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.
Have You Been Gaslit By Data Science?
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Machine learning excels at automating routine decisions. But the decisions that matter most are far from routine. The decisions that truly make or break an organisation are the high-stakes, one-off decisions where data is scarce, machine learning falls apart, and executive stakeholders are left relying on their gut. These are also the situations where data scientists have the potential to add the greatest value - if they know how. As a data scientist with a background in actuarial science and statistics, when machine learning has failed me, I’ve found myself falling back on actuarial and statistical techniques better suited to sparse data - while all the time worrying I was somehow doing data science “wrong”. If I wasn’t creating a machine learning solution, could I even call myself a data scientist? So when I came across How to Measure Anything by Douglas Hubbard, it was a revelation to me. Not because it taught me something entirely new, but because it confirmed I was doing data science right all along. It was as if data science had suddenly stopped gaslighting me. In the latest episode of Value Driven Data Science, Doug joins me to share how combining techniques from statistics, economics and decision theory can help data scientists tackle the problems that matter most. You’ll discover:
The decisions that make or break organisations rarely come with large datasets. This episode is about what to do when they don’t. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 109: How to Measure Anything and Make Better Decisions 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.
“AI destroys jobs but creates businesses.” That one statement crystallised what I’ve long believed about building a data science career. The statement was made by best-selling author and futurist Peter H. Diamandis in a recent post. Diamandis argues that while AI will lead to the destruction of white-collar jobs, as organisations replace human workers with AI, AI has made it easier than ever for entrepreneurs to launch one-person companies. The jobs that AI creates, therefore, won’t look like...
Quick quiz: If you randomly sampled just 5 people from a population of 20,000 or more, could you use that data to tell your stakeholders anything useful? For most data scientists, the answer is probably no. But according to Douglas Hubbard, author of How to Measure Anything, “you need far less data than you think.” Here’s the proof: If you randomly sample just five people from an organisation of any size and record the lowest and highest values of whatever you’re measuring, there’s a 93.75%...
I learned the true meaning of accountability on a Sunday morning, many years ago, when I had to go into the office to fix a calculation mistake made by a member of my team. I was an insurance pricing manager at the time. My team performed premium calculations that brought in $2 billion of revenue. It was complex work, spread across multiple staff, and the margin for error was incredibly low. When my boss spotted an error in a table my team had produced, it was all I could do not to say:...