Hiring is a fundamentally risky endeavor. With every new person you hire, you’re taking a gamble on how well that person will perform within your organization. Traditional hiring metrics like unstructured conversational interviews and resumes often do little to minimize the unknowable risks associated with each hire because the information they impart is often unreliable. For example, one study found that 57% of hiring managers had found lies or embellishments on a resume, and unstructured interviews are equally poor at predicting actual on-the-job performance.
While it’s unlikely that traditional hiring metrics are going away any time soon, the hiring process is becoming more and more data-driven over time. This is a positive trend because incorporating objective metrics into the hiring process is one way to tangibly reduce your risks.
The benefits of data-driven, evidence-based hiring practices are pretty clear – they not only provide you with better hiring results thanks to predictive data, but they also help to reduce some of the unconscious bias that unavoidably slips into the process.
But that doesn’t mean that any single data point or technology solution can eliminate all of the risks associated with each new person you hire. No single hiring tool will get you to “never make a bad hire again,” and you shouldn’t trust a vendor who makes that promise. What these data-driven tools do provide is a way to reduce your risks by enabling you to make more informed hiring decisions.
Scientifically validated pre-employment tests serve as a powerful tool for reducing risk because they are built on decades of research into the traits and abilities that lead to job success. Companies that incorporate tests into their hiring process experience lower turnover, fewer workplace accidents, and fewer incidences of employees engaging in counterproductive work behaviors, such as absenteeism, tardiness, theft, or fraud. These incremental benefits help to tangibly reduce the risk facing your organization with every hire.
