Covering Disruptive Technology Powering Business in The Digital Age

Home > DTA news > News > Big Issues Surrounding Big Data
Big Issues Surrounding Big Data
November 4, 2016 News big data Big Issues HR


Big data now influences how organizations make hiring and other employment decisions, but experts warn that, as its scope and usage increase, so will the HR challenges involved.

While the growing use of so-called big data could cause potential legal problems for employers, experts in the area of employment law and data privacy agree that, despite those risks, it offers such a compelling competitive edge that getting it right needs to be a critical HR objective.

Government officials and legal experts, in fact, discussed and debated the pros and cons of big data in the workplace at a recent Equal Employment Opportunity Commission public meeting in Washington.

Big data, which has been loosely defined as deploying mathematical algorithms, “data scraping” of the Internet, or other ways of analyzing complex information, is now part of hiring and other employment decisions. With that, both its use and scope are expected to grow exponentially, a panel of industrial psychologists, attorneys and labor economists told attendees at the EEOC meeting.

“Big data has the potential to drive innovations that reduce bias in employment decisions and help employers make better decisions in hiring, performance evaluations and promotions,” EEOC Chair Jenny R. Yang told meeting attendees. “At the same time, 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.”

“It can be a challenge to determine whether, when and how laws may apply in our increasingly technology-driven workplaces,” added EEOC Commissioner Victoria A. Lipnic at the meeting. “But I see this at the core of our responsibilities: ensuring that our understanding of today’s workplaces and our interpretation and administration of the law are as current and fully informed as possible.”

One of the EEOC panelists, Marko Mrkonich, a shareholder in the Minneapolis office of Littler, says the incumbent system used in recruiting, for example, already has obvious problems in terms of fairness. And while big data also could present hurdles, HR leaders and employers should prefer the technology-fueled alternative.

“Big data can eliminate existing major problems, and while it may create some new unexpected issues, we’re making progress,” he says. “I think that’s the right way to look at this.”

Christopher DeGroff, a partner in Seyfarth Shaw’s Chicago office, says the EEOC’s analysis of employer use of big data — especially in hiring — is particularly timely. He adds that the EEOC recently published its 2017-2021 Strategic Enforcement Plan, a blueprint for how the EEOC will structure its enforcement of EEO laws in the next five years.

The agency’s substantive priorities include, he says, “eliminating ‘barriers in recruitment and hiring’ and ‘addressing selected emerging and developing issues.’ The use of big data in hiring fits squarely within the intersection of these two topics.”

DeGroff says the use of big data is particularly tricky for employers because problematic results could crop up from what appear to be benign and objective criteria. The EEOC has, over the last several years, targeted programs that appear to be facially neutral, but have a disproportionate impact on a protected group — also known as a “disparate-impact” theory, he says. With that, he offers some steps employers might take to avoid potential big-data landmines.

For example, one step would be to analyze how particular algorithms affect candidates and/or employees — essentially conducting a “test run” of the selection tools to determine how they play out in real life. This may expose unintended flaws in the selection criteria. Importantly, however, HR leaders should conduct this sort of test run in close partnership with counsel to ensure these preliminary analyses are privileged.

“In some cases, a deeper dive could be useful, engaging disciplines like industrial/organizational psychologists to kick the tires on any proposed plans to validate an actual connection to job-related outcomes,” he says.

“It is an exciting, perhaps even revolutionary time for employers, but the old adage of ‘measure twice, cut once’ still applies to these big data tools,” DeGroff says.

Mrkonich says that gauging the disparate-impact issue is one of HR’s main big-data challenges.

“The nature of the correlation and the amount of information makes it very challenging to use old thought processes to determine whether something that is facially neutral is discriminatory,” he says. “In its purest form, big data is a way to eliminate discrimination because subjective biases and other related factors can be made to disappear.”

Mrkonich cites the example of “me too” bias in job interviews — when applicants who remind interviewers of themselves for one reason or another receive preferential treatment — as an ongoing problem that can be eliminated using this technology application.

“Big data doesn’t care about all that,” he says. “The key question is how you use the power of good without getting it subtracted or diverted into something not good.”

Naysayers and critics focused on big data and potential privacy concerns aside, Mrkonich says, the critical question is whether employers take reasonable steps to control abuses and take advantage of progress. Whether it’s Major League Baseball, business supply-chain management or marketing, he says, big data works.

“Employers can’t simply sit back and say it’s potentially risky,” he says. “Of course, there are potential privacy issues on the horizon and disparate-impact discrimination claims that we have to worry about. But you can’t stop progress.”

While the legal parameters involved in using big data in HR are still in a formative stage, Mrkonich says, employers moving into big data or extending its use must ensure they have addressed and considered privacy concerns, and received appropriate consent from employees and job applicants.

Another best practice Mrkonich cites is to carefully select any big-data vendor. Most of all, he says, HR should work with vendors that are clear about who is bearing specific risks and responsibilities. If, for instance, an employer is challenged on applicant-selection criteria based on an algorithm, will the vendor disclose the algorithm — a potential trade secret — in order to help an employer defend itself in a lawsuit?

“Risks aside, this is going to be a dominant topic in HR as we move forward,” he says. “The ship is sailing.”

If you factor in the generational shift in attitude toward work among younger employees, he adds, employers will need that equivalent of supply chain management to work for them.

“The so-called ‘Uberization’ of the workforce is happening everywhere,” Mrkonich says, noting that a 27-year-old may not want to work a predictable, standard, 40-hour week, because the weather is nice and he or she might want to go ride his or her mountain bike instead. Or he or she may want to go to a great concert during the week in another city, and will be willing to work on the weekend.

“How do you manage that type of workforce without using big data?” Mrkonich says.

This article was originally published on and can be viewed in full here