The age of analytics is upon us. As Andrew Leonard details over at Salon, the old ways of interviewing job candidates and evaluating prospective employees based on a healthy mix of gut instinct, signaling, and the good old human touch is well on its way out the door, displaced by the rise of online assessments that can be fed through the machinations of “big data.”

In his excellent December Atlantic article on the rise of “people analytics” in the workplace, Don Peck goes through the history of evaluations evaluations and the cutting edge of what the marketplace offers today, in the hiring process and on through the workday. Peck closes his piece by reflecting on the explosion in evaluations and people sorting he spent 8,500+ words recounting, and explains that while he had gone into the piece expecting to conclude that people analytics “would only widen the divergent arcs of our professional lives, further gilding the path of the meritocratic elite from cradle to grave, and shutting out some workers more definitively,” he came out of the reporting “cautiously optimistic”; in fact, now he believes it more likely “that we’re headed toward a labor market that’s fairer to people at every stage of their careers.”

You see, judging people by the ol’ gut instinct turned out not just to be inefficient, but discriminatory. Famously (in social science circles), job applicants with an African-American-sounding name on their resume have to send out many times the resumes or have up to eight years of additional work experience in order to receive the same number of job interviews as an applicant with a white-sounding name but otherwise identical credentials. Moreover, while being out of a job for six-plus months has a severely negative impact on one’s chances of getting another chance (often from prospective employers presuming there is some skeleton in the closet causing the unemployment that is not apparent on the resume), the numbers found no negative impact in job performance among the once long-term unemployed. Most interestingly, ex-cons turn out to have better attendance and performance in hourly-wage jobs than average, rebutting an otherwise understandable reluctance to hire the formerly criminal. The analytics not only allowed employers to get a better return on their payroll, but opened job opportunities to categories of people who had previously been shut out of jobs without reason.

Leonard is more skeptical. He recounts that many of these questionnaires may run afoul of the Americans with Disabilities Act by implicitly acting as a test for psychological disorders. That may serve the interests of employers, in Leonard’s view, but “if you game out people analytics far enough, with companies only hiring the perfect workers for each, you end up with a lot of unemployed imperfect workers.” I have broad concerns about the excessive use of quantitative metrics in humane enterprises (including work), but analytics well understood have the potential to help break one of the more pernicious myths in employment: that very “perfect” candidate.

As Peck tells the history, we have always used shortcuts to try and gauge all the things about a candidate you can’t know before working with them. Very often, that has meant looking for replicas of ourselves. Peck recounts former athletes prizing varsity letters, and the waspish set appreciating squash skills. For most of the past 50 years, we have used a very expensive signaling mechanism, the university degree, to stand in for all manner of personal qualities. Each of us can think back to the tremendously disparate characters of our classmates to realize how little an alma mater tells you about a person.

If employers hire an analytics company to look for one mold of optimal worker to populate their payroll, they will merely be following in a long tradition of over-simplistic and self-defeating hiring practices. What’s important about workplace analytics is that there be diversity, and that people be matched to situations in which they can flourish. It serves no one’s interest to make a miserable hire.