For years, the human resources function has been considered to be a ‘soft’ functions and not subject to the rigorous, black-and-white data-driven techniques that other fields – such as Sales, Marketing, Finance etc. – have been. Historically, we haven’t been able to measure definitely the things that we intuitively believe to be true. But businesses are mandating it. HR is being held accountable to deliver business results. And the language of the business is Analytics. There is a growing mandate for sophisticated analytics interventions to be applied to HR – beyond simply reporting what already exists in an organization.
Business strategists are looking at analytics to help the HR function more efficiently acquire and utilize the human capital at its disposal – from science-driven hiring processes to more efficient resource deployment and managing attrition of talent.
Today, multiple applications of analytics and artificial intelligence in the field of HR exist – and in a sense, the functions and processes performed by HR teams are getting a much needed shot in the arm through these two techniques. From talent acquisition, retention and attrition management, a myriad of poignant use cases exist for the application of analytics in HR. Let us look at a few areas where data and analytics are combining effectively to augment how corporations maximize their ROI on human capital across the employee lifecycle:
The goal for analytics here is to create a model that can predict possible career progression and trajectory of an individual based on his or her previous experience, industry expertise, education, professional skills and achievements etc. The modeling process requires an extensive data of prospective employees as well the successful trajectories seen of employees in the past. The data would need to ensure a solid base for analysis, including vacancies, job descriptions, available candidate resumes and much more. In addition to relying on resumes as a source of data, analytics can also help furnish insights from LinkedIn profiles, which can provide an even more thorough evaluation of individual talent characteristics – including industry experience, skills and competence – to define and attribute factors that are more and less important for a particular career model.
With all this information available, we need to then progress to create a complete picture of a person’s professional expertise and compare it against similar available job descriptions – focusing on the career path. This way we can discover both – the best matching positions for every level of competence and the skills and experience that are required for an expert to move up the corporate hierarchy.
Analytics in HR can also be an extremely beneficial resource to inform training and certification strategies for corporations. If we see from the data that specific skills are in extremely high demand, recruiters and HR consultancies can advise businesses circumstances in which there may be long term benefits for investing in training resources and programs rather than seeking out new hires. In addition to external talent augmentation – which can often be difficult and time-consuming – training existing employees can more economical as well while boosting organizational morale and productivity.
Learning Analytics is a sub-aspect of HR Analytics which can inform the development of every aspect of employee education. Learning analytics can help develop a better understanding of employees, building more relevant and impactful training programs and having more insights into the interaction between employees and the software. Further, it can help gauge what is working in employee training and what is not. Analytics can also be applied to determine training effectiveness and internalization. An internal matrix used to measure employee comprehension can be developed, with key benchmarks and targets. For example, which learning modules do employees typically struggle with? Evaluating engagement is also equally important and helps keep a track of whether employees are interacting with the content or not. Such feedback can be extremely instructive for authors of e-learning courses. Data on individual exercises, engagement and results can provide developers with important insights into how learners use the course and help them optimize content delivery.
Across industries, we are seeing a steady and constant shift towards ensuring that employees’ compensation is closely tied to their contribution to the organization. Numerous organizations are removing the pay limits that constrained high performers from earning more than their managers. A metric called the “Performance-based compensation differential” is increasingly being implemented which expresses how much more high performers are paid compared to their average peers. For example, a score of 1.3 means high performers typically receive 30% more compensation than average performers. Turning this critical question into a single number allows for powerful insights across the organization, and that groups of employees can be easily compared using simple visual analyses, irrespective of locations and business units.
During the annual pay review cycle, most HRIS systems will allow you to enter changes in pay. But these systems do not enable you to analyze how these hikes relate to performance and whether or not they are aligned to organizational goals. Most traditional HR departments provide guidance and then trust their managers to get it right. By analyzing these decisions, reporting those back to the organizational leadership and then revising these adjustments before confirmation we can ensure that the budget increases going into labour costs are being applied optimally and fairly.
The big opportunity for analytics in HR is to add value by stopping the wrong outcome from happening, rather than simply reporting on what has happened. One such instance of prescriptive and predictive application of HR analytics is for managing attrition of high-performing talent. For example, according to a research by PWC Saratoga and CEB, the cost of voluntary turnover has been established at approximately 1.5 times annual base pay for salaried employees. That is to say, if you prevent two high value employees, with salaries of $50,000, from leaving the organization you have saved approximately $150,000. This saving can be caught much earlier by applying predictive models to identify who might leave the company, before they leave.
Sophisticated algorithms that can use historical data to determine the likelihood that someone will resign are crucial to attrition management. There is often a clear set of known actions that will prevent talented employees from leaving – signing bonuses, formal agreements around career progression and learning opportunities. The crucial part of course, is knowing to whom you should offer these incentives. Once you focus on the right population, through powerful and validated statistical models, you can achieve better outcomes at lower costs.
Another area where analytics is becoming valuable and important for corporations is employee retirement and succession planning. This facet is similarly important to attrition management, specifically when these people are in key roles or hold key relationships that are critical for business continuity. On the flipside, it is a challenge to also keep a potential successor waiting, if the incumbent chooses not to retire at the time expected.
Old indicators of age and tenure to estimate retirement behavior are less applicable today. Modern analytics and data science can help apply algorithms that take into account many additional factors such as recent changes in role, pay level, rates of change in pay and incentive eligibility to refine the predictions of who will retire. Companies using this type of analytic approach tend to be more successful and effective in managing the retirement cycle and ensuring that key roles have a successor ready at the right time.
It is increasingly becoming mainstream to base the intricacies of organization culture based on data and insights. This is the brave new world of HR analytics, which helps companies understand the relationships—external and internal—that drive corporate decision-making around human capital. Once a company understands the behaviors that correlate to success, they can be measurable and predictable – and therefore manageable.
Author-Sameer Dhanrajani is widely recognized analytics and AI thought leader and known for his deep knowledge, innovation & topical approaches in the analytics and AI space. Currently he is Chief Strategy Officer at Fractal Analytics. Prior to this, he was Global Business Leader for Cognizant analytics and data sciences unit at Cognizant Technology Solutions. Sameer is a distinguished member of the NASSCOM Analytics Special Interest Group, CII knowledge roundtable, ASSOCHAM digital council and has been instrumental in leading the effort for positioning India as the next best destination of choice for AI & Analytics. He is the first analytics professional leader in India to deliver TED talk. His recent book “AI and Analytics: Accelerating Business Decisions “published by Wiley is a best seller and have been featured amongst top books in AI and Analytics space.