Share with your friends










Submit

Analytics Magazine

Predictive Analytics – Most swans are white: Living in a predictive society

May/June 2014

Thomas H.  DavenportBy Thomas H.  Davenport

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.

Thomas H. Davenport (www.tomdavenport.com) is a visiting professor at Harvard Business School, the President’s Distinguished Professor at Babson College, co-founder of the International Institute for Analytics and the co-author of “Competing on Analytics” and several other books on analytics. He is a member of INFORMS. 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” (February 2013) by Eric Siegel and is reprinted for promotional considerations.

business analytics news and articles

 

Analytics Blog

Electoral College put to the math test


With the campaign two months behind us and the inauguration of Donald Trump two days away, isn’t it time to put the 2016 U.S. presidential election to bed and focus on issues that have yet to be decided? Of course not.


Save



Headlines

Study: Salaries for early career data scientists decrease for first time

Salaries for early career data scientists decreased year over year for the first time in four years as did the percentage of early career data scientists with a Ph.D. while demand for data scientists continued to increase, according to a recently released Burtch Works’ 2017 salary study of data scientists. Salaries for more experienced data scientists generally held steady or increased slightly depending on an individual’s focus area, responsibility and geographic base, according to the report. Read more →

Generous health insurance plans encourage overtreatment, but may not improve health

Offering comprehensive health insurance plans with low deductibles and co-pay in exchange for higher annual premiums seems like a good value for the risk averse, and a profitable product for insurance companies. But according to a forthcoming study in a leading scholarly marketing journal, the INFORMS journal Marketing Science, such plans can encourage individuals with chronic conditions to turn to needlessly expensive treatments that have little impact on their health outcomes. This in turn raises costs for the insurer and future prices for the insured. Read more →

UPCOMING ANALYTICS EVENTS

INFORMS-SPONSORED EVENTS

CONFERENCES

2017 INFORMS Healthcare Conference
July 26-28, 2017, Rotterdam, the Netherlands

CAP® EXAM SCHEDULE

CAP® Exam computer-based testing sites are available in 700 locations worldwide. Take the exam close to home and on your schedule:


 
For more information, go to 
https://www.certifiedanalytics.org.