Starz

The most dangerous executive I’ve worked with loved statistics. Six months into a “data literacy program,” he started saying things like “statistically significant” in meetings… right before overriding models that had been validated for months. Three overrides. One quarter. All wrong. Data literacy in the wrong hands isn’t empowering. It’s a liability. You don’t get a better decision-maker. You get someone just confident enough to misinterpret uncertainty. They don’t question assumptions. They replace them with half-understood ones. And now you’re arguing with vibes dressed up as math. Everyone keeps shouting “teach executives data.” Why? So they can challenge your methodology with a Medium article they half-read last night? Executives don’t need to understand your models. They need to understand consequences. - Not “statistically significant” → “this makes us $2M” - Not “95% accuracy” → “this reduces churn by 8%” Your job isn’t education. It’s translation. Because the real problem isn’t executives who don’t understand data. It’s the ones who think they do. Those are the ones who override your model and call it insight. #Leadership #DataScience #DecisionMaking

Everyone in data science loves to flex their “advanced model.” Gradient boosting. Neural nets. Bayesian fairy dust. Cool. Now show me the dumb version it beat. Because if you can’t… you didn’t build intelligence. You built theater. True story: I ran analytics at an ad agency. Client wanted an in-house model. Their statistician spent a month crafting it in R... beautiful, complex, probably made him feel important. My team wanted a quick read. So I built a regression in Excel. Difference? 1 percentage point. That’s it. Simple doesn’t always win. But it shows up enough to make your “innovation” look suspiciously like overengineering. Here’s the real game: – Baseline first – Measure lift honestly – Price the complexity Accuracy isn’t free. And you may be paying Ferrari prices for bicycle problems. In the AI era, anyone can build something impressive. Very few can prove it’s necessary. That’s the difference between a data scientist… …and a spreadsheet with a superiority complex. What's the simplest model you've seen outperform something that took ten times longer to build? This post is based on my article, "Judgment Is the Scarce Resource in Data Science" available on All Things Insights: https://lnkd.in/eDEMwjES The infographic below is NotebookLM's summary of the article. You're welcome. #DataScience #MachineLearning #BusinessImpact