Michael Lewis’ Moneyball tells the story of how the Oakland A’s started hiring players based on objective data rather than the subjective beliefs of baseball insiders. This data often defied conventional wisdom. But they revolutionized the game and the A’s beat behemoth teams with 3x the salary line.
Corporate America needs Moneyball for hiring. Evidence that the current system isn’t working abounds. Job vacancies haven’t come down from 2008 levels despite 30% greater unemployment (the Beveridge curve has a tail). Number of interviews and time to hire has doubled. Companies spend 40% or more of revenue on payroll and yet average turnover rates have climbed to 15%. Simple multiplication: 6% of revenue is wasted every year. HR may fear change but I’ve always wondered… why don't CFOs care?
What is the current system? In Josh Bersin’s words: “The vast majority of hiring… decisions are made on gut feel, personal experience, and corporate belief systems. This is like the vice-president of marketing spending millions of dollars on a new campaign because he or she “always does it this way.”
About 30% of corporate America tries to collect data using questionnaires. Sounds good but let’s take a closer look. Let’s imagine a doctor trying to objectively determine your weight. Instead of weighing you, the doctor simply asks you your weight and takes that as fact. That’s like considering subjective answers to questionnaires the same as objective data.
Not only are questionnaires unreliable but they’re old! The Myers-Briggs is nearing its 75th birthday. Imagine companies trying to exist in today’s information age with 1940’s rotary phones, switchboard operators and typewriters. That’s how outdated questionnaires are compared to the alternatives.
What are the alternatives? The main alternative is objective data, either in the form of biodata or performance data. Biodata is any data that can be observed or collected about a person in a non-obtrusive way. Evolv, a HR big data company, found interesting correlations between browser downloads and job performance. The doctor / weight equivalent would be using your pant size to determine your weight. And finally there is performance data, where you actually examine someone’s cognitive and personality traits using objective measures. The doctor / weight analogy: she breaks out the scale. That is what pymetrics does.
Leading the pack on objective data-driven hiring are technology giants like Google, Apple and Facebook in response to developer talent scarcity. Such companies eschew resume factoids like GPA and look at data they can actually collect about you. Blue-chip firms like McKinsey and P&G also use objective testing.
Yet many companies resist data or testing saying it’s unfriendly, exclusionary and may limit diversity. The fact is screening must occur to cull 100 resumes to 10 interview slots. So the question really is: how does data compare to the status quo?
Is it friendlier to screen based on data or to use methods that currently lead to job loss 1 out of 7 times? Getting let go from a job hurts an employee way more than a company. A company will have a flood of new applicants to fill the spot. The person let go will be unemployed.
Is it more exclusionary to use data or to focus recruiting to prestigious universities and place less importance on students who don't go there? Companies have recruiting budgets and can’t send recruiters to all schools – fair point. But with the technological miracle also known as the interwebs (for the Rip Van Winkles who’ve been in a deep sleep since the late 1980s) that makes collecting data online easy, one could argue that, instead of being exclusionary, online data collection will allow inclusion never seen before. It’s like Kahn Academy for hiring.
Does it limit diversity to use an objective test that’s blind to gender and race or to go on gut decisions made from resumes and interviews? Instead of limiting diversity, one could argue that data will lead to greater gender, ethnic and socioeconomic diversity. Think of it like The Voice (or blind auditions) for corporate hiring.
Aside from these concerns, there are legal reasons why companies fear testing. However, again, let’s look at the data. Less than 1% of all EEOC suits regard pre-employment screening and an organization is 7x more likely to be sued by an employee / former employee than an applicant. So long as tests are administered to all applicants and don't discriminate against any group of individuals, their use is both legal and fair.
The real reason people fear data may be because they fear Gattaca-like determinism and loss of free will. As a behavioral scientist, I can assure you this type of scenario is only the stuff of Hollywood movies. Subjectivity and chaos will always win out. All I’m suggesting is that adding a little more objectivity might improve the process for everyone, including those who fear it most.