot up uncontrolled to form air quotes. Alas! Our podcast is an audio medium! What about those poor souls who got hired into an "Insights & Analytics" team within their company? Egad! Nonetheless, inspired by an email exchange with a listener, we took a run at the subject with Chris Kocek, CEO of Gallant Branding, who both wrote a book and hosts a podcast on the topic of insights! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

In celebration of International Women's Day, this episode of Analytics Power Hour features an all-female crew discussing the challenges and opportunities in AI projects. Moe Kiss, Julie Hoyer and Val Kroll, dive into this AI topic with guest expert, Kathleen Walch, who co-developed the CPMAI methodology and the seven patterns of AI (super helpful for your AI use cases!). Kathleen has helpful frameworks and colorful examples to illustrate the importance of setting expectations upfront with all stakeholders and clearly defining what problem you are trying to solve. Her stories are born from the painful experiences of AI projects being run like application development projects instead of the data projects that they are! Tune in to hear her advice for getting your organization to adopt a data-centric methodology for running your AI projects—you'll be happier than a camera spotting wolves in the snow! 🐺❄️🎥 For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Every so often, one of the co-hosts of this podcast co-authors a book. And by "every so often" we mean "it's happened once so far." Tim, along with (multi-)past guest Dr. Joe Sutherland, just published Analytics the Right Way: A Business Leader's Guide to Putting Data to Productive Use, and we got to sit them down for a chat about it! From misconceptions about data to the potential outcomes framework to economists as the butt of a joke about the absolute objectivity of data (spoiler: data is not objective), we covered a lot of ground. Even accounting for our (understandable) bias on the matter, we thought the book was a great read, and we think this discussion about some of the highlights will have you agreeing! Order now before it sells out! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Ten years ago, on a cold dark night, a podcast was started, 'neath the pale moonlight. There were few there to see (or listen), but they all agreed that the show that was started looked a lot like we. And here we are a decade later with a diverse group of backgrounds, perspectives, and musical tastes (see the lyrics for "Long Black Veil" if you missed the reference in the opening of this episode description) still nattering on about analytics topics of the day. It's our annual tradition of looking back on the year, albeit with a bit of a twist in the format for 2024: we took a few swings at identifying some of the best ideas, work, and content that we'd come across over the course of the year. Heated exchanges ensued, but so did some laughs! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Data storytelling is a perpetually hot topic in analytics and data science. It's easy to say, and it feels pretty easy to understand, but it's quite difficult to consistently do well. As our guest, Duncan Clark, co-founder and CEO of Flourish and Head of Europe for Canva, described it, there's a difference between "communicating" and "understanding" (or, as Moe put it, there's a difference between "explaining" and "exploring"). Data storytelling is all about the former, and it requires hard work and practice: being crystal clear as to why your audience should care about the information, being able boil the story down to a single sentence (and then expand from there), and crafting a narrative that is much, much more than an accelerated journey through the path the analyst took with the data. Give it a listen and then live happily ever after! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

For the first time since they've been a party of five, all of the Analytics Power Hour co-hosts assembled in the same location. That location? The Windy City. The occasion? Chicago's first ever MeasureCamp! The crew was busy throughout the day inviting attendees to "hop on the mic" with them to answer various questions. We covered everything from favorite interview questions to tips and tricks, with some #hottake questions thrown in for fun. During the happy hour at the end of the day, we also recorded a brief live show, which highlighted some of the hosts' favorite moments from the day. Listen carefully and you'll catch an audio cameo from Tim's wife, Julie! And keep an eye on the MeasureCamp website to find the coolest way to spend a nerdy Saturday near you (Bratislava, Sydney, Dubai, Stockholm, Brussels, and Istanbul are all coming up before the end of the year!). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

