Your Job, Their Data: The Most Important Untold Story About the Future
How employers bring "big data" to bear on hiring and management could be the defining issue of our times.
All the drones, synthetic biologists, and self-driving cars notwithstanding, the story of how companies quantify, analyze, and try to predict your job performance may be the most important story in technology.
That is to say, when we look back in 20 years about what has changed in our lives, we will be able to find this thread of data-driven personnel decision making as the thing that's changed people's lives the most.
My colleague Don Peck has an unnerving feature in this month's magazine on precisely this issue: "They're Watching You At Work." I highly encourage you to absorb this tale's anecdotes and data.
After reading it, your gut may feel optimistic, like his, or queasy, like mine. Because the "Moneyballing" of human resources and corporate management has already begun, and who is going to stop it?
Peck's reporting turned up some amazing/horrifying details about the current prevalence of data-driven corporate practices. For example, he writes, "The Las Vegas casino Harrah’s tracks the smiles of the card dealers and waitstaff on the floor (its analytics team has quantified the impact of smiling on customer satisfaction)."
Maybe that's nice from a bottom-line perspective, but imagine working at Harrah's: "Hey, Alexis, your smile ratio was down today. Keep those lip corners up, buddy!"
Do we want to live in that world?
As we reported this week, American truck drivers will soon have all their miles logged by electronic devices. Though safer roads are the nominal goal, no one really disputes that the data on braking or fuel efficiency might be used for other things (like hiring and firing decisions).
Corporations already have so much power relative to their workers. And the data — because they're the ones generating it — only seems likely to enhance that imbalance. At least that's how I see it.
Of course, there will be winners. For example, readers of a certain (and strategically undisclosed) Japanese manga site, who are looking for programming jobs. Here's another jaw-dropping passage from Peck:
Vivienne Ming, Gild’s chief scientist, told me that one solid predictor of strong coding is an affinity for a particular Japanese manga site.
Why would good coders (but not bad ones) be drawn to a particular manga site? By some mysterious alchemy, does reading a certain comic-book series improve one’s programming skills? “Obviously, it’s not a causal relationship,” Ming told me. But Gild does have 6 million programmers in its database, she said, and the correlation, even if inexplicable, is quite clear.
This is so weird!
Not that the current job market is all that normal. Peck points out that the way personnel decisions are currently made is far from perfect. Bias and discrimination are ubiquitous as people try to find the best people for a job based on partial, bad, and irrelevant information.
Perhaps more data will allow different types of people to get ahead! Perhaps our biases won't be encoded into indecipherable algorithms that are even harder to critique. But looking back in time, as Peck does, does not give one a lot of confidence that we can rise above our times riding the beast of big data.
Peck points out that in the mid-century, data-driven hiring was all the rage:
IQ tests, math tests, vocabulary tests, professional-aptitude tests, vocational-interest questionnaires, Rorschach tests, a host of other personality assessments, and even medical exams (who, after all, would want to hire a man who might die before the company’s investment in him was fully realized?)—all were used regularly by large companies in their quest to make the right hire.
Unfortunately, it did not work out. The tests looked for the wrong things.
[Many] were based on untested psychological theories. Others were originally designed to assess mental illness, and revealed nothing more than where subjects fell on a “normal” distribution of responses—which in some cases had been determined by testing a relatively small, unrepresentative group of people, such as college freshmen. When William Whyte administered a battery of tests to a group of corporate presidents, he found that not one of them scored in the “acceptable” range for hiring. Such assessments, he concluded, measured not potential but simply conformity.
So, the assessments "had almost disappeared by 1990."
Maybe bigger data will solve these problems. Companies will have learned from the problems of their predecessors. And soon, we'll all be curating every atom of our "data exhaust" to enhance our employability and promotability.
On the other hand, perhaps the quantification of workplace performance just won't work.
After all, startups, the very posterchildren for innovation, seem driven by serendipitous relationships and very human networks of connections.
The Oakland A's, the posterchildren for this very specific type of innovation, lost in the first round of the playoffs. Again. The A's have had success, but (speaking as a hometown fan), they remain also-rans in the great game of baseball. Not only do they have no championship rings, but they've never even made the World Series (during the Moneyball era). In fact, they've only won one playoff series in 11 years.
If Moneyball has long served as a parable for the wonders of analytics, then maybe it's time it starts to serve as a parable for the downsides and limitations, too.
In any case, no matter your view point, Peck's story is essential reading.