Primer on Big Data and Hiring: Chapter 6

This is the sixth chapter of a primer on big data and hiring. The structure of the primer is based on the following graphic created by Evolv, a company that provides "workforce optimization" services. Evolv was selected not because it is sui generis; rather, it is emblematic of numerous companies, from start-ups to well-established companies that market "workforce science" services to employers.

The Evolv graphic below is intended to illustrate the process of workforce science.

Chapter 6: Evaluate and Act

Analyzed Data Reveals Insights That Drive Workforce Performance and Retention

The Impact of Insights Are Quantified and Used to Inform Decision-making

As noted in a prior post, prejudice does not rise from malice or hostile animus alone. It may result as well from insensitivity caused by simple want of careful, rational reflection.

For example, take two insights from Evolv:

1. Living in close proximity to the job site and having access to reliable transportation—are correlated with reduced attrition and better performance; and
2. Referred employees have 10% longer tenure than non-referred employees and demonstrate approximately equal performance.

An employer confronted with these two insights might well determine that (i) applicants living beyond a certain distance from the job site (i.e., retail store) should be excluded from employment consideration and (ii) preference in hiring should be extended to applicants referred by existing employees.

Painting with the broad brush of distance from job site will result in well-qualified applicants being excluded, applicants who might have ended up being among the longest tenured of employees. Remember that the Evolv insight is a generalized correlation (i.e., persons living closer to the job site tend to have longer tenure than persons living farther from the job site). The insight says nothing about any particular applicant.

As a consequence, employers will pass over qualified applicants solely because they live (or don't live) in certain areas. Not only does the employer do a disservice to itself and the applicant, they increase the risk of employment litigation, with its consequent costs. How?

A recent New York Time article, "In Climbing Income Ladder, Location Matters," reads, in part:

Her nearly four-hour round-trip [job commute] stems largely from the economic geography of Atlanta, which is one of America’s most affluent metropolitan areas yet also one of the most physically divided by income. The low-income neighborhoods here often stretch for miles, with rows of houses and low-slung apartments, interrupted by the occasional strip mall, and lacking much in the way of good-paying jobs

The dearth of good-paying jobs in low-income neighborhoods means that residents of those neighborhoods have a longer commute. The 2010 Census showed that poverty rates are much higher for blacks and Hispanics. Consequently, hiring decisions predicated on distance, intentionally or not, discriminate against certain races.

Similarly, an employer extending a hiring preference to referrals of existing employees may be further exacerbating the discriminatory impact of its hiring process. Those referrals tend to be persons from the same neighborhoods and socioeconomic backgrounds of existing employees, meaning that workforce diversity, broadly considered, will decline.

With the huge amounts of "bad" data that get generated and stored daily, the failure to understand how to leverage the data in a practical way that has business benefit will increasingly lead to shaky insights and faulty decision-making, with significant costs to applicants, employees , employers  and society.