To data analyst, or to data science? To individually contribute, or to manage the individual contributions of others? To mid-career pivot into analytics, or to… oh, hell yes! That last one isn't really a choice, is it? At least, not for listeners who are drawn to this podcast. And this episode is a show that can be directly attributed to listeners. As we gathered feedback in our recent listener survey, we asked for topic suggestions, and a neat little set of those suggestions were all centered around career development. And thus, a show was born! All five co-hosts—Julie, Michael, Moe, Tim, and Val—hopped on the mic to collaborate on some answers in this episode. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We're seeing the title "Analytics Engineer" continue to rise, and it's in large part due to individuals realizing that there's a name for the type of work they've found themselves doing more and more. In today's landscape, there's truly a need for someone with some Data Engineering chops with an eye towards business use cases. We were fortunate to have the one of the co-authors of The Fundamentals of Analytics Engineering, Dumky de Wilde, join us to discuss the ins and outs of this popular role! Listen in to hear more about the skills and responsibilities of this role, some fun analogies to help explain to your grandma what AE's do, and even tips for individuals in this role for how they can communicate the value and impact of their work to senior leadership! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

A claim: in the world of business analytics, the default/primary source of data is real world data collected through some form of observation or tracking. Occasionally, when the stakes are sufficiently high and we need stronger evidence, we'll run some form of controlled experiment, like an A/B test. Contrast that with the world of healthcare, where the default source of data for determining a treatment's safety and efficacy is a randomized controlled trial (RCT), and it's only been relatively recently that real world data (RWD) -- data available outside of a rigorously controlled experiment -- has begun to be seen as a useful complement. On this episode, medical statistician Lewis Carpenter, Director of Real World Evidence (there's an acronym for that, too: RWE!) at Arcturis, joined Tim, Julie, and Val for a fascinating compare and contrast and caveating of RWD vs. RCTs in a medical setting and, consequently, what horizons that could broaden for the analyst working in more of a business analytics role. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

How good are humans at distinguishing between human-generated thoughts and AI-generated…thoughts? Could doing an extremely unscientific exploration of the question also generate some useful discussion? We decided to dig in and find out with a show recorded in front of a live audience at Marketing Analytics Summit in Phoenix! With Michael in the role of Peter Sagal, Julie, Tim, and Val went head-to-GPU by answering a range of analytics-oriented questions. Two co-hosts delivered their own answers, and one co-host delivered ChatGPT's, and the audience had to figure out which was which. Plus, a bit of audience Q&A, which included Michael channeling his inner Charlie Day! This episode also features the walk-on music that was written and performed live by Josh Silverbauer (no relation to Josh Crowhurst, the producer of this very podcast who also wrote and recorded the show's standard intro music; what is it about guys named Josh?!). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the  show page.

You know you've arrived as a broadcast presence when you open up the phone lines and get your first, "Long time listener, first time caller" person dialing in. Apparently, we have not yet arrived, because no one opened with that when they sent in their questions for this show. Our question is: why not?! Alas! That is a question not answered on this episode. Instead, we got the whole crew together and fielded questions from listeners that were actually worth attempting to answer, and we had a blast doing it! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Long-time listeners to this show know that its origin and inspiration was the lobby bar of analytics conferences—the place where analysts casually gather to unwind after a day of slides interspersed with between-session conversations initiated awkwardly and then ended abruptly when the next session begins. Of the many conferences where this occurs, Marketing Analytics Summit (née, eMetrics) is the one in which this show is most deeply rooted. And, we'll be recording an episode in front of a live audience with all of the North America-based co-hosts on Friday, June 7, 2024, in Phoenix, Arizona at the next one! To call that out, including announcing a promo code for any listeners interested in joining us for the event, Michael, Val, and Tim turned on the mics for a bonus episode with a little reminiscing about past experiences at the conference, including Val's mildly disturbing retention of dates and physical artifacts. Visit the show page for, well, not much more than you see here.

