Prediction seems to defy a Law of Nature: You cannot see the future because it is not here yet. We find a work-around by building machines that learn from experience. It’s the regimented discipline of using what we do know—in the form of data—to place increasingly accurate odds on what’s coming next.
—Eric Siegel, Predictive Analytics1
In general, our ability to predict outcomes for situations involving people is increasing within certain contexts. This is due largely to the fact that we now have vast amounts of data and the computing power and methodologies to efficiently mine these data for patterns and implications. However, at the time we are writing this, we are unaware of a process for accurately predicting what people will find valuable in the future. Once a need is identified, finding a solution that fills it is much easier. Necessity is the mother of invention.
Yet there is still a bit of skill involved in meeting an identified need in a way that is efficient, sustainable (environmentally and economically), and willingly adopted by people. And this is the area in which business and design so often find themselves collaborating, because it is here that value can be developed (and on which the entire premise of a business rests).
The human mind—the natural computer/the inherent designer—is very adept at processing large amounts of data and using this to build predictive models, but it also has limitations. It cannot fully analyze ...