Archive for November, 2015

November 29th 2015

A Rogue Liberal: Halting NSA Bulk Data Collection Compromises Intelligence

By Eric Siegel

This Newsweek article, originally published in Newsweek’s opinion section and excerpted here, resulted from the author’s research for a new extended sidebar on the topic that will appear in 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 to delve more deeply on this topic.

 

I must disagree with my fellow liberals. The NSA bulk data shutdown scheduled for November 29 is unnecessary and significantly compromises intelligence capabilities. As recent tragic events in Paris and elsewhere turn up the contentious heat on both sides of this issue, I'm keenly aware that mine is not the usual opinion for an avid supporter of Bernie Sander (who was my hometown mayor in Vermont).

But as a techie, a former Columbia University computer science professor, I’m compelled to break some news: Technology holds the power to discover terrorism suspects from data—and yet to also safeguard privacy even with bulk telephone and email data intact. To be specific, stockpiling data about innocent people in particular is essential for state-of-the-art science that identifies new potential suspects.

I'm not talking about scanning to find perpetrators, the well-known practice of employing vigilant computers to trigger alerts on certain behavior. The system spots a potentially nefarious phone call and notifies a heroic agent—that's a standard occurrence in intelligence thrillers, and a common topic in casual speculation about what our government is doing. Everyone's familiar with this concept.

Rather, bulk data takes on a much more difficult, critical problem: precisely defining the alerts in the first place. The actual “intelligence” of an intelligence organization hinges on the patterns it matches against millions of cases—it must develop adept, intricate patterns that flag new potential suspects. Deriving these patterns from data automatically, the function of predictive analytics, is where the scientific rubber hits the road. (Once they’re established, matching the patterns and triggering alerts is relatively trivial, even when applied across millions of cases—that kind of mechanical process is simple for a computer.)

 

Newsweek Image croppedIt may seem paradoxical, but data about the innocent civilian can serve to identify the criminal. Although the ACLU calls it “mass, suspicionless surveillance,” this data establishes a baseline for the behavior of normal civilians. That is to say, law enforcement needs your data in order to learn from you how non-criminals behave. The more such data available, the more effectively it can do so.

Here's how it works. Predictive analytics shrinks the unwieldy haystack throughout which law enforcement must hunt for needles—albeit by first analyzing the haystack in its entirety. The machine learns from the needles (i.e., known perpetrators, suspects, and persons of interest) as well as the hay (i.e., the vast majority that is non-criminal) using the same technology that drives financial credit scoring, Internet search, personalized medicine, spam filtering, targeted marketing, and movie, music, and book recommendations. This automatic process generates patterns that flag individuals more likely to be needles, thereby targeting investigation activities and more productively utilizing the precious bandwidth of officers and agents. Under the right conditions, this will unearth terrorists who would have otherwise gone undetected.

This increasingly common practice also drives other crime fighting functions. Today's law enforcement organizations predictively investigate, monitor, audit, warn, patrol, parole, and sentence…

CONTINUE READING: Access the complete article in Newsweek, where it was originally published.

 

Eric Image 2015 croppedEric Siegel, Ph.D. is the founder of the Predictive Analytics World conference series—which covers both business and government deployment—the author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Revised and Updated Edition (Wiley, January 2016), and a former computer science professor at Columbia University.

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November 25th 2015

Wise Practitioner – Workforce Predictive Analytics Interview Series: Jonathon Frampton at Baylor Scott & White Health

By: Greta Roberts, Conference Chair Predictive Analytics World for Workforce 2016


In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, Visualizing Organizational Movement for Opportunity Identification, we interviewed Jonathon Frampton, Director, People Analytics & Reporting at Baylor Scott & Jonathon Frampton ImageWhite Health. View the Q-and-A below to see how Jonathon Frampton has incorporated predictive analytics into the workforce of Baylor Scott & White Health. Also, glimpse what’s in store for the new PAW Workforce conference.

Q: How is a specific line of business / business unit using your predictive decisions?  How is your product deployed into operations?

A: We are in a state of constant iteration/development with our deliverables and since most of our current work shows up in presentation form much of the final use is masked to us.  This is actually a point of current focus for our team, capturing our “#wins” as we call them when our data / inference are used for the greater good.  Much of our current deployment focus around the enablement of our HR business partners and directors.  This team has been very quick to run with our results and distribute them into the workforce as needed.

Q: If HR were 100% ready and the data were available, what would your boldest data science creations do?

A: I LOVE this question!  We focus on creating a more efficient experience for our people leaders, as such an area ready for an end to end predictive / prescriptive solution would handle the entire process how our workforce is staffed and how mobile employees are deployed.  A solution that would only create, approve and source positions as they become predictively necessary given parameters that include elements of productivity and employee satisfaction would free up our leaders time to focus on our patient care.  Taking it a step further allows for thinner more qualified slate of candidate to be presented in a timely manner, given that our recruiting force would know well ahead of time what pipelines need to be tapped and ready!

