Predictive Analytics, from past few years, have seen shift outside the realm of Data Scientists and enterprise technologists to more mainstream business verticals, such as Human Resources and operations. The technology explores particular relevance in human resources where it finds the demand-supply gap in the company’s talent pool in advance and comprehends what impacts them will radically transform the success of organizations.
Human Resource is a quite widespread term and can mean a wide range of roles and responsibilities, includes talent procuring, workforce management, talent fit analysis, product management, and more. It offers companies with a variety of opportunities to implement various technology tools, such as Predictive Analytics, Big Data, and prescriptive analytics to study and fix these various procedures. Amongst these a variety of tools, Predictive Analytics can be quite significant, due to its ability to detect issues and fix it before they explode. Here are a few use cases regarding the impact of Predictive Analytics in human resources. The very first is employee skilling that is a big challenge for human resources today. As per the study that showed there will be a scarcity of over 85.2 million skilled workforces across the globe by 2030. Predictive Analytics tools can assist businesses to find existing and future talent shortage and also can invest in re-skilling and up-skilling initiative for their labor force to meet the company’s future demand.
Further, several companies face workforce loyalty and retention as critical issues, especially small and medium-sized organizations. The study reports illustrated that there are various factors for employee retention, including lack of clarity in their role, poor onboarding, and poor leadership. So here Predictive analytics takes into account that affects the workforces’ productivity and motivation at the workplace, such as salary growth, their tenure at the current job role, performance metrics as well as external factors like travel time and time spent outside during the working hours. All these metrics can be put into a predictive modeling algorithm which can sieve through a variety of causation and correlation factors to accurately foresee future retention rates.