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Can Big Data Improve Recruiting and Background Checks?

February 1, 2017

If ‘data is the new oil,’ a variety of effort must be made to drill and refine the crude oil of big data in order to realize value. No value comes from simply having large volumes of data. Analytics are one key component required.

Analyzing big data can bring tremendous value to a wide variety of industries. As consumers, most of us will have appreciated suggested products from Amazon or suggested programming from Netflix. London-based Mastodon C applies big data to city and local government planning in order to benefit society. Recruiting and background checks are certainly among the industries that can be improved by big data.

The Value of Referrals

Personal relationships retain an important place in business. “It’s not what you know, it’s who you know,” is still applicable. While sites like LinkedIn and Facebook enable us to easily stay connected, relationships are still at the core of the platforms. Hiring someone recommended by a current valued employee is always advantageous. But, of course, this recruitment channel has its limitations.

Applying Background Check Info to Improve Hiring

To many workers, “background checks” represent official pre-screenings required to receive a job offer. Specifically, they often entail a credit check, criminal background check, and drug test. These screenings may add value. Although as stated on “Last Week Tonight” with John Oliver, many Americans with poor credit scores are actually struggling with medical debt. In light of this fact, the supposition that those with poor credit have poor financial management skills or are generally irresponsible is highly questionable.

In any case, people worldwide are creating digital footprints literally before they’re born. Surely this data can be applied to greater use in both recruitment and background checks.

Using Big Data to Recruit

The data is out there, so how can we apply it? Online DIY background check websites provide instant results for a very low cost. However, the majority are not Fair Credit Reporting Act (FCRA)-compliant, and often provide incomplete or inaccurate results.

Weed out and Weed In

In their simplest forms, background checks can serve to merely eliminate certain candidates from consideration. However, big data contains not just a high volume of data, but a great degree of variety. (Velocity happens to be the 3rd ‘V’ of big data.) This variety enables employers to create a richer picture of candidates and employees alike, enabling development of robust hiring strategies. In other words, you can not merely ‘weed out’ bad candidates, but ‘weed in’ candidates to the positions they’re best suited for.

Analyzing Digital Footprints

Kelly Trindel, PhD, Chief Analyst, Office of Research, Information and Planning, EEOC, concurs with my assertion that analytics are necessary to realize value from big data. In an EEOC meeting last October, which focused on the use of big data in hiring and other employment decisions, Trindel stated, “In the employment context, I would define big data as follows: big data is the combination of nontraditional and traditional employment data with technology-enabled analytics to create processes for identifying, recruiting, segmenting, and scoring job candidates and employees.”

Trindel continues to state that value is gained from linking these various data points, potentially even enabling predictions of future behavior or seeking out passive job candidates. The mention of “nontraditional data” is particularly interesting and alludes to the variety of big data.

More Data is Not Always Better

According to the National Association of Professional Background Screeners, there are many different databases used to conduct background checks on individuals. The most commonly cited is the FBI database, which is a collection of different systems organized under the National Crime Information Center.

“An employer might learn from a person's Facebook page that they belong to a particular religious group or have a disability that is not visually apparent," said Mark Briggs of the Arizona-based Briggs Law Group. “Knowing that information can open up an employer to liability,… and once you know something, you can be accused of considering that information illegally when making the hiring decision." Coming across such information is also a risk when conducting your own searches online.

What Databases Are Used For The Different Kinds of Background Checks?

Big Data = More and Less Discrimination

With the advancement of computer scoring and algorithm refinement, human judgment in employment decision making may be reduced or even made unnecessary, minimizing intentional discrimination.

Michal Kosinski, Assistant Professor, Organizational Behavior, Stanford Graduate School of Business, states, “Importantly, Big Data models aimed at recruitment and performance appraisal are likely to disproportionately benefit groups that are currently the most discriminated against.”[MB4]

Trindel gives a very different hypothetical example of a Silicon Valley tech company utilizing an algorithm to assist in hiring new employees who 'fit the culture.' This may cause an adverse impact, by screening out women and older workers. However, if an employer actively seeks candidates who ‘fit,’ it should stand to reason that they’re going to reduce diversity – by hiring people who match the existing workforce. In this example, the goal was the cause of the problem, not the use of an algorithm. [MB5]

Balance with Common Sense

Rather than replacing human judgement entirely, big data analytics need to be guided by humans and balanced with common sense. Another reason for caution is algorithms seem to uncover relationships among variables that are largely correlational in nature.

“The fact that computers are playing a bigger role in the hiring process causes some trepidation, but it's important to realize that these algorithms aren't meant to replace recruiters. They're simply intended to arm recruiters with more information, which they can use to make a more informed decision,” Michael Housman, Workforce Scientist, hiQ Labs.

“Employers who choose to purchase or adopt these strategies must be warned to not simply 'trust the math' as the math in this case has been referred to, by at least one mathematician/data scientist, as an 'opinion formalized in code,'” stated Trindel. The current lack of research further drives the need for caution. Eric M. Dunleavy of DCI Consulting states, “This is a complex topic, and there is still little research on big data tools along a number of important dimensions for HR practitioners, and little precedent from EEO matters.”

Barriers to Entry

Fortune 100 companies with hundreds of thousands of employees worldwide can afford to apply millions of dollars to the recruitment process. A slight reduction in turnover rate can yield a positive ROI, making a massive technology investment a profitable venture. Unfortunately, small and even medium-sized businesses simply can’t justify high expenditures, making some solutions inaccessible.

A recent survey conducted by SHRM confirms this disparity. 32% of HR professionals reported that their organization uses big data to support HR, but those in larger organizations were nearly twice as likely to use big data.

Software-as-a-service (SaaS) and cloud computing have helped level the playing field between enterprises of various sizes. With cloud computing, you pay for only the computing power you use. SaaS apps enable small businesses to use sophisticated software without the need to install and maintain it. Salesforce.com, Gmail, and Dropbox are just a few examples of popular SaaS apps.

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