Most Swans are White: Living in a Predictive Society

In anticipation of the forthcoming Revised and Updated, paperback edition of Eric Siegel’s Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (coming January 6, 2016—preorder today), read here its Foreword by Thomas Davenport, which reviews the book and puts a revealing perspective on the topic.

Eric Siegel’s book—Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die—deals with quantitative efforts to predict human behavior. One of the earliest efforts to do that was in World War II. Norbert Wiener, the father of “cybernetics,” began trying to predict the behavior of German airplane pilots in 1940—with the goal of shooting them from the sky. His method was to take as input the trajectory of the plane from its observed motion, consider the pilot’s most likely evasive maneuvers, and predict where the plane would be in the near future so that a fired shell could hit it. Unfortunately, Wiener could predict only one second ahead of a plane’s motion, but 20 seconds of future trajectory were necessary to shoot down a plane.

In Siegel’s book, however, you will learn about a large number of prediction efforts that are much more successful. Computers have gotten a lot faster since Wiener’s day, and we have a lot more data. As a result, banks, retailers, political campaigns, doctors and hospitals, and many more organizations have been quite successful of late at predicting the behavior of particular humans. Their efforts have been helpful at winning customers, elections, and battles with disease.

My view—and Siegel’s, I would guess—is that this predictive activity has generally been good for humankind. In the context of healthcare, crime, and terrorism, it can save lives. In the context of advertising, using predictions is more efficient, and could conceivably save both trees (for direct mail and catalogs) and the time and attention of the recipient. In politics, it seems to reward those candidates who respect the scientific method (some might disagree, but I see that as a positive).

However, as Siegel points out—early in the book, which is admirable—these approaches can also be used in somewhat harmful ways. “With great power comes great responsibility,” he notes in quoting Spider-Man.  The implication is that we must be careful as a society about how we use predictive models, or we may be restricted from using and benefiting from them. Like other powerful technologies or disruptive human innovations, predictive analytics is essentially amoral, and can be used for good or evil. To avoid the evil applications, however, it is certainly important to understand what is possible with predictive analytics, and you will certainly learn that if you keep reading.

This book is focused on predictive analytics, which is not the only type of analytics, but the most interesting and important type. I don’t think we need more books anyway on purely descriptive analytics, which only describe the past, and don’t provide any insight as to why it happened. I also often refer in my own writing to a third type of analytics—“prescriptive”—that tells its users what to do through controlled experiments or optimization. Those quantitative methods are much less popular, however, than predictive analytics.

This book and the ideas behind it are a good counterpoint to the work of Nassim Nicholas Taleb. His books, including The Black Swan, suggest that many efforts at prediction are doomed to fail because of randomness and the inherent unpredictability of complex events. Taleb is no doubt correct that some events are black swans that are beyond prediction, but the fact is that most human behavior is quite regular and predictable.  The many examples that Siegel provides of successful prediction remind us that most swans are white.

Siegel also resists the blandishments of the “big data” movement. Certainly some of the examples he mentions fall into this category—data that is too large or unstructured to be easily managed by conventional relational databases. But the point of predictive analytics is not the relative size or unruliness of your data, but what you do with it. I have found that “big data often equals small math,” and many big data practitioners are content just to use their data to create some appealing visual analytics. That’s not nearly as valuable as creating a predictive model.

Siegel has fashioned a book that is both sophisticated and fully accessible to the non-quantitative reader. It’s got great stories, great illustrations, and an entertaining tone. Such non-quants should definitely read this book, because there is little doubt that their behavior will be analyzed and predicted throughout their lives. It’s also quite likely that most non-quants will increasingly have to consider, evaluate, and act on predictive models at work.

In short, we live in a predictive society. The best way to prosper in it is to understand the objectives, techniques, and limits of predictive models. And the best way to do that is simply to read Siegel’s book. 

Author Bio:

Thomas H. Davenport is the President’s Distinguished Professor at Babson College, a fellow of the MIT Center for Digital Business, Senior Advisor to Deloitte Analytics, and cofounder of the International Institute for Analytics. He is the coauthor of Competing on Analytics, Big Data @ Work, and several other books on analytics. This Foreword by Professor Davenport is excerpted with permission of the publisher, Wiley, from Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Revised and Updated edition, January 2016) by Eric Siegel.