Big Data for Employment Verification

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As former President Ronald Reagan once said, "trust but verify." The phrase certainly rings true when it comes to employment verification. Will a candidate lie on a resume in order to get an unfair edge?

According to a 2014 survey by CareerBuilder, 58 percent of hiring managers have caught candidates lying on a resume. The survey shows that the range topics about which a candidate lies is equally impressive, from misrepresenting skill sets (57 percent) to fabricating academic degrees (33 percent) and even falsifying employment history (26 percent).

"One of the reasons candidates may feel okay embellishing their resumes is that they don't realize hiring managers are actually following up to verify the claims they make on their resumes," explained CareerBuilder spokeswoman Mary Lorenz to Money magazine, You Won't Believe How Many People Lie on Their Resumes. Liars don't expect to get caught — this is where a strong employment verification process, one backed by big data, can help protect you from expensive hiring mistakes.

Using Big Data to Verify

Systems collect more and more information about everyone, from credit reports to court records to criminal backgrounds. When it comes to using available data in decision-making, the data's pure, unfiltered volume can overwhelm you. But companies are increasingly turning to big data, often from outside partners who specialize in conducting employee screening, to cross-check a candidate's claims against all available data about the candidate.

Let's look at the biggest example of big data used for employment verification. The U.S. Citizenship and Immigration Services (USCIS) has built an online verification system called E-Verify to help employers determine if a new hire is eligible to work in the United States. More than 600,000 employers use E-Verify, which pulls data from multiple government databases. User satisfaction with E-Verify is high.

Risks of Using Big Data for Verification

As data proliferates alongside the tools necessary to analyze, it's becoming increasingly more popular to use big data to verify candidates' backgrounds and experiences. But the practice is not without its risks. Data can get stale with age, and it may reflect hidden biases or simply be inaccurate. Identify theft is also a growing concern. Big data can present challenges around issues of data privacy, as well.

Organizations must also ensure their use of big data is not perceived as discriminatory hiring practices. The U.S. Federal Trade Commission (FTC) issued a recent report that highlighted some of these big data-associated risks, Big Data: A Tool for Inclusion or Exclusion? Understanding the Issues. In the area of verification, FTC Chairwoman Edith Ramirez recognizes the many benefits of big data but warns that "businesses must ensure that their Big Data use does not lead to harmful exclusion or discrimination."

Ensure Fairness When Verifying

How can companies increase impartiality in their use of big data for employment verification? According to the FTC, organizations should consider

  1. Whether their underlying data sets are missing important information, and the impact of those omissions. If certain demographics are either over-represented or underrepresented, bias or unfairness can become an issue.
  2. If biases are incorporated at both the collection and analysis stages. If so, steps should be taken to recognize and overcome them.
  3. Ethical and fairness concerns when using big data, especially when it comes to hiring, where there are regulations to eliminate discrimination. The FTC report points to the example of a company using big data analytics and finding a correlation between a candidate's long commute time and low engagement. While the correlation may be accurate, is it fair to deny employment opportunity to an otherwise qualified candidate based on their distance from a work site?

Big data could transform the employee verification and hiring processes. While you move forward to take advantage of big data's many benefits, be aware of the risks involved. Analytics may be a great tool, but it's not risk-free either.