Q&A: Arun Taneja: “The use of HR data still seems to be a tick-box activity”
Aon Hewitt’s resident experts on what analytics can do for the people function
Aon Hewitt’s resident experts on what analytics can do for the people function
The use of data analytics and human capital reporting is becoming an increasing part of everyday life for HR professionals. Whether it is to measure and assess performance, wellbeing, productivity or innovation, when used correctly data analytics can demonstrate return-on-investment and build operational efficiency. But research by the CIPD has shown that capability in HR analytics remains low. People Management spoke to Arun Taneja, a consultant at Aon Hewitt Middle East & Africa, about where HR in the GCC is on its analytics journey.
Organisations are using data in some shape and form to measure employee progress, but it seems to be a tick-box activity rather than one used to add value to the business. Having said that, it would be incorrect to say that HR is afraid of data or that it is only the HR function that is responsible for data analytics. Business functions must act like intelligent customers that demand more effective metrics to make decisions related to their people. After all, the onus of making the people-related decision must lie with the business stakeholders; the HR function must facilitate effective decision-making.
I have always believed that building strong relationships is the key to evolution and success. Measuring and making decisions based on data is always more efficient than making decisions based on gut instinct. And human capital data should not be seen as the villain, because the decisions are still being made by humans, until the time robots run the world – joke intended. Any data on human capital should be used to make decisions that help employees become better at their job. It’s the responsibility of the CHROs to ensure the employees do not feel dehumanised. Effective communication aligned with the cultural nuances of a region or country plays a key role in that.
One example I am aware of is an organisation that created a predictive attrition model that could predict the propensity of an individual to leave an organisation with an accuracy of 71 per cent. The model was created using demographic, engagement and business-related data and was self-correcting. Its accuracy improved with time and additional data. The challenge is about understanding the talent demand and supply gap, as well as the business needs, and then arriving at solutions.
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