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

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, the distinction between inference and prediction... and that's just a warm-up! What variables would you expect to be significant in a model aimed at predicting how engaging an episode will be? Presumably, guest quality would top your list! It topped ours, which is why we asked past guest Chelsea Parlett-Pelleriti from Recast to return for an exploration of the topic! Our model crushed it. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

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

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