ShareThis
I recently shopped on the Internet for video supplies to enhance my e-learning solutions. Now whenever I search for anything and anyone, I see ads for a note stand, microphones, extension cords and the like.
We've all been there. In marketing, our web histories, social network postings, crowd-sourcing contributions and emails have changed how companies advertise and sell to us. And Big Data is already transforming the stock market, where algorithms predict stock-price movements.
By one estimate, more than 98% of the world's data is now stored digitally. The volume of that data has quadrupled since 2007.
But now my friend Ken Levine alerted me to a new application of Big Data: Predictive statistical analysis promises to change the labor market and how people find, work or lose jobs. (There is a much longer article about people analytics in The Atlantic from which I have borrowed here.)
In corporations like General Motors and Google, HP and IBM, Intel and Procter & Gamble, but also SMEs, dedicated HR analytics teams use People Analytics, as some call it, to vet or screen for high-potential leaders or talents.
The ethical implications are troubling. Is an employer justified in sifting through billions of data about an employee or candidate?
It's not just the invasion of privacy. What if an employer uses the data to make judgments about prospective employees that they have never seen?
Let's look at that in more depth. In this short video (actually a commercial for people analytics), IBM extols the virtues of Big Data to help companies screen for salespeople that are both extraverts and introverts.
IBM is right: Bias is a huge problem in selecting the right people. I know this from my my own company. The people who are the coolest participants in a strategy workshop are not necessarily the ones who are great in the action afterward.
Such bias can lead to bad mistakes. In 2010, three professors at Duke’s Fuqua School of Business asked roughly 2,000 people to look at a long series of photos. Some showed CEOs and some non-executives. The participants, who didn’t know who was who, were asked to rate the subjects according to how “competent” they looked.
The study found that CEOs look significantly more competent than non-CEOs; CEOs of large companies look significantly more competent than CEOs of small companies; and, all else being equal, the more competent a CEO looked, the fatter the paycheck he or she received in real life.
And yet the authors found no relationship whatsoever between how competent a CEO looked and the financial performance of his or her company.
This bias is not new, and it can produce to the opposite error of judgment In the 1950s, 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.
In his book "Blink," Malcolm Gladwell tells a classic example from the 1970s and ’80s: Professional orchestras changed to “blind” auditions, in which each musician seeking a job performed from behind a screen.
The result was stunning: The proportion of women winning spots in the most-prestigious orchestras shot up fivefold, notably when they played instruments typically identified closely with men.
But we have not learned much since then; examples of bias still abound. Tall men get hired and promoted more frequently than short men, and make more money. Beautiful women get preferential treatment, too—unless their breasts are too large.
Older workers are seen as more resistant to change and generally less competent than younger workers, even though plenty of research indicates that’s just not so.
Workers who are too young or, more specifically, are part of the Millennial generation are tarred as entitled and unable to think outside the box.
Our biases (I call them filters) are subconscious, and can run deep. In 2004 the economists Sendhil Mullainathan and Marianne Bertrand put white-sounding names (Emily Walsh, Greg Baker) or black-sounding names (Lakisha Washington, Jamal Jones) on similar fictitious résumés, which they then sent out to a variety of companies in Boston and Chicago.
To get the same number of callbacks, they learned, they needed to either send out half again as many résumés with black names as those with white names, or add eight extra years of relevant work experience to the résumés with black names.
After the study came out, a man named Jamal sent a thank-you note to Mullainathan, saying that he’d started using only his first initial on his résumé and was getting more interviews.
In a recent survey of some 500 hiring managers by the Corporate Executive Board, 74 percent reported that their most recent hire had a personality “similar to mine.”
I see this often in my consulting: Team heads tend to hire people that have similar preferences, similar leadership styles, similar value-systems.
Lauren Rivera, a sociologist at Northwestern, interviewed professionals from elite investment banks, consultancies, and law firms about how they recruited, interviewed, and evaluated candidates, and concluded that among the most important factors driving their hiring recommendations were—shared leisure interests.
(I admit that I have used my sub-3-hour New York Marathon in selling consulting work to top and senior executives who shared a passion for running.)
The current system leads to hiring decisions that are questionable, to say the least. The Corporate Executive Board found recently 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.
The costs of losing a new hire can run to $1 million for recruiting, contracting, relocation, training and on-boarding. Not to speak of what happens if the new hire takes his new-found intelligence about your company to a competitor. It gets expensive.
Enters Knack, a tiny start-up based in Silicon Valley. Knack 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, where players must deliver the right sushi to the right customer at an increasingly crowded happy hour.
These games aren’t just for fun: they’ve been designed by a team of neuroscientists, psychologists, and data scientists to check 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 a typical assessment 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 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.
Hans Haringa, an executive at Shell, by revenue the world's largest company last year, heard about Knack. Haringa works for Shell's GameChanger unit: a 12-person team that for nearly two decades has had an outsize impact on the company’s direction and performance.
The unit’s job is to find potentially disruptive business ideas. Haringa and his team solicit ideas promiscuously from inside and outside the company, and then play venture capitalists, vetting each idea, meeting with its proponents, dispensing modest seed funding to a few promising candidates, and monitoring their progress.
They had a good record of picking winners, but identifying ideas with promise proved to be extremely difficult and time-consuming. The process typically took more than two years, and less than 10 percent of the ideas proposed to the unit actually made it into general research and development.
Haringa heard about Knack and was skeptical, but decided to test the Big Data approach.
When the results came back, Haringa recalled, his heart began to beat faster.
Without ever seeing the ideas, without meeting or interviewing the people who’d proposed them, without knowing their title or background or academic pedigree, Knack’s algorithm had identified exactly the people whose ideas had panned out.
The top 10 percent of the idea generators as predicted by Knack were in fact those who’d gone furthest in the process.
What do you say? Is People Analytics good or bad for your company, your people, you? I look forward to reading you on my blog:http://thomaszweifel.blogspot.com/.
No comments:
Post a Comment