Big Data: A New Way to Catch a Thief

Big data brainstorm
New research, based on big data, is proven to be a highly accurate—and certainly unique—means for catching a car thief: quantify and tabulate a car owner’s posterior.

Since Aristotle there have many efforts to come up with new ways to understand and predict human behavior. But over just the past few centuries, we’ve begun to “mathematize” the subjective. We’ve tried to predict human behavior by means of (little) data.

And that’s been our primary approach to understanding ourselves until. . .  big data.

What’s different about big data is that it doesn’t attempt to explain why people are doing things and develop a theory about the why. Big data is never about the why. It’s concerned with the what. So big data just gathers huge amounts of information, identifies patterns and estimates probabilities about how people will act in various situations.

Big data differs from previous attempts to understand how to do things in three different ways:

–instead of using small samples that statisticians have used for more than a century, it uses a lot of data

–instead of using unspoiled, clean, pristine data, it accepts messiness

–instead of attempting to understand causes, big data is just interested in correlations

Back-end operations
Professor Shigeomi Koshimizu, a professor at the Tokyo Advanced Institute of Industrial Technology, is a student of the art and science of people’s posteriors—their butts. You wouldn’t think that the way a person sits in a car provides unerring information, but it can. Prof. Koshimizu and his engineers convert backsides into data by measuring their pressure impact at 360 different points. That’s right. He uses sensors placed in a car seat and indexes each point on a scale of zero to 256. The result? A digital code unique to each individual and 98% accurate in distinguishing people

Here’s Koshimizu’s use of this big data science, quoted in aForeign Affairs article:

The research is not asinine. Koshimizu’s plan is to adapt the technology as an antitheft system for cars. A vehicle equipped with it could recognize when someone other than an approved driver sat down behind the wheel and could demand a password to allow the car to function. Transforming sitting positions into data creates a viable service and a potentially lucrative business. And its usefulness may go far beyond deterring auto theft. For instance, the aggregated data might reveal clues about a relationship between drivers’ posture and road safety, such as telltale shifts in position prior to accidents. The system might also be able to sense when a driver slumps slightly from fatigue and send an alert or automatically apply the brakes. Is that cool or not?

A warning
Be aware that what’s unique about big data is that it’s empirical, not narrative. You just have mathematized data, not narratives as in the typical psych or economic research. As David Brooks has written, you know what to pay attention to, but you don’t really know how to intervene. For example, the data may tell you that over the past 10 years your calculus students have all stumbled on the 4th exam, but you’ve got to get back into the world of personal responsibility and advise students to do X because it will cause Y. So when we adopt this tool, we’ll need an appreciation of its limitations as well as its power.

Big data is a resource and a tool meant to inform, not to explain. It takes aim on understanding, but it can still lead to misunderstanding. That’s a significant contrast to our best  20th century research tool.   All in all, this says that we need to understand its limitations as well as its power.

Flickr photo by: kevin krejci

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