The Great Resignation, a term coined around May last year over the talent exodus in the US amid the pandemic, which later became a global phenomenon, had organisations in a quagmire. Reeling from the massive fallout of the pandemic, organisations were forced to also make up for the talent lost. With digital transformation high on the agenda, companies across the globe began to look at Predictive Analytics as a viable alternative to understanding employee sentiment.

In conversation with Human Capital, Puneet Khurana, HR Head, Policybazaar.com & Paisabazaar.com, elucidates the changes brought forth by The Great Resignation and how Predictive Analytics can prepare organisations to overcome talent attrition.

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 attritions?

The “Great Resignation,” an unprecedented nationwide trend of employees leaving jobs for greener pastures, has employers on their tails and is changing the way they approach talent retention. From increased Paid Time Off (PTO) and worker flexibility to higher average wages in a variety of professions, there has been a renewed emphasis on employee well-being and maintaining a positive culture. This has led to an emphasis on the need for Predictive Analysis.

Predictive Analytics studies employee performance by taking descriptive analytics evidence and using it as inputs for advanced techniques like statistical modelling and machine learning. These methodologies provide predictive measures such as flight risk, which quantifies the likelihood of an employee leaving the organisation within a specific time frame.

Predictive Analytics also uncovers hidden links between key factors that contribute to employee turnover. Pay, promotion, performance reviews, time spent at work, commute distance, and relationship with a manager are the most common predictor variables studied. External data, such as labour market indicators and the current economic scenario, are also used by organisations as causative variables when developing hypotheses and building retention models. The findings of the modelling are used by HR teams and managers to better design timely interventions to help retain employees.

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?

Figuring Out the business needs: It is critical to select and monitor HR metrics that are relevant to your company’s goals. If you are new to HR analytics, start with the more basic business questions, such as measuring absence and sickness leave, performance issues, and training costs. From there, you can select a suitable time frame to measure the data, such as quarterly, half-yearly, or annually, to obtain a sufficient amount of information to identify any emerging patterns or trends.

Identify the data needs & Source: Organisations store data at various locations, whether they are paperbased or a mix of different online platforms, which can make analysis difficult. To ensure that your analysis runs smoothly, you must first identify which data is required and then collect all the data from various sources so that they can be merged in a single location for better analysis. You will be able to extract the information more quickly and accurately this way.

Connecting Business Objective & HR analytics: To get the best return on investment from the analysed data, HR must use it as evidence that there is a link between the patterns or trends highlighted and issues in business operations in order to improve processes in line with business objectives. Then, to determine whether the data analysed produced accurate results, the analysis must be performed on a regular basis and the data must be translated into an easily understandable format that can be presented and understood by anyone in the business if necessary.

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 in the near future?

Predictive HR analytics can assist leaders in making informed decisions that foster an enthusiastic and highperforming workforce. HR analytics used effectively and ethically can help businesses identify, hire, engage, and retain quality employees who fit the company culture and are eager to contribute to its growth. The areas of HR that will see a greater infusion of Analytical tools in the near future are:

- Recruitment & Hiring

- On-boarding

- Workforce Planning & Management

- Predicting Attrition Risk

- Employee Exits

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 by the HR departments for the holistic wellness of employees?

Large organisations frequently lack knowledge about the existing workforce required to leverage their skills. Predictive modelling can reveal which employees are closely aligned to new roles and any knowledge or skill gaps that need to be addressed for a good fit with access to data about individual employees’ preferences, skills, and aptitudes. Survey items designed for skill and aptitude assessment can be used to collect this type of information. Other survey data can be used to identify employees who want to advance in their careers, either up or out of the company. Recruiting these employees for a new role allows them to advance in their careers without leaving the company. It is also crucial to understand the organisation’s informal network of information and influence brokers. This information is difficult to find using hierarchical statistics, but HR may identify informal leaders using techniques like Organisational Network Analysis (ONA) and survey items that ask employees who they go to for expertise and who they consider to be good performers. This data can be used to predict which employees will be effective change advocates and opinion leaders, as well as who should be kept on board during downsizing or transferred to a new function if their current position is abolished.

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?

As previously stated, human capital is one of the most significant company assets, and companies invest thousands of dollars towards training, nurturing, and enriching each employee’s skills. Using Artificial Intelligence (AI), HR teams can predict employee performance, fatigue, flight risk, and overall engagement, allowing for more productive and strategic conversations to support the employee experience, retention, and performance. With the right platforms, it is simple to incorporate AI into workflows to create smarter, more personalised schedules. This enables employees, particularly those in frontline/hourly positions, to take more control of their work/life balance by enabling self-service reporting. Using AI to handle these important but repetitive administrative tasks, such as reporting, relieves managers of their burden, allowing them to spend more time working with customers and training teams.

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

1. Develop organisation-wide definitions of fairness

There will almost certainly never be a single metric or universal definition of fairness that applies in all cases — in fact, the Google Developers Glossary lists more than 45 definitions for “fairness.” Begin by identifying the most important aspects of fairness in your organisation. Then, based on metrics and standards, develop several compatible definitions that work in various use cases and circumstances.

For example, if inclusion is a top priority in your organisation, you can begin by defining what inclusion means and looks like. Then, plan how you will measure inclusion — or lack thereof — so you can track and evaluate progress. This could be a good time to enlist the assistance of your analytics colleagues in terms of measurement techniques and data interpretation.

2. Promote fairness with a strong business case.

Explain why fairness is critical to your business’s success. Present your ideas in business terms and gain the support of trusted stakeholders who are known for making data-driven business decisions. Draw a clear line, for example, between fairness and diversity. 3. Embrace technology while accepting its limits. Technology can be an effective tool for detecting and correcting bias in human processes. It may never be able to ensure that all data-informed decisions are fair and that all bias is addressed, but do not let that stop you from taking advantage of it. Every day, new technology emerges that promotes fairness in data-driven HR decision-making. So, stay up to date on new product developments and create a portfolio of software, tools, and procedures aimed at increasing fairness.

4. Conduct fairness audits.

Fairness audits are objective, systematic examinations of a company’s people data policies, practises, and procedures. The objectives of audits differ. One goal could be to look for potential issues that could lead to legal action against an organisation. Another possibility is to look for ways to improve fairnessrelated practices. A fairness audit could be performed using software tools, with the assistance of an external expert, or even through an internal review. What matters most is that they are completed on a regular basis by someone who understands your organisation’s definition of fairness and that any issues or opportunities are taken advantage of.

5. Find the underlying cause of your problem.

When you find a problem, look for the underlying assumptions or processes that need to be modified. Biases are frequently unconscious, and unfairness is usually the result of more than one decision. This makes determining the underlying cause of a problem difficult.

6. Make diverse, cross-group collaboration a must

A diverse group will be more capable of identifying and resolving issues of unjust bias in systems. To reduce the likelihood of unintended biases creeping in unnoticed, include individuals with a diverse range of thought and experience at each stage of the process. Solicit feedback from across the organisation by bringing together experts from various departments such as HR, data, technology, and legal to identify and promote new or improved people-related data systems. Collaborate with vendors or in-house data experts to develop and improve operational practices and ethical standards that make the use of people-related data systems more equitable.

7. Keep the H in HR.

There is not — and should not be — a substitute for human judgement in HR decision-making in many cases. Human participation is required for fairness in recruiting, employee evaluation, and other HR decision-making.