Love it!

Even more, I love your job title: Chief Decision Intelligence Engineer. Way to reframe “data science” as what it’s really about: smarter and more effective decision-making!

We were debating the merits or otherwise of the phrase “data driven” just last week. Vague conclusion: business is, at its root, myriad decisions made either by humans or by systems that have been constructed by humans. Each decision can be more or less informed by data, but it’s a messy human who decides which data to include and which to discount. Data need to be interpreted and contextualised before they can be used to make better decisions.

What’s more, the data in “data driven” are often understood as analytical quantitative data, but often can and should include qualitative data just as much – depending on what it is you’re trying to learn or decide. And your principles for attempting to counteract our confirmation biases apply equally vividly to qualitative data.

Have you come across Annie Duke’s Thinking In Bets? All about better decision-making in conditions of uncertainty and bias. There’s a book, but the Knowledge Project podcast episode she’s on (https://fs.blog/annie-duke/) is a great starting point. Very applicable stuff.

I also love Ray Dalio’s Principles. He talks a about how decisions are made at the company he founded, Bridgewater Capital. In short, they spend a lot of time discussing how they’ll choose the best ideas, not discussing the ideas themselves. He admits himself that this is really hard for most people to get used to, and that some simply can’t take it.

Like several other commenters, I’m looking forward to your thoughts on exploratory experimentation – that’s one of my passions.