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The way we hire and manage employees in America is fundamentally broken. Not only are unemployment rates still high in most cities, but approximately 32 percent of the current unemployed population has been unemployed for seven months or longer. Many people believe these long term unemployed workers no longer fit in today’s workplace, but they are wrong. To combat this issue the White House just unveiled new legislation for getting America back to work with the recent signing of the Workforce Innovation and Employment Act. Key to this initiative is taking action against the human biases and “skills gap” separating many unemployed workers from the companies that could hire them. As part of the President’s initiative, 300 corporations have pledged to change hiring practices that discriminate against the long term unemployed, enabling qualified individuals to get back to work.
They key vehicle for making that change? Predictive analytics.
It’s now recognized that the use of predictive analytics can surface powerful conclusions from disparate data sources that can, in turn, serve as the catalyst to foster change in business culture, improve hiring and management practices, and enable more Americans to find gainful employment in fulfilling roles. In my own experience at Evolv, just one of the harmful hiring biases we’ve used predictive analytics to debunk is that “People who haven’t worked recently aren’t viable candidates.” Our technology platform looked across millions of data points on employees across our customer network to prove that the long term unemployed perform no worse than those without an extended jobless spell and have empowered our clients (including several of the companies that supported this week’s legislation) to hire those candidates using a predictive score based on this same technology. We hope this finding in particular helps that 32 percent get that interview, that call back – that chance to show employers that they too, can be great additions to a team.
However, even with plenty of data to back up conclusions like this, many people fear and resist the use of big data and predictive analytics in employment practices. Images of robot recruiters with a marked lack of empathy or a dystopian society like the one in George Orwell’s “1984” come to mind. While much of the public conversation focuses on very real problems associated with big data (think NSA), the technology and political spheres have a tremendous opportunity to join forces to solve real human problems.
What’s missing from the current conversation is practical dialogue about how predictive analytics can be the catalyst for impactful partnerships between policy makers and businesses – for example how big data provides insights that destroy stereotypes when it comes to hiring. We are beyond simply “hacking” societal problems and must move toward progressive and actionable steps to change the way we conduct business on a larger scale. The technology industry must partner with America’s leaders to apply innovation to outdated processes that harm our economy’s growth, e.g. leaving millions of Americans out of meaningful work.
Some organizations are already doing great work connecting data science to social good. Kaggle is empowering data scientists to solve for challenges like decoding the human brain, classifying forest categories or predicting survival in disasters. Code for America enables skilled technologists to utilize their skills to impact their local governments by updating outdated sites and creating apps to meet community needs. Even academia has partnered with technology for impactful problem solving; Eric & Wendy Schmidt’s Data Science for Social Good Summer Fellowship is currently incubating its first class of do-good data scientists. But there is much more to be done.
Placing hope in predictive analytics isn’t just blind optimism, however. For example companies who use big data and predictive analytics to alter their management practices can significantly and simultaneously improve the health of their business. In the case of Xerox, one of the companies actively engaged in the push to hire the long-term unemployed, data showed that their prior practice of hiring based on previous work experience in a similar role didn’t necessarily predict success in that candidate’s new job at Xerox. When they opened up new doors for candidates who would previously never have gotten to interview based upon their resume, Xerox then hired more people who were better suited to their job and reduced attrition rates by 20 percent, allowing the company to pocket the otherwise wasted money connected with those who left shortly after onboarding and put workers in best suited roles.
Xerox’s example is just one of the many surrounding how technology and business have worked to impact a societal problem — the possibilities are endless. How can we in business, government and technology work together to solve more human problems — and at greater volumes? Predictive analytics is at once the platform and the catalyst. I hope other leaders look at this week’s report and act. Find ways to use today’s predictive technology capabilities to actively hire candidates who have nontraditional backgrounds but who can also prove (via other signals not found on a resume) that they can and will be successful in the job — especially those who have been unemployed for a long period of time. Using predictive analytics in this way, we can help make better decisions and change the way we work in this country for better.
Carl Tsukahara is responsible for marketing and product strategy at Evolv.
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