A fellow management consultant shared an article from The Atlantic on an online community we both belong to, Living in an Extreme Meritocracy Is Exhausting by Victor Chen. The the article argues, “a society that glorifies metrics leaves little room for human imperfections.” It is an attack on Taylorism—and more fundamentally—American meritocratic values:
…what does it mean to live in a culture that defers to data, that sorts and judges with unrelenting, unforgiving precision? This is a mentality that stems from Americans’ unabiding faith in meritocracy, that the most-talented and hardest-working should — and will — rise to the top.
Chen argues that our belief in meritocracy is at odds with our other American values, Puritanism.
From the days of the Puritans, they have found ways to temper their zeal for meritocracy, self-reliance, and success with values of equality, civic-mindedness, and grace, a surprising harmony of principles that the country’s earliest observers lauded as distinctly American.
Chen’s viewpoint is provocative, and worthy of consideration. For some of us it certainly feels like data collection and metrics have grown out of control (please press ❤️ if you are enjoying this read so far). Meritocratic values become dangerous if applied to a broad enough context where the participants don’t have the opportunity to compete on level ground. Further, we’re not really living in a meritocracy if we aren’t measuring the right things.
However there are some real dangers to elements of his argument: Chen flirts with an argument against data collection and measurement itself. This argument is at best nonsensical, and at worst dangerous. Further, Chen misses the actual moral dilemma: it isn’t acts of measurement which are to blame for unhappiness and disengagement but rather who’s defining and making the measures. Chen offers no consolation or solution to our measurement epidemic other than perhaps, “challenging deep-rooted notions of what success is.”
Attacking measurement is ridiculous. Measurement and data are nothing outside of the context of an experiment. It’s like arguing against the concept of counting. At worst, this attitude leads us further away from accruing knowledge through the scientific process. It gives undo validity to using belief as a tool for separating truth from falsehood: belief that vaccines are dangerous; belief that humans don’t contribute to a changing climate; belief that people with blue eyes are more prone to violent crime. The true moral dilemma isn’t data collection or science itself, it’s that millions of workers are subjected to human experimentation without their consent.
Think about it: management sets metrics. Worker behavior responds to these metrics. Business performance is gathered, and the cycle repeats. It’s little wonder why workers feel their jobs are dehumanizing. They aren’t in control of defining what’s really important or how they should be measured.
The solution isn’t to remove measurement from the workplace. Experimentation and measurement are fine. In fact, they are essential to making sense of the world. Instead, we must shift what gets measured, who does the measuring, how the measure is made (qualitatively or quantitatively), and—most importantly—keep this process as close to the worker as possible. Instead of incentivizing our workers for meeting metrics that feel arbitrary, we must reward them for the most important metric of all: good experimental design and execution. Only then will we have work that is both richly rewarding and productive.
