About the Book

An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics works and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie or die.

About the Author

Eric Siegel is the founder of Predictive Analytics World and executive editor of The Predictive Analytics Times. A former Columbia University professor, he is a renowned speaker, educator and leader in the field.

Table of Contents:
Foreword Thomas H. Davenport Preface to the Revised and Updated Edition What's new and who's this book for--the Predictive Analytics FAQ Preface to the Original Edition What is the occupational hazard of predictive analytics? Introduction The Prediction Effect How does predicting human behavior combat risk, fortify healthcare, toughen crime fighting and boost sales? Why must a computer learn in order to predict? How can lousy predictions be extremely valuable? What makes data exceptionally exciting? How is data science like porn? Why shouldn't computers be called computers? Why do organizations predict when you will die? Chapter 1 Liftoff! Prediction Takes Action (deployment) How much guts does it take to deploy a predictive model into field operation and what do you stand to gain? What happens when a man invests his entire life savings into his own predictive stock market trading system? Chapter 2 With Power Comes Responsibility: Hewlett-Packard, Target, the Cops and the NSA Deduce Your Secrets (ethics) How do we safely harness a predictive machine that can foresee job resignation, pregnancy and crime? Are civil liberties at risk? Why does one leading health insurance company predict policyholder death? Two extended sidebars reveal: 1) Does the government undertake fraud detection more for its citizens or for self-preservation, and 2) for what compelling purpose does the NSA need your data even if you have no connection to crime whatsoever and can the agency use machine learning supercomputers to fight terrorism without endangering human rights? Chapter 3 The Data Effect: A Glut at the End of the Rainbow (data) We are up to our ears in data. How much can this raw material really tell us? What actually makes it predictive? What are the most bizarre discoveries from data? When we find an interesting insight, why are we often better off not asking why? In what way is bigger data more dangerous? How do we avoid being fooled by random noise and ensure scientific discoveries are trustworthy? Chapter 4 The Machine That Learns: A Look Inside Chase's Prediction of Mortgage Risk (modeling) What form of risk has the perfect disguise? How does prediction transform risk to opportunity? What should all businesses learn from insurance companies? Why does machine learning require art in addition to science? What kind of predictive model can be understood by everyone? How can we confidently trust a machine's predictions? Why couldn't prediction prevent the global financial crisis? Chapter 5 The Ensemble Effect: Netflix, Crowdsourcing, and Supercharging Prediction (ensembles) To crowdsource predictive analytics--outsource it to the public at large--a company launches its strategy, data and research discoveries into the public spotlight. How can this possibly help the company compete? What key innovation in predictive analytics has crowdsourcing helped develop? Must supercharging predictive precision involve overwhelming complexity, or is there an elegant solution? Is there wisdom in nonhuman crowds? Chapter 6 Watson and the Jeopardy! Challenge (question answering) How does Watson--IBM's Jeopardy!-playing computer--work? Why does it need predictive modeling in order to answer questions, and what secret sauce empowers its high performance? How does the iPhone's Siri compare? Why is human language such a challenge for computers? Is artificial intelligence possible? Chapter 7 Persuasion by the Numbers: How Telenor, U.S. Bank and the Obama Campaign Engineered Influence (uplift) What is the scientific key to persuasion? Why does some marketing fiercely backfire? Why is human behavior the wrong thing to predict? What should all businesses learn about persuasion from presidential campaigns? What voter predictions helped Obama win in 2012 more than the detection of swing voters? How could doctors kill fewer patients inadvertently? How is a person like a quantum particle? Riddle: What often happens to you that cannot be perceived, and that you can't even be sure has happened afterward--but that can be predicted in advance? Afterword Eleven Predictions for the First Hour of 2022 Appendices A. The Five Effects of Prediction B. Twenty Applications of Predictive Analytics C. Prediction People--Cast of "Characters" Notes Acknowledgments About the Author Index