Exceptional talent is hidden in plain sight. By using data, we can uncover these individuals, unlock their potential and enable them to become highly successful professionals who grow into top performers and leaders.
Companies, nonprofits and government agencies have amassed troves of data on applicants, trainees, program efficacy and business outcomes. It’s time to leverage it for social good, eliminate bias in hiring and create on-ramps to in-demand careers for people who are often overlooked and underestimated.
Focus on aptitude not skills
A first step is to use data to build effective hiring screenings. These look past the skills that someone has now, and determine how quickly someone can acquire skills and put them into practice in the future. They focus on aptitudes like intellectual agility along with wiring for curiosity, grit and tenacity.
This differs from traditional recruiting approaches that rely on pedigree and degrees. One of the benefits of using machine learning to predict measurable, successful outcomes is that algorithms determine which traits are important and give them appropriate weighting. Data can tell you if there’s correlation between years of experience and successful job performance.
Data-based hiring can also replace resumes, which are false friends. Resumes are infused with bias – from names imbued with ethnic, racial, gender assumptions to educational credentials that systematically eliminate two-thirds of Americans without a four-year college degree and application form questions about felony convictions that disproportionately impact people of color.
Using data rather than resumes/pedigree for outside hires is a great first step. But companies can also take a data-informed view for promoting and growing current employees. Internal programs for structured growth and talent acceleration, supported by measurement and tracking, are both socially responsible and economically sustainable. Given that it’s six times more expensive to hire from outside the company, you can use data to grow your own future high-performers and leaders at a lower cost with better results.
Social good and business effectiveness are not mutually exclusive
By screening, hiring and training with data, companies can build workforces that closely mirror local populations across a variety of demographic dimensions: race, gender, education and socioeconomic background. This approach transcends the individual, impacting families and communities in tangible, financial terms by providing paths to well-compensated jobs with strong future demand.
1. Reduced labor costs
Opening new recruiting channels expands your applicant pool, reduces the hard costs of hiring, eliminates the cycle of poach-and-be-poached and helps retain institutional knowledge.
2. Enhanced competitiveness by filling open tech job needs
If companies can’t deliver on expectations, customers will turn to competitors. According to Accenture, $11.5T in economic growth driven by intelligent technologies could be lost if companies can’t – or don’t – fill skills gaps. Companies can grow their market position with a steady stream of the right technical resources and better meet expectations of a diversifying population by using data to build more diverse workforces.
3. Democratized talent
It’s hard to draw employers or employees outside of the major tech hubs. Data-based hiring can find equally talented people anywhere, making it possible for companies to compete and people to find work no matter the location.
4. Increased socioeconomic equality
The tech industry already has a rightfully earned reputation for exacerbating social and economic inequality. At a time where nearly 50 million people in the U.S. have filed for unemployment, the difference between the haves and have nots will be supercharged, creating a more stratified society. Data-driving hiring can break the cycle of inequality, bringing in previously overlooked populations into an expanding industry and breathing life into communities full of aptitude, but lacking opportunity.
We all play a part
Words and statements of support for diversity, inclusion, equity and opportunity are important. But actions speak louder than words.
A data-based applicant review and hiring processes can help companies identify and root out bias by giving an opportunity for those from nontraditional backgrounds to succeed.
Those in mid-career positions can use their voices and influence, holding employers accountable for implementing structured career paths that boost any current employee with ability for a position, regardless of their pedigree. Look for opportunities to mentor and become involved in crafting data-based policies beyond directional statements of support.
And for the many people searching for new jobs or career paths, know and articulate your value beyond your credentials. Invest time into finding advisors and mentors to help chart a course, regardless of starting point.
Lessening the skills gap and bringing more people into family- and community-sustaining careers of the future isn’t just an economic issue. It’s a gender and racial equity issue. It’s a social and economic justice issue. It’s a matter of creating an economy that allows the American Dream to survive, rather than exacerbating inequality.
By using data and data science for social good, we can strengthen companies and communities.