The New Power: Data?
Let's take a look at the power of data, specifically predictive machines that can predict employee retention, crime, fraud, and even pregnancy.
Join the DZone community and get the full member experience.Join For Free
With Power Comes Responsibility
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? An extended sidebar on fraud detection addresses the question: How does machine intelligence flip the meaning of fraud on its head?
What would happen if your boss was notified that you’re allegedly going to quit even though you had said this to no one? If you are one of the numbers who works at your reported organization, your employer has tagged you — and all your colleagues — with a “Flight Risk” score. This simple number foretells whether you’re likely to leave your job. As an employee, there’s a good chance you didn’t already know that.
Prediction snoops into your private future. These cases involve the corporate deduction of previously unknown, sensitive facts: Are you considering quitting your job? Are you pregnant? This isn’t a case of mishandling, leaking, or stealing data. Rather, it is the generation of new data, the indirect discovery of unvolunteered truths about people. Organizations predict these powerful insights from existing innocuous data as if creating them out of thin air. Are they equipped to manage their own creation?
These are few of the questions that will calibrate you to think on the Power of Predictive Modeling.
Below are a few strong things that marketers do not care about directly, but that could be a strong predictor of a wider shopping market and its needs.
1. What’s predicted: Which employees will quit.
2. What’s done about it: Managers take the predictions for those they supervise into consideration, at their discretion. This is an example of decision support rather than feeding predictions into an automatic decision process.
1. What’s predicted: The location of a future crime.
2. What’s done about it: Police patrol the area.
1. What’s predicted: Which transactions or applications for credit, benefits, reimbursements, refunds, and so on are fraudulent.
2. What’s done about it: Human auditors screen the transactions and applications predicted most likely to be fraudulent.
Network Intrusion Detection
1. What’s predicted: Which low-level Internet communications originate from imposters.
2. What’s done about it: Block such interactions.
1. What’s predicted: Which email is spam.
2. What’s done about it: Divert suspected emails to your spam folder.
1. What’s predicted: Which female customers will have a baby in the coming months.
2. What’s done about it: Market relevant offers for soon-to-be parents of newborns.
Pregnancy prediction faces the opposite dilemma that is faced by crime prediction. Crime prediction causes damage when it predicts wrong, but predicting sensitive facts like pregnancy can cause damage when it’s right. Like X-ray glasses, predictive analytics unveils new hot-button data elements for which all the fundamental data privacy questions must be examined anew. No one wants her pregnancy unwittingly divulged; it’s safe to assume organizations generally don’t wish to divulge it either.
Google itself appears to have sacrificed a significant boon from predictive modeling in the name of privacy by halting its work on the automatic recognition of faces within photographs. When he was Google’s CEO, Eric Schmidt (currently Google’s executive chairman) stated his concern that face recognition could be misused by organizations that identify people in a crowd. This could, among other things, ascertain people’s locations without their consent. He acknowledges that other organizations will continue to develop such technology, but Google chooses not to be behind it.
It’s not what an organization comes to know; it’s what it does about it. Inferring new, powerful data is not itself a crime, but it does evoke the burden of responsibility. Target does know how to benefit from pregnancy predictions without actually divulging them to anyone (the alleged story of the pregnant teen is at worst an individual yet significant gaffe). But any marketing department must realize that if it generates quasi-medical data from thin air, it must take on, with credibility, the privacy and security practices of a facility or department commonly entrusted with such data. You made it, you manage it.
Predictive analytics is an important, blossoming science. Foretelling your future behavior and revealing your intentions is an extremely powerful tool and one with significant potential for misuse. It needs to be managed with extreme care. The agreement we collectively come to for PA’s position in the world is central to the massive cultural shifts we face as we fully enter and embrace the information age.
Opinions expressed by DZone contributors are their own.