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, and we tested our own data literacy (data skepticism) with a real-time review. The parallels to the day-to-day work of an analyst were many! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

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. He’s been thinking about (and ranting about) organizations’ failure to take goal establishment, KPI identification, and target-setting seriously enough for years (we found a post he wrote in 2009 on the subject!). He also really helped us earn our explicit tag for this episode — scatologically and onanistically, we’re afraid. But solid content nonetheless, so hopefully you can hear past that! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

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. But that doesn’t mean we don’t think there are a lot of useful applications of generative AI for the analyst. We do! As Moe posited in this episode, one useful analogy is that thinking of using generative AI effectively is like getting a marketer effectively using MMM when they’ve been living in an MTA world (it’s more nuanced and complicated). Our guest (NOT from a PR firm solicitation!), Martin Broadhurst, agreed: it’s dicey to fully embrace generative AI without some understanding of what it’s actually doing. Things got a little spicy, but no humans or AI were harmed in the making of the episode.

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