I have been around since the beginning of the data revolution and have been involved with analytics for the vast majority of my career. Whether as a consultant or a company insider, I helped capture, query, and analyze data. I used this data to provide quantitative answers to questions like: "Are we meeting our revenue targets?" and "Why?" or "Why not?" But as I moved into more strategic work — evaluating new markets, assessing potential acquisitions, and realigning operations — I found that traditional analytics offered little to no help. Those are decisions, and decisions are ultimately about the future.
That realization set me on a search to understand decision making more deeply. I found no shortage of books, articles, and frameworks on the subject. But most of what I found focused on avoiding biases, or on general principles like "gather more information," "involve the right people," "rely on your intuition," or "don't rely on your intuition." None of the literature seemed to address the question: when faced with a big, high-stakes choice, how do you actually, concretely, go about making it?
At some point it dawned on me that the key lies in predicting the future. Because if you could predict the future, decisions would be trivial. If you knew whether or not a market would be anxious to buy a product at a certain price, and you knew the size of that market, then whether or not to enter it would be a no-brainer. So, how to predict the future?
I read books — many, many books. Most of what I found was aimed at economists, investors, or machine learning specialists. But as I worked through it, I began to see how the same ideas could be applied directly to everyday business decision making. I learned that:
- Useful signals about the future exist — they may not come in traditional datasets, but they are no less valuable.
- Human judgment and intuition can be treated as data — extracted, structured, and quantified rather than left vague.
- Big decisions can be decomposed — breaking them into manageable components is essential for understanding the inputs that truly drive the decision.
- Confidence matters — understanding not just the forecast but how much certainty surrounds it is a vital, often overlooked, element of decision making.
- Integration is transformative — when judgment, traditional data, and other signals are combined into a single quantifiable output, decision makers gain a view of the future that none of the inputs could provide on their own.
The most important thing I learned, though, is that the future can be described in concrete terms. Probability theory can let us visualize not just one future that we have to make a bet on, but many possible futures — all with different probabilities of occurring. Once those futures and the assumptions that drive them are made visible, they can be examined, stress-tested, and debated.
Quantification brings clarity by forcing us to say exactly what we mean ("60%" instead of "pretty confident"); comparability by letting us weigh different paths side by side; transparency by surfacing the assumptions that drive our judgments; calibration by allowing us to learn over time whether our forecasts match reality; and, most important of all, decision leverage by letting leaders "test drive" possible futures before committing resources.
This is the foundation of the work I do today: helping leaders combine data with judgment to make the future visible.
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