Semicon: Data and Answers

HR Analytics: When HR Has the Data but Not the Answers

Imagine an HR team that knows employees are leaving the company, but does not really know why they are leaving. So, every year, HR hires more people, conducts exit interviews, gives retention bonuses, and hopes the problem will improve. But the same thing keeps happening. People keep leaving.

This is where HR Analytics becomes important. HR Analytics means using employee data to understand what is happening in the workforce and then using that information to make better HR decisions. So, instead of HR saying, "I think employees are leaving because of salary," HR can actually look at the data and ask, "Are people with lower salaries leaving more? Is it happening in one department? Is it happening under certain managers? Does it happen mostly in the first year?" This changes HR from being based mainly on opinions to being more data-driven.

The Problem

One of the biggest problems for many companies is employee turnover. Employee turnover means employees leaving the organization and being replaced by new employees. Some turnover is normal, but when too many employees leave, it becomes expensive and difficult for the company.

The problem becomes even bigger when HR only looks at the overall turnover number. For example, suppose a company has a 20% turnover rate. HR knows that 20% of employees are leaving, but this number alone does not tell us much. So, which employees are leaving? Are they young employees? Are they experienced employees? Are they from sales? Are they leaving because of their manager? Are they leaving because of low pay? Are they leaving because they cannot see a career path?

If HR does not ask these questions, it may use the same solution for everyone. So, the company may increase salaries for all employees when the real problem is poor management in one department. Or it may introduce another engagement survey when the real problem is that employees cannot see opportunities for growth.

This is where HR Analytics can help.

Analysis of the Problem

The main issue is not simply employee turnover. The deeper issue is that HR may have a lot of employee data but may not be connecting that data to actual business problems.

Most companies already have information about employees. They may have data about salary, age, department, performance, attendance, tenure, promotions, engagement surveys and resignation dates. But so what if this information is sitting in different Excel sheets or different HR systems and nobody is actually studying it?

For example, HR might find that the company has high turnover. But after breaking the data down, it may find that turnover is very high among employees who have worked for less than one year. So, suddenly, the problem looks less like a general "retention problem" and more like an onboarding and early employee experience problem.

In another case, HR may find that employees under two particular managers are leaving much more often than employees under other managers. So, the problem may not be salary at all. It could be leadership style, workload, communication or lack of support.

This is why HR Analytics is useful. It helps HR move from asking "What is happening?" to asking "Why is it happening?" and then "What should we do about it?"

A Real-Life Case: Semicon India

A very interesting real-life case comes from Semicon India, the Indian subsidiary of an American semiconductor company. The company had a serious employee attrition problem. Since it was a skill-based business, losing employees was not just an HR problem. It could also affect the company's work, knowledge and business results.

The company brought in a human capital strategist to understand why employees were leaving. Instead of simply assuming that employees were leaving because of salary or better opportunities, the team used a data-driven approach to find the actual reasons behind the turnover. The case was later published by Singapore Management University as a real-life workforce analytics case.

The interesting part is that the HR team did not start with the solution. They started with questions. So, they looked at the relationship between employee turnover and different factors in the organization. They also looked at how HR policies could be connected to the reasons employees were leaving.

The case shows an important lesson. Sometimes HR already has many possible explanations for a problem, but those explanations are only assumptions until the data supports them. The company therefore had to create clear questions, identify the right data, test its assumptions and then use the results to recommend actions.

HR Concepts Reflected in the Problem

The first major concept here is HR Analytics. HR Analytics means using employee data to understand workforce problems and make better decisions. It can be as simple as comparing turnover between departments or as advanced as using predictive models to identify employees who may be at risk of leaving.

The second concept is turnover analysis. Instead of looking at one overall turnover percentage, HR can break it down by department, job role, age group, tenure, manager, salary level and other factors. So, a 20% company-wide turnover rate may actually hide a 35% turnover rate in one department and only 8% in another.

The third concept is predictive analytics. This goes one step further. Instead of only studying who has already left, HR can use past data to identify patterns that may be connected with future turnover. For example, an organization may find that employees with low engagement, long working hours and no career movement have a higher risk of leaving.

The fourth concept is evidence-based HR. This means HR decisions should be based on reliable information rather than only personal opinions or assumptions. So, instead of a manager saying, "People are leaving because they want more money," HR can actually test whether compensation is connected with turnover.

The fifth concept is HR metrics and KPIs. Metrics such as turnover rate, voluntary turnover, absenteeism, tenure, time to fill and retention rate help HR understand what is happening. But the important part is not simply collecting numbers. HR has to understand what the numbers mean and what action should follow.

Solution Using HR Theories and Concepts

The first step should be diagnosis. HR should not immediately introduce a new policy. It should first understand the problem.

For example, HR could divide turnover by department, job role, manager, age group, salary range and tenure. If one department has much higher turnover than others, HR can investigate that department more closely.

The next step is to look at employee engagement. Employees who feel disconnected from their work or organization may be more likely to leave. HR can use engagement surveys, employee feedback and exit interviews to understand what employees are experiencing.

Another useful theory is the Job Demands-Resources Model. In very simple terms, this model says that employees have job demands, such as workload, stress and long working hours. They also have resources, such as manager support, recognition, learning opportunities and autonomy. So, if demands are very high and resources are very low, employees may become exhausted and disengaged.

HR could therefore compare turnover with factors such as overtime, workload, manager support and engagement scores.

Herzberg's Two-Factor Theory can also help. Employees may leave because of problems with salary, policies, working conditions or management. These are linked to hygiene factors. At the same time, employees may also leave because they do not get recognition, responsibility, growth or meaningful work. These are motivators.

So, if the data shows that employees are leaving because they have no career growth, simply increasing salary may not solve the whole problem.

HR can then create targeted interventions. If turnover is high among new employees, the company could improve onboarding and introduce 30, 60 and 90-day check-ins. If turnover is high under certain managers, HR could provide manager training. If employees are leaving because of career growth, HR could create clearer internal career paths and development plans.

Finally, HR should measure whether the intervention actually worked. So, the process becomes:

Collect data → Find the problem → Identify the reason → Take action → Measure results → Improve the solution.

What Was Actually Done in Real Life?

Semicon India's case shows this process in action. The team used workforce analytics to investigate the causes of high attrition instead of simply relying on assumptions. The case involved identifying turnover drivers, examining HR policies, creating hypotheses, collecting the required data and connecting workforce information with business performance.

There are also other real-world examples showing how this approach can work. At Experian, high attrition had become a major problem, and traditional workforce management methods were not giving HR enough information about its root causes. The company used predictive workforce analytics to study employee data and identify patterns connected with attrition. According to the published case study, Experian's global attrition rate fell by 4 percentage points by 2019, with reported savings of CAD$14 million over two years.

Another example comes from a healthcare-related workforce analytics case. Workpartners combined different types of employee information, including tenure, job characteristics, compensation, engagement, absence and safety data. Its model was reported to identify call-center employees who were likely to leave within 90 to 180 days with 90% accuracy. The organization then used this information to target interventions. During the following seven months, monthly turnover in the intervention group fell by 17.5%, compared with a 4.6% decrease in other high-turnover departments that did not receive the same interventions.

Conclusion

The biggest lesson from HR Analytics is that HR should not stop at collecting data. A dashboard showing turnover, salary and headcount is not enough. The real value comes when HR uses the data to ask better questions and solve actual employee problems.

That is what makes HR Analytics more than just numbers and Excel sheets. It allows HR to understand people problems in a more clear and structured way and connect HR decisions to actual business results.

For me, that is the most interesting part of HR Analytics. The data does not replace the human side of HR. It helps HR understand the human side better.