The Analytics Power Hour
The Analytics Power Hour is a podcast where digital analytics professionals share their thoughts and experiences on the cutting edge of the field. Each episode covers a closed topic in an open forum format, aiming to provide listeners with actionable insights for their work. The hosts, including Michael Helbling, Tim Wilson, and Moe Kiss, draw from real-world discussions and industry best practices. The podcast originated from conversations at analytics conferences and aims to contribute valuable knowledge to the analytics community.
Episodes
#299: AI Can (Help) Build the Dashboard. It Can't Build the Buy-In.
There are roughly a thousand ways to roll out a new analytics platform, a BI tool migration, or an AI initiative to your organization. Most of them involve a town hall, an email with a link to some training materials, and the quiet hope that everyone figures it out. Most of them also don't really work. On this episode, Yehonatan Schwarzmer joined Michael, Val, and Tim to bring some long-overdue or
#298: Listener Questions Answered Live from Marketing Analytics Summit!
Picture this: four analytics professionals, one live audience, a bunch of submitted questions, and absolutely no filter when it comes to sharing their real thoughts about AI, stakeholder management, and the state of the industry. That's what you get when the Analytics Power Hour goes live from Marketing Analytics Summit, with Michael, Moe, Tim, and Val fielding everything from, "How do I prove I'm
#297: Durable Wisdom in an Age of AI Slop
What do colors, soup kitchens, and mountain climbing have in common? They're all part of the mental models that have shaped how we think about analytics, and they're exactly the kind of durable wisdom that matters more than ever in an age of AI slop. This campfire-style conversation among the co-hosts reveals the concepts, books, and aha moments that have stuck with us across decades of analytics
#296: Avoiding Major Oopsies: Twyman's Law, Intuition, and Valuing Accuracy Over Precision
What do diamond ring shopping, Uber pricing psychology, and active user metrics gone wrong have in common? They all highlight our complicated relationship with precision versus accuracy—and how that relationship can either build or destroy trust in our data. Arik Friedman from Atlassian joins us to unpack why being "about right" often beats being "exactly wrong," and why your nagging feeling that
#295: Research and Analytics: the Peanut Butter and Chocolate of Data?
Research and analytics: are they more like peanut butter and chocolate, or more like oil and water? On this episode, we dig into the surprisingly common (and surprisingly unfortunate) divide between these two disciplines with Stefanie Zammit, Global Director of Analytics and Insights at Bang & Olufsen. Stefanie has spent her career bridging the qual and quant worlds, and she makes a compelling cas
#294: Adapting an Analytics Team to an AI World
AI is moving fast. But so is life. AI is widely recognized as a must-adopt technology, but how and where are data workers expected to find the time for that?! Organizations are struggling to find effective ways to productively drive healthy adoption of AI: What is it they expect their workers to do with AI? Is it purely an efficiency driver, or should they expect other avenues of value creation to
#293: Tool Selection and the Unhelpfulness of Feature Comparisons
The one rule about the Analytics Power Hour is that we don't talk about specific tools. But that doesn't mean we won't talk about tool SELECTION! Jason Packer recently released the second edition of Google Analytics Alternatives, (also available on Amazon) and his approach in the book is very much not an RFP-like "check which features your tool offers" system. And his rationale for that seems just
#292: AI Without Adult Supervision with Aubrey Blanche
As Kevin McCallister once taught us: just because the house is still standing doesn't mean everything's under control. Everyone's racing to adopt AI, but has anyone actually read the fine print? For this year's International Women's Day episode, we are joined by Aubrey Blanche to unpack the hype, the hidden tradeoffs, and the quiet ways teams are giving up agency in the name of "productivity." We
#291: The Data Work that Lives in the Shadows
We know what the work of the data practitioner is, right? It's everything from managing data ingestion to data governance to report development to experimental design to basic and advanced analytics. It's writing (or vibe-writing?) SQL or Python or R while also being adept at whatever data stack—no matter how modern—is at hand. Of course, it's a lot more, too! And that's the topic of this episode:
#290: Always Be Learning
From a professional development perspective, you should always be learning: listening to podcasts, reading books, connecting with internal colleagues, following useful people on Medium and LinkedIn, and so on. Did we mention listening to podcasts? Well, THIS episode of THIS podcast is not really about that kind of learning. It's more about the sort of organizational learning that experimentation a
#289: The Imperative of Developing Business Acumen
That darn data. It's so complicated and fragmented and gap-filled and noisy that no amount of time is ever enough to truly get to the bottom of all of its complexity. As a result, it's pretty easy to fill all of our time handling as much of that underlying data messiness as possible. At what cost, though? It's easy for the analyst's connection to the business to suffer as they get mired (too) deep
#288: Our LLM Suggested We Chat about MCP. Kinda' Meta, No?
