The Analytics Power Hour – Details, episodes & analysis

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 potential options? Privacy concerns are something no one really cares about…or they are something everyone cares deeply about? Voices were raised. Light profanity was employed. Laughter ensued.

This episode's Measurement Bite from show sponsor Recast is a brief explanation of statistical significance (and why shorthanding it is problematic…and why confidence intervals are often more practically useful in business than p-values) from Michael Kaminsky.

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

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 enterprises have scenarios where some datasets easily fit into Google Sheets with limited scrolling required. Should this data be dismissed out of hand, or should it be treated as what it is: potentially useful? Joe Domaleski from Country Fried Creative works with a lot of businesses that are operating in the small data world, and he was so intrigued by the potential of putting data to use on behalf of his clients that he's mid-way through getting a Master's degree in Analytics from Georgia Tech! He wrote a really useful article about the ins and outs of small data, so we brought him on for a discussion on the topic!

This episode's Measurement Bite from show sponsor Recast is an explanation of synthetic controls and how they can be used as counterfactuals from Michael Kaminsky!

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

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 marketing mix modelling, we explore how this artificial data is generated, where its strengths truly lie, and the potential pitfalls to watch out for! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Remember how we used to have bonus episodes? With International Women's Day coming up in a week, it seemed like a good time to bring them back. Tim is joined by Rehgan Avon, the founder of the organization that runs the DataConnect Conference, which will be June 2-3, 2022 in Columbus, Ohio (and virtually). It's a conference open to everyone to attend, but all of the speakers are women or gender minorities. Tim and Rehgan also discuss the current state of gender diversity in the profession and how it has changed since Rehgan started the organization that became this conference back in 2016. And there's a discount code! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

With so many types of companies to work for, and analysts being in high demand, we're at a point where many of us find ourselves in the enviable position of being able to pick which company — and which type of company — we want to work for. Oh, bother! That means… we have to choose! In-house? Consultancy? Agency? Product? What's the "best" option? If you already know the answer is, "It depends," then you just might be the perfect fit for consulting! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Mistakes happen. In healthy work environments, not only is that fact acknowledged, it's recognized as an opportunity to learn. That's something JD Long has been thinking about quite a bit over the past few years, and he joined the show for a chat about psychological safety: what it is, why it's important, and different techniques for engendering it. Michael trolled Tim almost immediately, which is: 1) ironic, and 2) slated to be addressed in a blameless post-mortem. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Some would say that, given the breadth and depth of data that is available to businesses these days, a surefire path to business value is to load up a department with smart data scientists, task them with developing a solid machine learning strategy, and then execute that strategy. The people who've said that might take issue with this episode. Cassie Kozyrkov joined the show to discuss decision-making: what it is, how we often frame decisions too narrowly, and the different roles data can play to support the process. 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.