Continued Privacy Worries That Could Eradicate Predictive Analytics
And there's the now-famous case of Target which, in 2012, noticed a correlation between certain products being purchased and pregnancy and, therefore, started marketing baby- and pregnancy-related products to those consumers as soon as that purchase behavior began. It seemed like a good idea at the time, but it freaked out a lot of people, especially those who hadn't yet mentioned the impending parenthood to their families, significant others or employers.
Eric Siegel, executive editor of Predictive Analytics Times, refers to these as "unvolunteered truths," and says they're the source of the head-on collision that this critical area of statistical science is about to experience. Why? Because jillions of these data points are collected and synthesized every day, yielding one customized, predictive score after another. The problem is that this is done without the data subject's knowledge or consent and without the individual's ability to either correct errors or opt out of the process. As such, those data privacy regulators and watchdogs are turning their attention toward predictive analytics.
Advocates of predictive analytics — which I generally consider myself to be — remind us that alternative scores aren't specific to a single person, and are instead an aggregated pattern of documented facts from many individuals that, when combined in certain ways, yield a likelihood of some future behavior. However, for some, it might not feel that way when you're the one receiving the maternity wear promotions and you've yet to disclose your pregnancy — to anyone.
I'm not casting aspersions. My company developed proprietary alternative scores to compare and contrast entire audiences for direct marketers, uncovering previously unconsidered matches between specific promotions and the thousands of audiences that marketers can choose from for email and other direct marketing campaigns.
As we built those tools, we looked closely at the tenets espoused by IBM's Jeff Jonas, who has spent years baking in data privacy guards into its SPSS Modelers software. An inspirational figure in the field, Jeff is elevating the discipline by making infraction-proof systems, which is the template used for our tools — our systems never bring in any individual audience member's personally identifiable information. So for now, we like where we sit in the privacy spectrum, and we remain hopeful that the science of predictive analytics can continue to grow and thrive, while the industry creates reasonable oversight and privacy protections.