If there's one thing that we absolutely knew would be coming along with the increased interest and use of AI, it would be… more acronyms! And, along with the acronyms, we pretty much could predict that we see a lot of online flexing through casual dropping of said acronyms as though they're deeply understood by everyone who's anyone. We tackled one such acronym on this episode: MCP! That's "model
#287: 2025 Year in Review
It's the most…won…derful…tiiiiime…of the year! And by that, we mean it's the time of the year when we sit back, look at each other, and ask, "Where did all the time go?!" We brought back a very special someone for this episode as we collectively reflected on the year—show highlights (and what about those shows have stuck with us), industry reflections, and a little shameless shilling for Tim's boo
#286: Metrics Layers. Data Dictionaries. Maybe It's All Semantic (Layers)? With Cindi Howson
Semantic layers are having something of a moment, but they're not actually new as a concept. Ever since the first database table was designed with cryptic field names that no business user could possibly understand, there's been a need for some form of mapping and translation. Should every company be considering employing a semantic layer? Is the idea of a single, comprehensive semantic layer with
#285: Our Prior Is That Many Analysts Are Confounded by Bayesian Statistics
Before you listen to this episode, can you quantify how useful you expect it to be? That's a prior! And "priors" is a word that gets used a lot in this discussion with Michael Kaminsky as we try to demystify the world of Bayesian statistics. Luckily, you can just listen to the episode once and then update your expectation—no need to simulate listening to the show a few thousand times or crunch any
#284: I Used to Think...But Not Any More
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 po
#283: Good Things (Can) Come in Small Datasets with Joe Domaleski
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 ent
#282: Using (and Creating!) Data to Understand Pop Culture with Chris Dalla Riva
Data does not just magically spring into existence. Someone, somewhere, has to decide what data gets created and the rules for its creation. We would claim that this often starts as a pretty simple exercise, and then, over time, that simplicity balloons to be pretty complex! What if, for instance, you decided to listen to every #1 song on the Billboard Hot 100 going back to its inception in 1958?
#281: Analytics: The View from the Corner Office with Anna Lee
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.
#280: Dashboards Must Die! Long Live Dashboards! with Andy Cotgreave
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 irre
#279: The Process(es) of Analytics (We Have Thoughts)
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 proc
#278: Is AI Good at Data Analysis? That's the Wrong Question? with Juliana Jackson
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
#277: ANOVA? I Hardly Know Ya'! with Chelsea Parlett-Pelleriti
Did you know that, upon closer inspection, many a statistical test will reveal that "it's just a linear model" (#IJALM)? That wound up being a key point that our go-to statistician, Chelsea Parlett-Pelleriti, made early and often on this episode, which is the next installment in our informally recurring series of shows digging into specific statistical methods. The method for this episode? ANOVA!
#276: BI is Dead! Long Live BI! With Colin Zima
Product managers for BI platforms have it easy. They "just" need to have the dev team build a tool that gives all types of users access to all of the data they should be allowed to see in a way that is quick, simple, and clear while preventing them from pulling data that can be misinterpreted. Of course, there are a lot of different types of users—from the C-level executive who wants ready access
#275: The Modern Data...Job Search with Albert Bellamy
It's a process few people genuinely enjoy, but it's one which we all find ourselves going through periodically in our careers: landing a new job. We grabbed MajorData himself, Albert Bellamy, for a wide-ranging discussion about the ins and outs of that process: LinkedIn invitation etiquette (and, more importantly, effectiveness), how networking is like spousal communication (!), the usefulness of
#274: Real Talk About Synthetic Data with Winston Li
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 ma
#273: Data Products Are... Assets? Platforms? Warehouses? Infrastructure? Oh, Dear. With Eric Sandosham
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 mo
#272: When the Metric is Calculated and Complex with Dan McCarthy
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
#271: It Might Be Irrational, but Let's Talk Behavioral Science with Dr. Lindsay Juarez
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 behavio
#270: AI and the Analyst. We've Got It All Figured Out.
