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Using Data to Find Out Why Employees Are Absent

Introduction

Imagine a company where employees are regularly absent from work. So, managers have to find replacements, other employees have to take on extra work, deadlines get delayed, and sometimes overtime costs increase. HR knows that absenteeism is becoming a problem, but there is one big question. Why are employees actually absent?

So, the company starts guessing. Maybe employees are taking too much leave. Maybe they are not motivated. Maybe they want more money. Maybe they are simply not serious about their jobs. But what if none of these are the real reason?

This is where HR Analytics can become very useful. Instead of HR saying, "I think this is why people are absent," HR can use employee data to find patterns. So, HR can look at when employees are absent, where they work, how often they are absent, how long they take leave, when they take vacations, and what happens before and after the absence.

The interesting part is that data can sometimes show something very different from what people expected.

The Problem

Absenteeism basically means employees being away from work when they were expected to be there. Some absence is completely normal. People get sick, have family responsibilities, take holidays, or sometimes need personal time.

So, the problem is not that employees take leave. The problem is high or unplanned absenteeism that starts affecting the business and other employees.

Imagine a factory where five employees are absent on the same day. The company still needs to complete the same amount of work, so the remaining employees may have to work more. If this happens again and again, employees can become tired, productivity can fall, and overtime costs can increase.

Now imagine HR looks at the monthly attendance report and sees that absenteeism is high. That tells HR what is happening, but not why it is happening.

This is where many HR teams can make a mistake. They may immediately create a strict attendance policy or reduce leave benefits. So, the company tries to control the symptom without understanding the actual cause.

Analysis of the Problem

The first thing HR needs to understand is that absenteeism can have many different causes. So, it is not useful to treat every absence as the same.

For example, if employees in one department have much higher absence than employees in another department, HR should ask why. If absence is higher on certain days of the week, HR should investigate that pattern. If employees who take very little annual leave later have more unplanned absences, that is also something worth studying.

HR can therefore look at data such as absence frequency, absence duration, department, location, job role, shift, overtime, leave usage, season, day of the week and employee tenure.

The next step is to compare these patterns.

So, imagine HR discovers that employees who work very long shifts have more unplanned absences. That gives HR one possible area to investigate.

But imagine another company discovers that employees who take very little vacation actually have more unplanned absences later in the year. That would be very different from what many managers might expect.

This is why HR Analytics should be about testing ideas rather than assuming answers.

And there is another important point. Data should not be used to blame employees. If HR finds that a particular group has high absenteeism, the next question should be, "What is happening in this group?" and not simply, "How do we punish these employees?"

The Real-Life Case: E.ON

A very interesting real-life example comes from E.ON, a large German energy company. The company had more than 43,000 employees in the case study, and its absenteeism had risen above the benchmark that HR considered acceptable. So, the company wanted to understand what was driving the increase.

Instead of starting with one explanation, E.ON's people analytics team created 55 different hypotheses about what might be connected to absenteeism. So, they were basically saying, "There could be many reasons. Let's test them."

Out of the 55 hypotheses, the team was able to test 21 using the available data. From these, 11 were validated.

One of the most interesting findings was about holidays.

The team found that the duration and timing of holidays had an important connection with later unplanned absence. Employees who did not take a longer holiday during the year, or who did not take occasional short breaks, showed a different absence pattern later on.

This was interesting because the company had also considered whether employees selling unused vacation days might be causing the problem. But the analysis did not find a statistically significant link between selling unused vacation days and absenteeism.

So, the data challenged what could have been an easy assumption.

Instead of saying, "Employees are absent because they are taking too much leave," the data suggested that not taking enough proper breaks could also be connected with later absence.

The insight was then shared with managers so that they could improve how holiday approvals were handled.

This is a great example of how HR Analytics can change the question from:

"How do we reduce leave?"

to:

"What patterns in leave and work are connected with unplanned absence?"

HR Theories, Frameworks, Models and Concepts Reflected in the Problem

One useful concept here is attendance behaviour. Attendance is not always just about an employee deciding whether they want to come to work. It can be affected by many personal and workplace factors. So, HR needs to look at the wider situation around the employee.

Another useful concept is workforce productivity. When one employee is absent, the effect may not stop with that person. Other employees may need to cover the missing work. So, HR should look at absenteeism as a workforce issue rather than only an individual attendance issue.

A useful HR metric here is the Absenteeism Rate. In simple terms, it tells us how much scheduled work time is being lost because employees are absent. HR can compare this rate across departments, locations, shifts or time periods.

