This week I’m participating in the Post-2-Post virtual book tour for The Two-Second Advantage: How We Succeed by Anticipating the Future — Just Enough, by Vivek Ranadive and Kevin Maney. I originally intended to post my review on my own blog, but after reading it, I realize it sheds some light on a subject critical to collaborative innovation — how to turn large amounts of data into innovations of value.

Often when companies embrace open innovation techniques, especially crowdsourcing, they are unable to use more than a fraction of the ideas generated. Usually the answer to this problem has been to frame or focus the crowdsourcing campaign so the majority of the input is more aligned with the company’s goals or strategy. But doing so often means that IF a breakthrough idea should happen to come through, it will be discarded long before it ever gets to someone who could recognize it for the breakthrough it is, because of the many man-hours required to evaluate crowdsourcing results.

But — what if you had a computer program that could screen the data from thousands of customer ideas and generate a report on not just possible breakthrough suggestions, but also patterns in the data that indicated possible new avenues for innovation that you hadn’t even thought of? Such a thing doesn’t exist. But according to The Two-Second Advantage, the kind of predictive technology that would allow for this is not only on its way, it’s already showing up in a few places.

The premise of The Two-Second Advantage is that talent is comprised of the ability to anticipate events — think hockey great Wayne Gretzky “skating to where the puck will be.” The kind of higher-level thinking that allows for prediction as opposed to after-the-fact analysis is what our brains can do, and what computers can’t yet do. Authors Ranadive and Maney talk about how in very talented people the brain learns to effortlessly sift through data — and in some cases the absence of data — and make predictions about what action to take next. This is not analysis, it’s more like learning to see patterns and then anticipating what to do when those patterns recur. It’s seeing a few trees and predicting there’s a forest, rather than needing to count every tree and feed the data on how many trees there are into an algorithm that then and only then says “X number of trees make up a forest.”

The authors cite Google’s flu-tracking effort as one example of a company using large amounts of data to make predictions. While tracking the number of searches people make for “flu” and “fever” isn’t exactly the same as tracking the kinds of ideas that people contribute to something like Dell’s Ideastorm, the underlying dynamic is similar enough that reading The Two-Second Advantage made me realize that predictive technology might be the “missing piece” in crowdsourcing.