Big Data: The Rise of Talent Analytics is a Cause for Concern

December 6, 2016
Gail Lin

Back in 2012, forbes caused a stir when it published an article detailing how Target was tracking customer purchases so closely that it figured out a teen was pregnant before she had even told her parents. for many people, it was their first real glimpse of how big data – the ever-growing mass of our electronically-stored personal information – is being analyzed to predict behaviors and spot trends. The idea that a retailer could have such detailed knowledge of the most intimate aspects of our lives raised many concerns about how that information was being used.

Chief among those concerns was whether or not the rise of big data would lead to greater discrimination. Could someone, for example, be denied health insurance based on their grocery store purchases? What if they had their DN analyzed by a geological service out of a benign interest in tracing their ancestry? Could that be used against them in an entirely unforeseen way?

What about the workplace? Could big data play a role in employment decisions? The answer to this appears to be yes — and it’s already happening.

The Wall Street Journal recently detailed how “employee wellness firms and insurers are working with companies to mine data about the prescription drugs workers use, how they shop and even whether they vote, to predict their individual health needs and recommend treatments.” Companies can now, for example, pinpoint “which workers are at risk for diabetes and target them with personalized messages nudging them toward a doctor or services such as weight-loss programs.” As with the trend of encouraging workers to use fitness trackers, employers defend their actions by saying they use their analysis of big data to boost employee wellness and reduce health insurance costs.

Big data may also be worming its way into the decisions a company makes about hiring, promotions, and terminations. Employers are increasingly using what’s become known as “people analytics” to gain insights into both their current workforce as well as potential hires. By working with insurers and other third-party vendors that collect and scrutinize the personal data of workers, for example, companies can identify not only the demographics of successful employees but also seemingly unrelated traits and habits that predict their productivity and overall benefit to the employer’s bottom line. As noted in the story by The Wall Street Journal, people who vote in mid-term elections tend to be healthier, and those with a higher credit score are more likely to avoid a relapse after surgery because they will fill prescriptions and attend follow-up care appointments.

In this new era of big data, then, employers now have the unprecedented ability to connect previously very disparate details about current and previous employees and leverage what they find. At face value, this could be a good thing. Taking an interest in the health of workers is laudable, even if it is also a cost-cutting measure. Finding out what makes for a content, successful employee who fits with a company’s particular culture might remove the guesswork from recruiting and result in a happier workplace overall.

We should not, however, ignore the very real concerns raised by the darker side of big data, particularly if it is used, whether intentionally or not, to discriminate against potential employees or current workers.

The problem with relying on people analytics is that they are driven by algorithms designed to slice-and-dice data to extract specific information. for example, an algorithm might find a link between productive employees and the zip codes where they live. Harmless, right? The employer merely wants to know where the best pool of potential talent is located. But what if that algorithm is used to weed out job applicants solely based on their zip code? And what if the zip codes that don’t pass muster with the algorithm are neighborhoods with a high percentage of minorities living There? By relying on that algorithm an employer would remove minority workers entirely from consideration – probably without even realizing it. Discrimination is baked into the process and becomes structural.

There are also concerns for existing employees. Could their shopping or voting habits lead to termination because a computer predicts they will cost more to insure? If an algorithm determines a worker’s choice in entertainment during off-hours differs from what is most common among senior employees, will he or she be sidelined when it comes time for promotion? These are strong possibilities if an employer relies too heavily on big data in making HR decisions, and does not factor in any inherent bias that might be present in the analytical process.

The potential for increased discrimination because of big data has also caught the attention of the Equal Opportunity Employment Commission (EEOC). Jenny R. Yang, the agency’s Chair, recently hosted a public meeting about the rise of big data in HR that looked at both the potential benefits of talent analytics as well as the very real risks. “It is critical that these tools are designed to promote fairness and opportunity, so that reliance on these expanding sources of data does not create new barriers to opportunity,” she said in an EEOC press release on the forum. Speakers at the event raised concerns about how the use of big data by employers might violate several anti-discrimination laws, including the Americans with Disabilities Act and the Genetic Information Non-Discrimination Act.

Talent analytics represent a sea change in employment practices, one that will have both benefits and potentially serious drawbacks for employees. Used correctly, big data can result in happier, healthier workers. There remains a risk, however, that making employment decisions based entirely on the output of an algorithm may lead to discriminatory practices. This may be an unintended consequence but is no less a real possibility. Employers must ensure their use of talent analytics does not result in the exclusion of entire groups of employees from their workforces, or the unfair treatment of current workers.

At Outten & Golden, we are committed to staying on top of the use of big data by employers, as well as other ways in which technology can negatively impact employees.

(*Prior results do not guarantee a similar outcome.)