Another useful measure is absence frequency. This looks at how often employees are absent. So, two employees could both have ten absent days, but one may have taken one ten-day leave while the other may have taken ten separate one-day absences. These two patterns could mean very different things for workforce planning.

We can also look at absence duration. This tells HR how long each absence lasts. So, HR can understand whether the problem is mainly short, repeated absences or longer periods away from work.

Another important concept is seasonality. Some HR problems change depending on the time of year. Absenteeism may be different during winter, summer, festival periods, school holidays or busy business periods. So, looking only at the yearly average can hide useful patterns.

Then there is correlation. This means checking whether two things move together. For example, HR might find that overtime and absenteeism increase at the same time. But this does not automatically mean overtime caused absenteeism. It simply gives HR something that needs more investigation.

This is where causal thinking becomes important. HR should ask whether one factor is actually causing another or whether both are connected to something else. So, if absenteeism is higher in one department, the reason may not be the department itself. It could be shift timings, workload, manager practices, location or the type of work.

Solution Using HR Theories, Frameworks, Models and Concepts

The first step should be to create a clear absenteeism problem statement. HR should decide exactly what it wants to understand. For example, the question could be, "Why has unplanned absenteeism increased in the production department during the last 12 months?"

The next step is to collect the right data. HR can combine attendance records with information about shifts, overtime, leave, department, job role and other relevant workforce information. So, instead of looking at one attendance spreadsheet, HR creates a bigger picture.

Then HR can break the data into different groups. It can compare departments, locations, shifts, months, days of the week and absence types. This is called segmentation. It is simple but very useful because the overall company number may hide smaller patterns.

HR can then create hypotheses. For example, HR might ask, "Does overtime increase the chance of absence?" Another question could be, "Do employees who do not take enough vacation have more unplanned absence later?"

Instead of deciding that the answer is yes, HR tests the idea using data.

HR can also use benchmarking. This means comparing the company's absenteeism with an internal target, previous years or an appropriate external benchmark. So, HR can understand whether the problem is actually unusual or whether it is within the normal range for that workforce.

The next step is to turn the findings into action.

If the data shows that one shift has unusually high absenteeism, HR could study the shift pattern, workload and staffing levels. If the data shows a connection between high overtime and absence, managers could review scheduling. If the data shows that employees are not taking enough breaks or holidays, HR could encourage better leave planning.

So, the solution should be based on the reason behind the absence, rather than treating every absence as the same problem.

The Actual Solution Implemented in Real Life

In the E.ON case, the people analytics team did exactly this type of investigation. Instead of assuming one reason for absenteeism, they developed many hypotheses and tested them using available workforce data. Out of 55 hypotheses, 21 could be tested and 11 were validated.

The findings around holiday behaviour were then communicated to managers. The company used the insight to improve its approach to holiday approval, with the idea that employees should have opportunities to take proper breaks instead of allowing patterns of insufficient rest to continue.

What I find most interesting about this case is that the solution was not simply, "Make employees come to work more often." The data suggested that better rest and better holiday patterns could be part of the answer.

There is also another real-life case that shows how analytics can lead to a more direct operational solution. A European shipping company had high absenteeism among port security officers. The company had already tried competitive pay and revised employment contracts, but the problem continued. Its HR team then combined job analysis, focus groups and Excel-based absenteeism tracking. The analysis suggested that the problem was more connected to job design and role clarity than compensation. The company redesigned the roles, and the reported result was a 6% reduction in absenteeism and a €350,000 reduction in contractor costs.

So, these cases show two different but important lessons. E.ON used workforce data to understand patterns around absence, while the shipping company combined data with employee feedback to understand what was behind the numbers.

Conclusion

Absenteeism can look like a very simple HR problem. Employees are absent, so HR should make them come to work. But it is actually much more complicated than that.

So, before HR creates a new attendance rule, it should ask:

When are employees absent?

Where is absenteeism highest?

How often are employees absent?

How long are they absent?

Is there a pattern?

What could be causing that pattern?

And most importantly:

What does the data tell us that our assumptions did not?

The E.ON case shows why this matters. The company tested many possible explanations and found that some common assumptions did not hold up, while holiday patterns gave useful information about absenteeism.

So, HR Analytics is not just about making a colourful dashboard with attendance numbers. It is about using those numbers to ask better questions.

For me, that is the most interesting part of People Analytics. The data does not tell HR to blame the employee. It tells HR where to look deeper.

And sometimes, the reason employees are absent is not that they want to work less.

It may be that the way the work is designed is making it harder for them to keep showing up.

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