Imagine a scenario where the human resources (HR) enters an employee’s basic details into a system and finds out his expected tenure, performance, ideal learning curve, development needs and learning mix at the click of a button! While this is a slightly futuristic scenario, it certainly is not far-fetched. Organisations today no longer make gut-feel decisions, but those based on data analytics. Companies that have got their data architecture accurate can predict the time to fill for different roles and address gaps in their recruitment processes.
Much of this journey has been possible due to the increasing availability of talent-related data. While supply chain, marketing and finance functions have already embraced data in decision-making, HR is still lagging — relying more on HR-metric forecasts. Though this has improved internal HR efficiencies, it still does not present clear benefits to the business. HR needs to align with business needs and prepare for improving an organisation’s performance. That, though, is easier said than done.
The big (data) HR challenge: With data, HR functions in India face challenges on three fronts. The first is lack of understanding on what data needs to be captured, which talent challenges are hampering business achievement, and what insights will help businesses address those challenges? The second is not understanding how to set about capturing data. Should we capture this data through a technology intervention (PMS, LMS, ATS) or should this be done through surveys that link back to the technology? The third is not knowing how to report data for quick and easy decision-making. How do we ensure that the businesses get real-time, simple and easy-to-comprehend talent information to help it take decisions?
Best-in-class organisations focus on the most critical talent-related problems facing different parts of the business to determine what data to capture. For example, to predict employee attrition, some organisations are studying technology triggers (such as visiting the “exit process” page or applying for mass leaves) and their impact on employee attrition. They are also using organisation network analysis (ONA) to capture continuous data to determine the strength of internal networks by analysing email metadata and internal social interactions, supplemented by pulse surveys. This information acts as input in predicting employee attrition and performance, employee potential, learning needs and even improving workplace layouts. Advanced visualisation tools with an underlying analytics engine now help HR teams generate customised dashboards with insights relevant for different businesses.
Embarking on the talent analytics journey: A leading life insurer in India was faced with over 60% annual frontline sales (FLS) attrition, resulting in a bad hire cost of more than $10,000 per employee. The organisation used analytics to inform the different phases in solving this challenge. This involved building a predictive model for frontline sales hiring by evaluating data of past and current employees. The firm was able to identify cohorts of candidates with different probabilities of attrition through the model. This further helped the organisation evaluate reasons why those with high probabilities of attrition left, which resulted in redesigning the entire hiring architecture, infusing more technology and a mindset shift. Over the course of the year, the goal of the organisation is to leverage analytics to reduce FLS attrition by 20 percentage points.
Time to shift gears: There is no denying the fact that the future is going to be more data-driven, and possibly machine-driven. It will be imperative for traditional HR to evolve and play a much bigger role. At the end of the day, while data analytics will throw up valuable insights and predictions, it will be the role of HR professionals to contextualise and implement them given organisational realities and practical challenges. For that, organisations will have to build a diverse HR team — data scientists, seasoned HR professionals and people with experience in business roles — to drive organisation priorities.
The onus is on HR to leverage data, technology, machines and the power of its collective intellectual capital to unlock the strong business value trapped in its operating model.