“Big data” is a huge trend right now, and the Atlantic recently came out with a solid piece on how companies are using “people analytics” to make decisions about hiring and even promoting and firing. This is a revival of an old trend. After World War II, there was a lack of manpower within the workforce so companies put a premium on hiring people who had “managerial potential.” They turned to psychological tests and all sorts of assessments, most of which were fairly unscientific. In the 1990’s, the fervor behind treating hiring as a science began to fade and ad-hoc interviews as we know them today took their place.
The problem with our current system is bias: We are biased towards hiring people like us in terms of interest and identity, and our intuitive judgment is frequently inaccurate.
Recent survey data collected by the Corporate Executive Board, for example, indicate that nearly a quarter of all new hires leave their company within a year of their start date, and that hiring managers wish they’d never extended an offer to one out of every five members on their team.
Now there is a rising trend towards a more algorithmic assessment of workers’ potential through tests and games, as well as a more data-driven picture of what a “good hire” looks like.
Here are two examples. The first is a video game.
Knack is a start-up that makes app-based video games, among them Dungeon Scrawl, a quest game requiring the player to navigate a maze and solve puzzles, and Wasabi Waiter, which involves delivering the right sushi to the right customer at an increasingly crowded happy hour. These games aren’t just for play: they’ve been designed by a team of neuroscientists, psychologists, and data scientists to suss out human potential. Play one of them for just 20 minutes, says Guy Halfteck, Knack’s founder, and you’ll generate several megabytes of data, exponentially more than what’s collected by the SAT or a personality test. How long you hesitate before taking every action, the sequence of actions you take, how you solve problems—all of these factors and many more are logged as you play, and then are used to analyze your creativity, your persistence, your capacity to learn quickly from mistakes, your ability to prioritize, and even your social intelligence and personality. The end result, Halfteck says, is a high-resolution portrait of your psyche and intellect, and an assessment of your potential as a leader or an innovator.
“Data” can be collected from all kinds of sources, including your email:
Bloomberg reportedly logs every keystroke of every employee, along with their comings and goings in the office. 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). E‑mail, of course, presents an especially rich vein to be mined for insights about our productivity, our treatment of co-workers, our willingness to collaborate or lend a hand, our patterns of written language, and what those patterns reveal about our intelligence, social skills, and behavior. As technologies that analyze language become better and cheaper, companies will be able to run programs that automatically trawl through the e-mail traffic of their workforce, looking for phrases or communication patterns that can be statistically associated with various measures of success or failure in particular roles.
One of the profound effects of this shift is that companies are relying less on college-education as a proxy for potential. The Atlantic reporter writes that “for a lot of companies who are using advanced analytics to reevaluate and reshape their hiring, nearly all of them told me that their research is leading them toward pools of candidates who didn’t attend college—for tech jobs, for high-end sales positions, for some managerial roles.”
We have maintained for awhile now that the resume is becoming increasingly defunct because who you are is increasingly transparent in a technological and online age. We have also argued that our metrics must adapt to the times and become metrics that capture how we behave towards each other.
The Atlantic article demonstrates that these two trends are truly converging and escalating. Big data is becoming People data: We are capturing, measuring and comparing observations about how people behave — even how they communicate over email — at an unprecedented rate. Applying rigor towards hiring, promoting and firing based on real observations and not “gut instinct” is a welcome change, not just because it hedges against our personal biases, but because it does a better job of capturing the “full person” and his or her character traits (we’ve written elsewhere about the importance of hiring for character and not just skills or traits), and weans us off limited proxies such as the level of education a person has.
Moreover, these metrics provide a set of tools and guidelines for employees to do their job better. Of course, there is a difference here between shifting and elevating behavior, and nothing elevates more than a sense of a purposeful mission, shared values and trust. That said, metrics help us become more precise about how we measure things like “elevated behaviors.”
Some of the data is not related to character traits. For instance, Gild, a company that helps other companies hire good coders, found that a strong predictor of a good coder is “an affinity for a particular Japanese manga site.” It remains a mystery as to why it is, but the correlation remains. There are certainly “Big Brother” implications to this People data phenomenon, which is something to be mindful of.
The Atlantic article begins and concludes with reference to Moneyball, but the point about Moneyball misses the real innovation behind people analytics. Moneyball helped baseball coaches and managers figure out a more precise set of desirable outcomes; they started looking for on-base-percentages, and not just home runs. They were still, however, for the most part measuring “what” players produced. The real innovation lies in capturing the behaviors, values and traits that lie underneath the outcomes. This is the ripe potential of people analytics.