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 e
#269: The Ins and Outs of Outliers with Brett Kennedy
How is an outlier in the data like obscenity? A case could be made that they're both the sort of thing where we know it when we see it, but that can be awfully tricky to perfectly define and detect. Visualize many data sets, and some of the data points are obvious outliers, but just as many (or more) fall in a gray area—especially if they're sneaky inliers. z-score, MAD, modified z-score, interqua
#268: You Get an Insight! And YOU Get an Insight! with Chris Kocek
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 "Ins
#267: Regression? It Can be Extraordinary! (OLS FTW. IYKYK.) with Chelsea Parlett-Pelleriti
Why? Or… y? What is y? Why, it's mx + b! It's the formula for a line, which is just a hop, a skip, and an error term away from the formula for a linear regression! On the one hand, it couldn't be simpler. On the other hand, it's a broad and deep topic. You've got your parameters, your feature engineering, your regularization, the risks of flawed assumptions and multicollinearity and overfitting, t
#266: AI Projects: From Obstacles to Opportunities
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 fra
#265: Connected Wellness in the Age of AI with Michael Tiffany
Every listener of this show is keenly aware that they are enabling the collection of various forms of hyper-specific data. Smartphones are movement and light biometric data collection machines. Many of us augment this data with a smartwatch, a smart ring, or both. A connected scale? Sure! Maybe even a continuous glucose monitor (CGM)! But… why? And what are the ramifications both for changing the
#264: When the Analyst's Toolbox Includes Assessing the Zeitgeist with Erika Olson
We all know that data doesn't speak for itself, but what happens when multiple instruments of measurement contain flaws or gaps that impede our ability to measure what matters on their own? Turning to our intuition and triangulation of what's happening in the broader macro sense can often help explain our understanding of our customers' ever-changing choices, opinions, and actions. Thankfully we h
#263: Analytics the Right Way
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 framew
(Bonus) 2024 Listener Survey...Wrapped!
The start of a new year is a great time for reflection as well as planning for the year ahead. Join us for this special bonus episode where we talk through some of our favorite learnings and takeaways from our 2024 listener survey and some of the ways we've already been able to put that feedback into practice! We also have some freebies and helpful nuggets to share with our listeners, so be sure t
#262: 2025 Will Be the Year of... with Barr Moses
Every year kicks off with an air of expectation. How much of our Professional Life in 2025 is going to look a lot like 2024? How much will look different, but we have a pretty good idea of what the difference will be? What will surprise us entirely—the unknown unknowns? By definition, that last one is unknowable. But we thought it would be fun to sit down with returning guest Barr Moses from Monte
#261: 2024 Year in Review
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
#260: Once Upon a Data Story with Duncan Clark
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
#259: Dateline Data
There's data, data everywhere, including in the media! Data often gets collected, analyzed, published in a study, covered by a journalist, and then distilled down to a headline. The opportunities for lost-in-translation (or lost-in-simplification? Lost-in-summarization?) misfires are many. We tried an experiment—each of the available co-hosts brought some headlines that made them raise an eyebrow,
#258: Goals, KPIs, and Targets, Oh My! with Tim Wilson
KPIs? Really? It's 2024. Can't we just ask Claude to generate those for us? We say… no. There are lots and lots of things that AI can take on or streamline, but getting meaningful, outcome-oriented alignment within a set of business partners as they plan a campaign, project, or initiative isn't one of them! Or, at least, we're pretty sure that's what our special guest for this episode would say. H
#257: Analyst Use Cases for Generative AI
udging by the number of inbound pitches we get from PR firms, AI is absolutely going to replace most of the work of the analyst some time in the next few weeks. It's just a matter of time until some startup gets enough market traction to make that happen (business tip: niche podcasts are likely not a productive path to market dominance, no matter what Claude from Marketing says). We're skeptical.
