HR analytics lets HR make better decisions on the basis of historical information of employee performance. For example, if data suggests that some of your best talent have certain education background, hobbies or profile, you will be able to screen profiles from the candidates pool and get those who are most likely to be successful. This would mean lower cost of recruitment, reduction attrition in future and better business results. The availability of online databases, applications, profiles in social media and career directories, documents, etc. today enables how we can improve the effectiveness of recruitment and easily learn more about applicants.
Similarly, we can use online databased and career directories to build profiles and job descriptions based on how other organizations define such roles and the availability of talent pool in the market. This is higher success rate during not only recruitment but also in retention
Using historic data of employee performance and specific conditions that led an employee to performance better, HR managers can using Clustering Models to put together teams of like minded employees where every individual performs it his/her best. Similarly, inconsistent performance, spikes or drops in performance can help HR analysts identify key drivers for such pattern.
This is one of the most widely used application or example for HR Analytics. By using historic data related to employees it is possible to used Machine Learning (ML) classification models is very accurately predict employees who are most likely to leave the organization. This is called as Predictive Model for Employee Attrition. The model provides the propensity or probability that an employee would leave in near future. This data based approach can replace RAG (Red/Amber/Green) colour codes that HRBP’s use to classify employees based on high flight risk.
Linking Performance to Pay is a ever green topic in HR. With performance data that goes beyond performance rating, C&B professionals can build statistical models to validate if the increased compensation and benefits to an individual can result in justifiable business performance improvement. Further data analytics can be used to profile employees based on the value they see various benefits provided by the organization and personalize the package.
L&D can play pivotal in enhancing business performance and building a future fit workforce by using the data to identify training needs, establish quantitative effectiveness measures for L&D interventions and statistically prove effectiveness of the program. For example, using wearables, L&D professionals can capture real time data of employees heart rate, to ascertain the effectiveness of the learning module covered in training. Data can be used to design effective intervention.
And most importantly, L&D can rescue themselves from the perception of being providers of different career development programmes that deplete a large part of the company’s budget.