The Analytics Power Hour – Details, episodes & analysis

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 to write SQL…or identifying business problems a job or a skill? Does it matter? Aren't we all just trying to get to the Insights Water Slide? Katie Bauer, Head of Data at Gloss Genius and thought-provoker at Wrong But Useful, joined Michael, Julie, and Val for a much less metaphorically tortured exploration of the ever-shifting landscape in which the modern data professional operates. Or swims. Or sinks? 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.

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.