What are People and HR analytics?
The analysis of data related to every employee in the organisation using HR analytics tools and metrics is known as People analytics. The main purpose of carrying out HR analytics activities is to understand, quantify, manage, and improve employee talent when it comes to the execution of strategies. These involve collecting data from external and internal sources, processing and storing them and analysing these data, in order to collect insights about employees who are responsible for organizations performance.
With the help of these insights from analytics, human resource managers can make intelligent decisions that are related to an employee such as recruitment, training them, evaluating their performance, compensation and much more.
Descriptive and Predictive Analytics
There are two types of analytics that human resource managers apply in order to get insights about the organization’s workforce. With the help of descriptive analytics, the human resource team can collect and analyze data that relays the current and historical events of the organization.
Getting answers for questions like what’s going on in the organisation at the moment? Becomes pretty easy with the help of descriptive analytics. For example, with descriptive analytics, managers can discover multiple reasons regarding why employees leave the organization or compare the budgets of training employees sorted by years and units.
Predictive analytics is the process where the current & historical data is analysed to forecast outcomes and events in the future. With the help of predictive analysis, human resource managers can get an answer to questions like “what might happen in the future?” To use predictive analytics, a lot of statistical techniques, like data mining to detect any patterns in the data and machine learning, are required.
Predictive analytics is already being used to optimize operations and to improve the experiences of employees. Let’s have a look at some of its applications:
Smarter Recruitment and People Management
Human resource managers can enhance the background checks with the help of tools that help explore and analyse every employees’ activities on online sites and forecast their opinions towards topics of toxic behaviours like sexism, bullying or intolerance. Fama, a known tool of the same kind, uses machine learning and NLP or natural language process, to analyse the content that’s present on these public sites and the human resource data to spot such behaviours.
Forecasting Employee Turnover
Employee turnover simply refers to the total number of employees that leave an organisation and are replaced by new employees. You can take employee engagement and employee retention up a notch if you utilise predictive analytics and machine learning. This allows the HR team to find risk factors that contribute to an employee leaving a job.
Being Able To Predict Employee Leaves
In HR Solutions, one of the best areas where the human resources team can use predictive analytics is predicting the unscheduled absences or day offs. Deloitte described the same in 2016 Global Human Capital Trends report that this solution is helpful for predicting unscheduled absences.
They reported that automobile companies were studying how employees were likely to take leaves based on the patterns of unplanned absence. With the help of these absence forecasts, human resource managers will be able to assign extra staff or reschedule shifts.
One of the other applications of predictive analytics is being able to identify current & future skill gaps. Mexico’s Ministry of Energy also used predictive analytics so they could understand the reason for the shortage of skilled employees in various industries. Other than this, the ministry has implemented predictive analysis in other sectors as well, like renewable energy etc.
Other than automating operations for the human resource team and providing detailed analytics related to the organisational processes, these HR solutions are helpful in multiple areas that deal between employees and the organisation.