The Analytics Power Hour

Application Programming Interfaces (APIs) are as pervasive as they are critical to the functioning of the modern world. That personalized and content-rich product page with a sub-second load time on Amazon? That's just a couple-hundred API calls working their magic. Every experience on your mobile device? Loaded with APIs. But, just because they're everywhere doesn't mean that they spring forth naturally from the keystrokes of a developer. There's a lot more going on that requires real thought and planning, and the boisterous arrival of AI to mainstream modernity has made the role of APIs and their underlying infrastructure even more critical. On this episode, Moe, Julie, and Tim dug into the fascinating world with API Maven Marco Palladino, the co-founder and CTO at Kong, Inc. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Professional development is a big topic—way more than just thinking about what job you want in five years and setting milestones along the way. Thankfully we had Helen Crossley, Senior Director of Marketing Science at Meta, join Michael, Moe, and Val to dive deep into this topic! We explored how to set really good, meaningful goals, the challenges across each stage from junior analyst to leader, and how to give great feedback. We also spent quite a bit of time discussing the new challenges that becoming a first-time manager presents and, hopefully, some helpful tips and thought exercises to help out our listeners who are or are about to be faced with this challenge. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

From running a controlled experiment to running a linear regression. From eyeballing a line chart to calculating the correlation of first differences. From performing a cluster analysis because that's what the business partner asked for to gently probing for details on the underlying business question before agreeing to an approach. There are countless analytical methodologies available to the analyst, but which one is best for any given situation? Simon Jackson from Hypergrowth Data joined Moe, Julie, and Tim on the latest episode to try to get some clarity on the topic. We haven't figured out which methodology to use to analyze whether we succeeded, so you'll just have to listen and judge for yourself. 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.

In order to produce a stellar analysis, have you ever requested a team to teardown a Tesla and count every last washer and battery cell? No? Well our guest this week, Jason DeRise, joined Tim, Julie, and Val to share that story and others on how alternative data can be used to enrich analyses. Luckily you don't have to have a Wall Street-sized budget in order to tap into the power of alternative data. Looking just outside your tried and true data sets and methodologies to see how you might be able to add to your mosaic of understanding a business question can be powerful! In this episode we talk about some of the considerations and approaches when you put down that hammer and see the world around you is more than just a bunch of nails. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

It happens occasionally. Someone in the business decides they need to just take the analysis into their own hands. That leaves the analyst conflicted — love the interest and enthusiasm, but cringe at the risk of misuse or misinterpretation. Occasionally (rarely!), though, such a person goes so deep that they come out the other side having internalized everything from Deming's obsession with variability all the way through the Amazon Weekly Business Review (WBR) process. And they've written extensively about it. Cedric Chin was such a person, and we had a blast digging into his exploration of statistical process control — including XmR charts — and mulling over the broader ramifications and lessons therein. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Data communities have played a major role in the careers of many analysts, but times they are a-changin'. We're not sure if we're different, if the communities' purposes and missions have shifted, or both. One thing we are confident in, though, is that Pedram Navid was absolutely the right guest to invite on to the show to explore the topic alongside Michael, Moe, and Val. His blog post last year that discussed how "this used to be fun" was a great reflection on some of the environmental trends influencing the communities we've come to know and love. But don't worry, it's not all doom and gloom! The crew all agreed that there are still places and ways for data practitioners to connect and support each other, even if it doesn't look identical to the early aughts. 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.

As a general rule, analysts are drawn to precision: let's understand the business problem and then go figure out how the data can be acquired and crunched to provide something specific and useful. Fair enough. Where, then, do pencil and paper and 10-second sketches fit in? Or hastily and collaboratively drawn flippy chart or whiteboard sketches? We could draw you a picture to explain, but podcasts are an audio medium, so, instead, we brought on the illustrious illustrator, consultant, and author, Dan White. From triangles, to rolling snowballs, to trees, to Venn diagrams, to the conjoined triangles of success, this episode paints a pretty clear picture of the power of the quick sketch! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

They say an analysis is only as good as the question that was asked, so for our 2024 International Women's Day Episode, Julie, Moe, and Val were joined by Taylor Buonocore Guthrie to discuss how to ask better questions. Every analyst is naturally curious, but the thoughtfulness that Taylor puts into what type of questions to ask, how to ask them, and when to ask them to get the optimal response is truly an art form. Instead of drilling the five-whys the next time you are gathering context with a business partner for an analysis or conducting discovery interviews, try prompting them with, "Can you walk me through your thinking?" or "What else is important for me to know?" to gather the right context and clarify your understanding. We can't wait for you to hear all of the practical advice and suggestions for things you might consider incorporating into your repertoire! 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 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 data has problems. It ALWAYS has problems. Sometimes they're longstanding and well-documented issues that the analyst deeply understands but that regularly trip up business partners. Sometimes they're unexpected interruptions in the data flowing through a complex tech stack. Sometimes they're a dashboard that needs to have its logic tweaked when the calendar rolls into a new year. The analyst often finds herself on point with any and all data problems—identifying an issue when conducting an analysis, receiving an alert about a broken data feed, or simply getting sent a screen capture by a business partner calling out that something looks off in a chart. It takes situational skill and well-tuned judgment calls to figure out what to communicate and when and to whom when any of these happen. And if you don't find some really useful perspectives from Julie, Michael, and Moe on this episode, then we might just have a problem with YOU! (Not really.) 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.

