Albeit the practice of using talent analytics is not new, many HR leaders remain perplexed with its term mostly because it is widely used interchangeably between HR analytics and workforce analytics. Davenport et al. defined talent analytics as a suite of methodologies that allow identifying patterns from company data to manage a workforce, drive changes, and eventually create value. Different from other analytics that focuses on answering general HR questions, talent analytics helps HR answer critical questions regarding human capital and company success indicators. In addition, the same as other analytics, talent analytics also rely on metrics and big data.
See also: Why Should HR Care about Big data?
According to Fink and Sturman, metrics and key performance indicator (KPI) are useful to evaluate the effectiveness of an organisation’s existing processes. They can access how HR teams are performing in terms of developing the workforce. Yet, talent analytics, which also deals with metrics, aims to do differently. Fink and Sturman cited that the ultimate goal of the analytics is to identify patterns so as to predicts alternative scenarios that can inform strategic decisions.
To illustrate, an organisation is committed to improving the ethnic diversity in its workforce. In this context, talent analytics can help identify the set of potential measures that might increase diversity and assess their future impact on turnover. This would be very different from what metrics and KPI do. Metrics and KPI only capture the present moment and cannot assess its impact on future performance.
As employer and HR dept have always tried to make sense of their employees’ data to improve overall performance, talent analytics can come to perform better analytic by combining the system with big data.
As mentioned earlier, the analytics help HR in terms of addressing key questions around recruitment, retention, and human resources management. Yet, the use of talent analytic can be more specific such as to help measure the return of investment, the benefit of a specific compensation scheme, and can, therefore, provide senior management with the key facts that can inform development, wrote Nocker and Sena. In a fine term, talent analytic is a set of facts that can beat the gut feeling that might drive decisions at a senior level.
Another research by Falletta revealed that talent analytic can also be used in an area that directly supports business performance, including workforce planning and reviewing the effectiveness of reward practices. However, Falletta found that talent analytics can only give greater effect in a large organisation that can afford to invest in tech and expertise required to support talent analytic. The best examples typically come from sectors with strong technical, scientific, or data orientation, such as high-tech sectors, biotechnology, and retail.