The past few years have seen predictive analytics move outside the realm of data scientists and enterprise technologists to more mainstream business verticals like human resources and operations. The technology finds particular relevance in human resources where knowing the demand supply gap in your talent pool in advance and understanding what impacts them will dramatically alter the success of your organization.
Human resources is a pretty generic term and can mean a wide variety of roles and responsibilities. This includes talent acquisition, talent fit analysis, employee management, resource training, productivity management, and so on. This provides businesses with a number of opportunities to apply various technology tools like big data, predictive analytics and prescriptive analytics to study and fix these various processes.
Among these various tools, predictive analytics can be quite significant given its ability to identify and fix issues before they blow up. Here are a few use cases.
This is by far the biggest challenge facing human resources today. According to one study, there will be a shortage of more than 85.2 million skilled workers worldwide by 2030. This is despite the growing adoption of automation across various industries. Predictive analytics tools help organizations identify current and future skill shortage and invest in reskilling and upskilling their labor force to be able to meet future demand.
This is something that has today been adopted by the Ministry of Energy in Mexico. The government has leveraged a number of different macroeconomic variables like oil price and exchange rates to build a predictive model for future skill shortage and has been investing in relevant training programs.
Employee loyalty and retention is a critical issue for most organizations; especially those in the small and mid-sized segments. One survey of over 1000 workers found that nearly 31% of them had quit a job within the first six months at least once. There are a number of different reasons why employees quit – poor onboarding, lack of clarity in their role and poor leadership are the oft-cited reasons.
Predictive analytics takes into account a host of different parameters that affect an employee’s productivity and motivation at work. This includes salary growth, their tenure at the current role, performance metrics along with external factors like time to commute and time spent working outside of work hours. All these metrics can be inputted into a predictive modeling algorithm that can sift through the various causation and correlation factors to precisely predict future retention rates.
This may then be used by organizations to fix possible attrition. For instance, the organization may shuffle responsibilities among workers, offer flexible-timings, or work-from-home options to make employees more productive.
Employee productivity can be a major player in business success. One recent study found that a whopping 70% of American workers do not feel engaged at work. The figures are unlikely to be any different in office spaces across the world. Predictive analytics can help measure employee productivity and stem the decline before it happens.
One of the most commonly deployed algorithms involves the study of monotonicity at work. Workers who perform repetitive tasks over and over again are likely to face boredom and disengagement a lot faster than those who work on fresh tasks regularly.
Predictive analytics can help understand measure overall productivity of employees across various functions and processes to identify specific tasks where monotonicity sets in. Such analytics functions can also help infer pretty accurate data on how long it takes for a worker to feel disengaged enough to quit their organization. Knowing this information can help organizations take corrective action like automating these functions or enabling a routine change so that workers are shifted to other tasks well before boredom sets in.
Having said that, it is important to realize that predictive analytics, as a tool, is only an enabler. True success with human resource management comes with intent and the willingness to look into data that may otherwise get discarded or sidelined till it is too late. It is necessary for organizations to proactively use analytical modeling tools to understand where their HRM processes are moving and how to fix the declining metrics before they cost the organization.