#256: Live at MeasureCamp Chicago
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, wi
#255: Dear APH-y: Career Inflection Points
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 su
#254: Is Your Use of Benchmarks Above Average? with Eric Sandosham
It's human nature to want to compare yourself or your organization against your competition, but how valuable are benchmarks to your business strategy? Benchmarks can be dangerous. You can rarely put your hands on all the background and context since, by definition, benchmark data is external to your organization. And you can also argue that benchmarks are a lazy way to evaluate performance, or at
#253: Adopting a Just In Time, Just Enough Data Mindset with Matt Gershoff
While we don't often call it out explicitly, the driving force behind much of what and how much data we collect is driven by a "just in case" mentality: we don't know exactly HOW that next piece of data will be put to use, but we better collect it to minimize the potential for future regret about NOT collecting it. Data collection is an optionality play—we strive to capture "all the data" so that
#252: The Ever-Shifting Operating Environment of the Data Professional
Broadly writ, we're all in the business of data work in some form, right? It's almost like we're all swimming around in a big data lake, and our peers are swimming around it, too, and so are our business partners. There might be some HiPPOs and some SLOTHs splashing around in the shallow end, and the contours of the lake keep changing. Is lifeguarding…or writing SQL…or prompt engineering to get AI
#251: The Continued Rise of the Analytics Engineer with Dumky de Wilde
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
#250: Real World Data (RWD) Lessons from Healthcare-land with Dr. Lewis Carpenter
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 tre
#249: Three Humans and an AI at Marketing Analytics Summit
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
#248: The Fundamentally Fascinating World of APIs with Marco Palladino
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 na
#247: Professional Development, Analytically Speaking with Helen Crossley
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, a
#246: I've Got 99 Analytical Methodologies and Need to Pick Just One
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 ana
#245: Dear APH-y - An Analytics Advice Call-In Show
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
#244: Data Is Everywhere. Why Do We Limit Ourselves by Default?
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 d
#243: Being Data-Driven: a Statistical Process Control Perspective with Cedric Chin
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 variab
#242: The Rise and Fall of Data Communities with Pedram Navid
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
(Bonus) Marketing Analytics Summit Is Nigh!
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
#241: The Analyst's Underutilized Tool: the Sketchbook with Dan White
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
#240: Asking Better Questions with Taylor Buonocore Guthrie
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
#239: Non-Technical Backgrounds in the Modern Analytical World with Kirsten Lum
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 questio
#238: The Many Problems in Dealing with Data Problems
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
#237: Crossing the Chasm from the Data to Meaningful Outcomes with Kathleen Maley
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 fr
#236: The AI Ecosystem with Matthew Lynley
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, Supe
#235: 2023 Year in Review with Josh Crowhurst
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 co
#234: Establishing Expectations for Analysts
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 expectation
#233: Analytics Mentors (Having One, Being One)
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
#232: The Reality of Uncertainty Meets the Imperative of Actionability with Michael Kaminsky
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,
#231: Estimating the Effort for Analytics Projects
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
#230: First, We Must Discover. Then, We Can Explore. With Viyaleta Apgar
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 pro
#229: Data and the ABCs (SERIES A, B, and C, That Is!) with Samantha Wong
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
#228: What AI Can't Do with Dr. Brandeis Marshall
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 sur
#227: Demystifying Complex Data Science Concepts for Non-Technical Audiences with Dr. Nicholas Cifuentes-Goodbody
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
#226: Training Analysts to be Curious and Use Business Context with MaryBeth Maskovas
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
#225: From Stakeholder Buy-In to Stakeholder Knowledge of What That Means
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, analy
#224: The Chronic Undervaluing of Analyst Communication Skills
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
#223: Explainability in AI with Dr. Janet Bastiman
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
#222: A is for… Analytics. Agency. Acquisitions! with Bob Morris
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 t
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