In conversation with Human Capital, Pawan Kumar Bhageria, President Global-HR, IT, Admin & Education, Tata Technologies, states that one of the key enablers for leveraging Predictive Analytics in managing people would be the HR Technology infrastructure that underpins the Human Capital Management in an organisation.

The Great Resignation has come about as one of the biggest fallouts for organisations across the globe in the wake of the pandemic. How do you believe companies can make use of Predictive Analytics to avoid mass attrition?
 

The Great Resignation reflects the uncertain times that we live – which presents opportunities as well as risks. In most cases, employers do not fully understand why employees are leaving and the factors that employers perceive as important and the factors that employees think are important, are often divergent. While there is no single solution to this challenge, technology can help organisations understand the pulse of their people proactively and take actions that may minimise the risk of attrition.

Organisations have implemented Artificial Intelligence-based tools that scan internal data and initiate spontaneous conversations at regular intervals, and at different touch-points with the employees. Such tools can highlight employees that need special attention, and nudge leaders to act at the earliest. Unlike traditional employee surveys which have long questionnaires and happen once or twice a month, AI tools work every day to ensure that the pulse of what the employees are feeling and act proactively.

Anthony DiRomualdo from The Hackett Group has said, “The big challenge in 2021 is putting in place the digital infrastructure, the digital service delivery model for HR so that it can be done in a sustainable way at scale.” How do you believe that organisations can work around the obstacles and ensure optimum use of Predictive Analytics in their HR operations?

One of the key enablers for leveraging Predictive Analytics in managing people would be the HR Technology infrastructure that underpins the Human Capital Management in an organisation. Companies must think through the entire life cycle of People – Hire to Retire to Rehire while making decisions regarding the digital infrastructure and applications that will support the HR processes in the organisation. It is critical to make sure clean data is captured right at the time of sourcing candidates and that clean data is updated and refreshed throughout the life cycle of the employee and made available to all other HR systems including Learning & Development, Talent Management etc to leverage the full potential.

One of the biggest impediments towards the implementation of Predictive Analytics in a big way is the fact is the need for employees with the relevant skillset to interpret the datasets and highlight the areas of relevance for organisations. How do you believe organisations can address this aspect?

Contextualising Technology to address company-specific issues is a challenge that is being faced by every organisation today. We believe that there are two key approaches that can help organisations get the relevant skillset to leverage Technology in HR processes:–

a) Implementation of Technology Practice within the HR team such that there is an HR technology leader within the team and is responsible for embedding technology across HR processes and integrating them, and

b) Working with new-age Startups and HR tech companies who have innovated nimble solutions to address OEM complex challenges.

We have implemented both these approaches at Tata Technologies and noted some benefits.

Given the increased preference for HR in the boardrooms as a strategic partner to the business, do you believe that organisations will rely more on Predictive Analytics in the days to come? What areas of HR shall see a greater infusion of Analytical tools soon?

We believe that HR has always been seen as a strategic partner to the business and its importance has increased further in the current uncertain times. We see the adoption of Predictive Analytics and technology becoming more prevalent across every stage of the employee life cycle: Strategic manpower planning and forecasting, increased quality of hire, and competency-based hiring – eliminating biases in the selection process, to the management of talent, understanding the organisational and psychological needs of employees and further reducing attrition and redefining employee value proposition, to measuring skill and gaps in talent and effectively bridging the demand and supply.

 Employee Wellness was one of the most prominent issues that organisations needed to address during the pandemic. How do you believe Predictive Analytics can be made use of the HR departments for the holistic wellness of employees?

While there has been significant adoption of wellbeing programmes, technology wearables and gamified app-based solutions across other organisations – they are perhaps building ways to understand how people are doing, and helping combat employee wellness challenges by reducing stress, building cognitive capabilities and mental resilience. We have been focused on fostering a culture that values mental health, and becoming a mentally flourishing workplace. We are nurturing a culture that provides psychological safety – we are sensitising about what constitutes a healthy work environment, allowing them to be able to cater to the psychological needs (autonomy, relatedness, competence) of the employees. As for Predictive Analytics, we have benchmarked our wellbeing indicators in happiness, satisfaction, work-life balance, stress etc. that we intend to monitor periodically. We are also measuring the engagement and mood of employees and teams continuously and enabling managers to proactively work on areas that need improvement, to ensure engagement and mood scores remain an upward trend.

Data collection is one of the crucial aspects of Predictive Analytics. However, this brings focus on issues related to employees’ data privacy and protection. How must organisations tread along to uphold the element of data privacy and ensure that the Analytics tools are given access to employees’ data?

 Data is the backbone of Predictive Analytics, and we need to make sure clean data is recorded for relevant analytics. With clear data privacy laws and regulations now being available across most countries, we now have a framework of what we can collect and use and what we cannot. Organisations must implement this framework such that all internal systems, as well as vendor partners, follow relevant policies and control for data usage.

Internal clearances for empanelling of vendors are gathered only after the following security categories’ protocols are found to be satisfactory by our IT Security Team: Information Asset Protection, Identity, Entitlement and Access Management, Platform & Application security, Vulnerability Management, Privacy Information, Information Security, Industry standard certifications. Additionally, organisations must conduct awareness sessions for their employees and win their confidence on how the data is being leveraged for their good.

 Increased use of Predictive Analytics is seen to eliminate HR Processes in organisations. What according to you are the pros and cons for organisations doing away with manual HR processes?

 It does eliminate a few redundant steps, and it is helping us to do away with transactional activities, and empowering HRs to be more value-driven. While we can gather actionable insights, with the help of predictive analytics, it is allowing us to commit time to devise scientific/ strategic interventions.

- Pros: Better quality of hire, leading indicator in retention, improved workforce planning, better business performance

- Cons: High acquisition and maintenance cost of tools, reporting and governance.

It has been found that making use of training data sets to develop algorithms for various processes in HR brought in Cognitive Bias into the system. How must organisations bring in Predictive Analytics to overcome Cognitive Bias?

Most of the Predictive Analytics algorithms today have self-learning capabilities as the systems get implemented. So, organisations must make sure that when they define the training data for the analytics solutions, it is curated to represent the true profile of the organisation and then a pilot for at least a reasonable period to make sure that the algorithm training data sets are being updated through self-learning before it is deployed in full scale.