Aptiv, Baidu, Cerebras, Dataiku… we could keep going… and going… and going. If you know what this list is composed of (nerd), then you probably have some appreciation for how complex and fast moving the AI landscape is today. It would be impossible for a mere human to stay on top of it all, right? Wrong! Our guest on this episode, Matthew Lynley, does exactly that! In his Substack newsletter, Supervised, he covers all of the breaking news in a way that's accessible even if you aren't an MLE (that's a "machine learning engineer," but you knew that already, right?). We were thrilled he stopped by to chat with Julie, Tim and Val about some of his recent observations and discuss what the implications are for analysts and organizations trying to make sense of it all. 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.

It would be a fool's errand to try to list out every expectation for an analyst's role, but where should you draw the line? How specific do you need to be? And how can you document the unspoken expectations without stepping into micromanagement? Tim, Moe, and Julie took a run at hashing these questions out in our most recent episode so you don't have to rely solely on that generic role expectations grid you got from HR. Even though this topic is about setting expectations for other analysts, the conversation took quite a few introspective turns about how your internal standards are calibrated and what experiences along the way shaped them. As usual, you can expect some great stories about expectation setting gone wrong and what happens when you make Tim have a conversation about feelings, you miss one of Moe's deadlines, or use the wrong font in one of Julie's deliverables! 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.

It's been said that, in this world, nothing is certain except death and taxes, so why is it so hard to communicate uncertainty to stakeholders when delivering an analysis? Many stakeholders think an analysis is intended to deliver an absolute truth; that if they have just enough data, a smart analyst, and some fancy techniques, that the decision they should make will emerge! In this episode, Tim, Moe, and Val sat down with Michael Kaminsky, co-founder of Recast, to discuss strategies such as scenario planning and triangulation to help navigate these tricky conversations. Get comfortable with communicating the strengths and drawbacks of your different methodological approaches to empower decision making from your stakeholders! 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.

It's a lot of work to produce each episode of this show, so we were pretty sure that, by this time, we would have just turned the whole kit and kaboodle over to AI. Alas! It seems like the critical thinking and curiosity and mixing of different personalities in a discussion are safely human tasks… for now. Dr. Brandeis Marshall joined Michael, Julie, and Moe for a discussion about AI that, not surprisingly, got a little bleak at times, but it also had a fair amount of hope and handy perspectives through which to think about this space. We recommend listening to it rather than running the transcript through an LLM for a summary! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

One of the biggest challenges for the analyst or data scientist is figuring out just how wide and just how deep to go with stakeholders when it comes to key (but, often, complicated) concepts that underpin the work that's being delivered to them. Tell them too little, and they may overinterpret or misinterpret what's been presented. Tell them too much, and they may tune out or fall asleep… and, as a result, overinterpret or misinterpret what's been presented. On this episode, Dr. Nicholas Cifuentes-Goodbody from WorldQuant University joined Julie, Val, and Tim to discuss how to effectively thread that particular needle. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We were curious about… curiosity. We know it's a critical trait for analysts, but is it an innate characteristic, a teachable skill, or some combination of both? We were curious. How can the breadth and depth of a candidate's curiosity be assessed as part of the interview process? We were curious. Who could we kick these questions (and others) around with? We were NOT curious about that! MaryBeth Maskovas, founder and Principal Consultant at Insight Lime Analytics, joined Michael, Julie, and Tim to explore the topic. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

This topic was such a big deal that we managed to have no guests, and yet we had five people on the mic! Why? Because this episode doubles as a marker of a shift in the show itself. Beyond that, though, we had a lively discussion about how every business stakeholder professes to being committed to being data driven. That should make every stakeholder super easy to work with, right? And, yet, analysts often find themselves struggling to get on the same page with their counterparts due to the realities of the data: what it can and can't do and how it is most effectively worked with. Not a small topic! There were even pop quizzes (feel free to let us know how you'd score the answers)! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

On the one hand, analysts generally know and accept that part of their responsibility is to not only conduct analyses, but to effectively communicate the results of those analyses to their stakeholders. On the other hand, "communication" can feel like a pretty squishy and nebulous skill. On this episode, Michael, Moe, and Tim tackled that nebulosity (side note: using obscure words is generally not an effective communication tactic). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

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.

Do you listen to this podcast because you're pretty sure that you're a professional fraud, and you're hoping-hoping-hoping that you will absorb enough knowledge to stay ahead of being exposed as such? Well, stop that negative self-talk! Impostor syndrome is a very real thing, and we've devoted a whole show to digging into it! Julie Hoyer and Val Kroll joined Moe on this International Women's Day episode to discuss the topic. It turns out that there has been a lot of research in the area, there ARE techniques for battling it, and it IS useful to hear how common it is! 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.