Advertise on podcast: The Analytics Power Hour

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.