Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.
"That's Interesting" is NOT a Compliment
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“Analyse this dataset and come back with any insights you find.” This was one of the first requests I ever received as a data scientist, and to be perfectly honest, I didn’t quite understand what it meant. Sure, I understood the “analyse the dataset” part, but at the time, I had no idea what my stakeholder meant when she spoke of “insights”. I interpreted the word as a synonym for “interesting facts”. And that’s exactly how she politely responded when I returned with my findings. “That’s interesting.” Unsurprisingly, my work didn’t drive any change. In fact, I suspect it just got shoved in a drawer, never to be seen again, as soon as I left the room. In the years that followed, I found myself frequently reflecting on that experience and wondering what I did wrong. The answer didn’t come until I eventually read Effective Data Storytelling by Brent Dykes. In it, Brent argues that interesting observations and genuine insights are not the same thing - and the ones that truly matter are those that offer a tangible promise of value, such as increased revenue, cost savings or reduced risk. In the latest episode of Value Driven Data Science, Brent joins me to share the frameworks behind identifying and communicating insights that actually move organisations to act. In this episode, you’ll discover:
If your work keeps ending up in a drawer, this episode will show you where to begin. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 103: The Art of the Actionable Insight Talk again soon, Dr Genevieve Hayes |
Data Science Impact Algorithm
Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.
Machine learning models are designed to predict outcomes. Given the characteristics of a customer, a machine learning model can, for example, predict whether they'll buy a product, click on a link, cancel a service, or claim on their insurance. But what an ML model can't tell you is what causes the customer to do any of these things - and what would happen if an organisation intervened to change their behaviour. This is because ML models are correlation-based. And as most data scientists...
Recently, I've been systemising my workflows. It's partly about efficiency, but also a way of experimenting with AI. Whenever I encounter a process I've performed dozens of times, I look for a way to automate, templatise or standardise it by feeding my existing knowledge and artefacts into Claude. It's system leverage 101, turbo-charged by AI. But even though AI makes this easier than ever before, none of this would be possible if I hadn't already mastered these tasks. I'm not asking AI to...
If you want to build a physical structure, be it a mine, a bridge, or a factory, the first thing you invariably need to do is create a blueprint. You can’t just show up at the construction site and start building, or you risk wasting a lot of time and money on a project that returns nothing more than a structurally unsound pile of raw materials. The same is true when building a digital twin. But here’s where data scientists have one major advantage over their counterparts in the physical...