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Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die |  |
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Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die | |
"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer
"The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com
Award-winning | Used by over 30 universities | Translated into 9 languages
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.
Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections.
How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.
Predictive Analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.
In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction:
How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more.
A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.
作者简介ERIC SIEGEL, PhD, 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.
目录Foreword
Thomas H. Davenport xiii
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 xv
What is the occupational hazard of predictive analytics?
Introduction
The Prediction Effect 1
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) 17
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) 37
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) 67
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) 103
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) 133
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) 151
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) 187
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 218
Eleven Predictions for the First Hour of 2022
Appendices
A. The Five Effects of Prediction 221
B. Twenty Applications of Predictive Analytics 222
C. Prediction People—Cast of "Characters" 225
Notes 228
Acknowledgments 290
About the Author 292
Index 293
网友对Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die的评论
This is a great book on the Topic. What are you going to learn. Predictive analytics, which represents a data mining or statistical solution derived from techniques and algorithms that can be used with unstructured or structured data to arrive at outcomes, has been in use for some time. Indeed, the discipline has been in use with structured data for several decades. However, the visibility and subsequent market adoption of the discipline have increased significantly in recent years as computer power has increased. Processing memory and speed have increased at exponential rates, and this novel fact has been reported on by the media. For example, TIME magazine reported that the typical smartphone in 2012 had greater computing power than all the computers it took to send Apollo 11 to the moon in 1969. Furthermore, the cost of computing power has decreased as quickly as the speed and memory capabilities have increased. This revolution in computing capability has put predictive analytics in reach of mainstream business, as a predictive model can produce outcomes in minutes rather than days. In the past, businesses could not afford the computing power necessary to gather and interpret data that changed continuously in real time. This lack of cost effective options presented obstacles to integrating the output of a predictive model into the business process. Now, with the price per CPU decreasing and the computer power increasing, predictive analytics has become a practical, even necessary tool, for most organizations. Hope this helps, overall a great book, Eric Siegel great book we should talk sometime..:)
As time has gone by, I've found myself going back again and again to refer to specific points discussed in this book. It was a bit heavy at first, thick with facts that I found irritating and contradictory to certain favorite and closely held biases of mine, but over time, I could see his points better and better, in spite of myself.
Life isn't fair, and people certainly aren't. The ways that they react to things reflects this to a degree that would surprise even the coldest eyed cynic, and there it is- the thing that bothered me so much....but it's best if you face it. There are some pleasant discoveries in here too, but I think the most important aspect is illusion busting. Those sweet daydreams about how things should be, might be exactly what is holding you back.
Forewarned is forearmed, and the information in here is of a hefty caliber. Use it well.
Yes, I did actually buy this book, and it was worth every penny.
Predictive analytics, risk modeling, and other burgeoning fields based on Big Data are making a lot of people nervous these days--for good reason. But the beneficial applications of these methods can't be denied, and Eric Siegel makes a very persuasive case for those benefits in this surprisingly approachable and funny book, which packs a lot of interesting anecdotes and case studies. It's definitely not a textbook or technical manual. The book will appeal most to people who already have an interest in this area or who find any subject inherently fascinating if it's presented well.
Dr. Siegel seems to have written this book for those with limited math skills, but with a desire to better understand the techniques for extracting meaning from big data. Since this describes me, I found the book quite valuable and gave it my highest rating. If you already have a strong grasp of the tools for organizing and interpreting big data, the book will probably not meet your needs.
While the author writes well, the Introduction and first chapter skipped around on topics and anecdotes which caused me some initial concern. However, keep going because once past this early stage, the book gained traction quickly. In chapter 2, the author considers ethical concerns arising from predictive analysis. Target's analysis of a woman's buying patterns for pregnancy and Hewlett-Packard's analysis of its own employees for those that may quit both raise thought-provoking issues of whether such analyses are, to use the author's phrase - insight or intrusion. Using predictive analytics to prevent online fraud probably isn't as controversial.
The author describes the tools to undertake predictive analysis. Decision trees, ensembles, and ensembles of ensembles may all be used to draw meaning from data. He describes the IBM team's development of Watson for the famous contest on the game show "Jeopardy" when Watson beat two humans who had performed at championship levels on this show. The author details the challenges of natural language processing to enable a machine to derive meaning from spoken English. He goes through examples that illustrate the high-level challenge. The IBM team used the tools of ensembles of ensembles (read the book to understand this) coupled with statistical interpretation to determine the most likely correct answer to any question and to do so faster than the human contestants. This was machine learning at the currently highest level. One of the fascinating points is that art drives machine learning.
Can predictive analytics be employed to forecast an individual's actions? The thought seems troubling to me, but the possibility that prediction could be so used must be recognized. Which persons are most likely to favorably respond to a cell phone renewal offer as opposed to interpreting the offer as an opportunity to seek another carrier could have meaningful financial implications to a telecommunications carrier. He describes a predictive modeling undertaken by Oregon to predict which potential parolee is more likely to commit another crime if released from prison. This, too, has real world implications for the potential parolee and society. Once again, predictive analytics challenged me on many levels.
Dr. Siegel identifies five effects of prediction. These are: (1) the prediction effect; (2) the data effect; (3) the induction effect; (4) the ensemble effect; and (5) the persuasion effect. I encourage you to read the book to learn about these effects and consider their potential cumulative "effect" on society, for good and ill.
In respect of full disclosure I have known Eric for years in his capacity as founder of the Predictive Analytics World conference, and in my work in data mining and predictive analytics. That having been said, this is an excellent book for anyone who wants to learn what predictive analytics is, and how predictive analytics may be deployed across a wide range of disciplines. If you are looking for a hardcore set of algorithms or code examples this is not the book for you, and other reviewers have commented on that. I don't think that was the point of Eric's work. Eric's work does provide a review of what I think are the main pillars of predictive analytics; data, modeling, ensembles, uplift, unstructured data, deployment and ethics. If I had an issue with this book it would be in the ordering of the chapters, but, that is my personal view, and I can see why the book was structured the way that it was. The book will help you understand the major themes of predictive analytics, written in a way that let's the reader focus on the outcome, the advantages and the possibilities around predictive analytics. It is an 'easy' read yet still contains valuable insights. If you want to understand what people are talking about when they are talking about predictive analytics, read this book.
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