Q: When do you think businesses will be ready for "black box" workforce predictive methods, such as Random Forests or Neural Networks?

A: I am not sure that the businesses like the business of people will ever be 100% ready, or even that they should be.  It is one thing to set a model churning our trades at a nanoseconds pace, but can you imagine making a decision on someone’s future as quickly?  That being said, if the focus of workforce predictive analytics was less on the true HR work and focused on people enablement actions the adoption rate would be much quicker. 

Q: Do you have suggestions for data scientists trying to explain the complexity of their work, to those solving workforce challenges?

A: This is a great question and one I am sure many are grappling with.  Like many we are pretty young in this game, so I can speak to how we are currently seeing success in this area.  We have gone with an internal consultant model for our organization.  Our group does sit within HR, but we have an individual (consultant) wholly focused on taking the work of our analysts and educating, training and enabling our HR professionals to gain full value of it.  This has been a huge bonus for us as the street flows in both directions and has turned into a wonderful quantitative / qualitative feedback loop of their commentary flowing in our direction feeding increasingly relevant results to theirs.

Q: What is one specific way in which predictive analytics actively is driving decisions?

A: As we are a very young group we do not yet have any direct results from a predictive product driven decision as the majority of our deliverables have been inferential in nature allowing our business partners and operations teams to infer the predictive nature of the data.  This is by design as the idea of jumping from a finally standardized set of HR KPIs directly into predicting results can (and should) be quite overwhelming for our customers, however we are rolling out bits and pieces as I type so look for great things soon.

Q: How does business culture, including HR, need to evolve to accept the full promise of predictive workforce?

A: I think the (re)evolution has already begun, but it is far from over.  What we need is the consistent delivery of increasingly complex results from HR data.  I think we (HR) will follow the same trajectory seen by marketing in recent years with adoption following an exponential curve after hitting a “tipping point” a number of years in.  Additionally a strong focus on educating our operational customers on the uses and values of our people insights would speed adoption rates and create more of a pull effect.

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Don't miss Jonathon’s conference presentation, Visualizing Organizational Movement for Opportunity Identification, at PAW Workforce, on Monday, April 4, 2016, from 10:40 to 11:25 am. Click here to register for attendance

By: Greta Roberts, CEO, Talent Analytics, Corp. @gretaroberts and Conference Chair of Predictive Analytics World for Workforce.

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November 18th 2015

Wise Practitioner – Workforce Predictive Analytics Interview Series: Ben Waber of Humanyze

By: Greta Roberts, Conference Chair Predictive Analytics World for Workforce 2016


In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, Using Wearables and Big Data to Reinvent Management, we interviewed Ben Ben Waber imageWaber, CEO at Humanyze. View the Q-and-A below to see how Ben has incorporated predictive analytics into the workforce of Humanyze. Also, glimpse what’s in store for the new PAW Workforce conference.

Q: How is a specific line of business / business unit using your predictive decisions?  How is your product deployed into operations?

A: Global services divisions use our technology to create and test new workplaces.  Using a combination of wearables and digital data analytics, we identify what behaviors lead to higher performance and how new workplace designs will impact those behaviors.  Our customers then use our technology to A/B test these changes before rolling them out to the entire organization.

Q: If HR were 100% ready and the data were available, what would your boldest data science creations do?

A: Automate org charts, compensation, and hiring while putting HR in charge of setting up A/B tests to shape these systems.

Q: When do you think businesses will be ready for "black box" workforce predictive methods, such as Random Forests or Neural Networks?

A: These methods are another input to decision making, and with limited understanding they will be of limited usefulness.  As People Analytics divisions become the norm over the next 10 years, we'll see more advanced methods become more common.

Q: Do you have suggestions for data scientists trying to explain the complexity of their work, to those solving workforce challenges?

A: If your explanation wouldn't make sense to a random person on the street, you need to simplify.

Q:  What is one specific way in which predictive analytics actively is driving decisions?

A: Predicting what org charts will positively impact performance and retention.

Q: How does business culture, including HR, need to evolve to accept the full promise of predictive workforce?

A: Today companies make decisions about their people after an executive reads an article about what a cool company like Google does.  We need to move from that model to one closer to marketing: have a good idea, validate and predict with current data, test.

Q:  Do you have specific business results you can report?

A: We have used people analytics to improve loan sales in a major multinational bank by over 10%, over a billion euros in additional revenue a year.

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Don't miss Ben’s conference presentation, Using Wearables and Big Data to Reinvent Management, at PAW Workforce, on Monday, April 4, 2016 from 3:05-3:25 pm. Click here to register for attendance

By: Greta Roberts, CEO, Talent Analytics, Corp. @gretaroberts and Conference Chair of Predictive Analytics World for Workforce

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November 10th 2015

Most Swans are White: Living in a Predictive Society

By: Thomas H. Davenport

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, 2016preorder 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.

 

 

 

 

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