Toward Fairer Data-Driven Performance Management

the most common approach to quantifying someone’s job performance is to rely on subjective ratings, whether by the employee (self-rating of performance) or their manager (supervisory ratings). The typical correlation between self-ratings and supervisory ratings of job performance is merely 0.22, which translates to a trivial 4% overlap between the two. In other words, 96% of the variability in employees’ self-rated job performance is unrelated to how their managers’ view their performance.

While employees are generally too generous in their self-evaluations of performance, there is not much evidence for the superior accuracy of supervisory ratings in measuring workers’ true contributed value or output, though aggregating ratings of different managers or sources, including peers, will significantly boost reliability. Needless to say, it is not just possible, but also desirable, to improve how others see us through factors unrelated to our actual job performance

the KPI definition process has to be transparent and inclusive. Executives shouldn’t think that they can design metrics in a vacuum, or that just because a metric is predictive that it’s ethical to use. Creating ethics committees with independent members, engaging in regular discussions with stakeholders across the organization, and admitting when certain metrics are a best guess rather than the absolute “right” metric are essential for building a fairer, faster, and more data-driven organization.