Is it just us, or does it seem like we're going to need to start plotting the pace of change in the world of analytics on a logarithmic scale? The evolution of the space is exciting, but it can also be a bit dizzying. And intimidating! There's so much to learn, and there are only so many hours in a day! Why did we choose that [insert totally unrelated field of study] degree program?! These questions and more—including a quick explanation of bootstrapping for Tim's benefit, which is NOT bootstrapping or bootstrap—are the subject of the latest episode of the show, with Kirsten Lum, the CTO of storytellers.ai, joining us to discuss strategies and tactics for the technically-non-technical analyst to thrive in an increasingly technical analytics world. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

The backlog of data requests keeps growing. The dashboards are looking like they might collapse under their own weight as they keep getting loaded with more and more data requested by the business. You're taking in requests from the business as efficiently as you can, but it just never ends, and it doesn't feel like you're delivering meaningful business impact. And then you see a Gartner report from a few years back that declares that only 20% of analytical insights deliver business outcomes! Why? WHY?!!! Moe, Julie, and Michael were joined by Kathleen Maley, VP of Analytics at Experian, to chat about the muscle memory of bad habits (analytically speaking), why she tells analysts to never say "Yes" when asked for data (but also why to never say "No," either), 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.

For those who celebrate or acknowledge it, Christmas is now in the rearview mirror. Father Time has a beard that reaches down to his toes, and he's ready to hand over the clock to an absolutely adorable little Baby Time when 2024 rolls in. That means it's time for our annual set of reflections on the analytics and data science industry. Somehow, the authoring of this description of the show was completely unaided by an LLM, although the show did include quite a bit of discussion around generative AI. It also included the announcement of a local LLM based on all of our podcast episodes to date (updated with each new episode going forward!), which you can try out here! The discussion was wide-ranging beyond AI: Google Analytics 4, Marketing Mix Modelling (MMM), the technical/engineering side of analytics versus the softer skills of creative analytical thought and engaging with stakeholders, and more, as well as a look ahead to 2024! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

To mentor, or not to mentor, that is the question: whether 'tis more productive to hole up in a cubicle and toil away without counsel, or to hold close one's experience to the benefit of no one else. Perchance, the author of this show summary should have checked with one of his mentors before attempting a Shakespearian angle. But, he didn't, and the show title is pretty self-explanatory, so we'll just roll with it. On this episode, Michael, Val, and Tim chatted about mentorship: its many flavors, its many uses, and what has and has not worked for them both when being mentored as well as when being mentors. 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 noticed that recipes that include estimates of how long it will take to prepare the dish seem to dramatically underestimate reality? We have! And that's for something that is extremely knowable and formulaic — measure, mix, and cook a fixed set of ingredients! When it comes to analytics projects, when you don't know the state of the data, what the data will reveal, and how the scope may shift along the way, answering the question, "How long will this take?" can be downright terrifying. Happy Halloween! Whether you are an in-house analyst or working in an agency setting, though, it's a common and reasonable question to be asked. In this episode Michael, Moe, and Val dive into the topic, including sharing some stories of battle scars and lessons learned along the way. As a bonus, Sensei Michael explains how he uses Aikido on his clients to avoid scope creep! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Seemingly straightforward data sets are seldom as simple as they initially appear. And, many an analysis has been tripped up by erroneous assumptions about either the data itself or about the business context in which that data exists. On this episode, Michael, Val, and Tim sat down with Viyaleta Apgar, Senior Manager of Analytics Solutions at Indeed.com, to discuss some antidotes to this very problem! Her structured approach to data discovery asks the analyst to outline what they know and don't know, as well as how any biases or assumptions might impact their results before they dive into Exploratory Data Analysis (EDA). To Viyaleta, this isn't just theory! She also shared stories of how she's put this into practice with her business partners (NOT her stakeholders!) at Indeed.com. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Most of the time, we think of analytics as taking historical data for a business, munging it in various ways, and then using the results of that munging to make decisions. But, what if the business has no (or very little) historical data… because it's a startup? That's the situation venture capitalists — especially those focused on early stage startups — face constantly. We were curious as to how and where data and analytics play a role in such a world, and Sam Wong, a partner at Blackbird Ventures, joined Michael, Val, and Tim to explore the subject. Hypotheses and KPIs came up a lot, so our hypothesis that there was a relevant tie-in to the traditional focus of this show was validated, and, as a result, the valuation of the podcast itself tripled and we are accepting term sheets. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.