In an ever-increasing corporate landscape, making quality hires is critically important for organisations looking to improve their bottom line.
To reduce the occurrence of bad hires, a growing number of businesses are turning to predictive analytics and big data. Using algorithms to analyse past and current data, these businesses more effectively can predict and adapt to future trends.
Predictive analytics in hiring is shifting the paradigm of hiring decisions away from resumes and traditional metrics and towards data-driven analysis and advanced simulations.
There are a broad range of applications of predictive analytics for hiring and staffing. In addition to helping identify the best talent, analytics can be used for talent pipeline planning. By leveraging macroeconomic data, organisations can better allocate resources. For example, a business might use such insights to identify the best geographical locations to invest into recruitment campaigns looking to attract candidates with a specific skill.
Additionally, data-driven recruiting and hiring help overcome bias, one of the biggest flaws in the human element of hiring. Though the vast majority of businesses and recruiters have no intention of exhibiting bias in their decision-making processes, many forms of bias can sub-consciously affect the hiring process nonetheless.
The impact of hiring decisions is extremely significant. Bad hires can result in legal risks, are very costly, and can damage overall morale. By contrast, good hires enhance productivity and are a cornerstone or an organisational growth strategy. Leveraging the power of predictive analytics empowers today’s leading businesses to hire with greater confidence and achieve better results.