Talent analytics still in a nascent stage in India: Willis Towers Watson study


Majority organisations in India lack dedicated resources or suitable skills for talent analytics

Talent analytics may be the buzz word in HR today, but research says, it is yet to pick up in India. The 2018 ‘State of Talent Analytics in India’ study by Willis Towers Watson reveals that a whopping 74 per cent of organisations are still at a nascent stage of maturity on talent analytics.

With only 18 per cent of organisations using advanced analytics and merely 11 per cent of organisations in India saying they are satisfied with the current level of talent analytics, there is a lot that needs to be done in the space. What is more, just 24 per cent are satisfied with the current level of workforce planning in their organisation!

Challenges of data accumulation and access form one of the biggest barriers to optimal adoption of talent analytics, according to the study. Integration of data from disparate sources (71 per cent), fragmented data (68 per cent), lack of automated/repeatable processes (63 per cent), poor data quality (58 per cent) and lack of skills to analyse data (39 per cent) are some of the biggest barriers for effective implementation of talent analytics.

The pioneering study that assesses the overall maturity of talent analytics and workforce planning in India, defines talent analytics as the application of statistics, technology and expertise to large sets of people data, to make better decisions for an organisation. Workforce planning, on the other hand, looks at the current and future workforce requirements in terms of capacity, capability and future skills requirement.

The study found that workforce optimisation ranked a close third among the top four HR priorities. This is a clear testimony to the role and importance of talent analytics in workforce planning, going forward. It also indicates that predictive analytics, the science of using data and statistics to predict likely outcomes, is a high-interest area. Yet, only three per cent organisations are currently leveraging predictive analytics for making people decisions.

Further highlighting the need for adopting latest technology in talent analytics, the study found that a vast majority—79 per cent—still use Excel-based spreadsheets, while only 18 per cent use statistical tools, such as SAS, R, SPSS and so on.

Neeraj Tandon, practice lead-workforce analytics and planning, Asia Pacific, Willis Towers Watson says, “Organisations are increasingly recognising the value of big data and data analysis in predicting and dealing with constant change and complexity. From an HR and talent-management context, this difference could have far reaching implications.”

He believes that evidently, HR teams that have been able to effectively utilise people data, are realising the benefits and value for their stakeholders and at the same time succeeding in elevating the role of HR in making boardroom decisions. “Our study shows that companies in India recognise this, and are beginning to put in efforts in the right direction, but there is some distance to cover,” he adds.

Although the adoption at the overall organisational level may not be too significant, the study found that adoption at the CXO level is quite noteworthy. 84 per cent CHROs, 58 per cent CEOs and 32 per cent COOs have adopted talent analytics as a strong medium for workforce optimisation, and this needs to be sustained. Also, with regard to the application of talent analytics, most organisations (90 per cent) are using it for recruitment and retention, while only 64 per cent have integrated it for workforce planning.

The study further revealed that a majority of the organisations in India lack dedicated resources or suitable skills for talent analytics, but that seems set to change with higher investments. 53 per cent companies are gearing up to increase their investment in talent analytics. The key focus areas of investment will be technology (71 per cent), people/competence (68 per cent) and data quality (61 per cent).

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