The HR analytics guide

You didn't pursue a career in HR to get busy with statistics, yet HR analytics can be valuable within your HR policy. How do these insights help your business? How do you turb analytics into action items? This guide will help use HR analytics effectively.


  1. What are HR analytics?
  2. Why are HR analytics valuable?
  3. Concrete application: Absenteeism & The Bradford Factor

1. What is HR analytics?

What does HR analytics mean?

HR analytics or people analytics aims to support HR decisions with results from statistical analysis of HR data. These analyses are carried out on the basis of different types of HR data. On the one hand, objective data such as personnel data or trends like employee turnover and absenteeism. On the other hand, subjective data is also used, which contains information about the effectiveness of training courses or the involvement of employees, for example.

If you combine these human resources data with other relevant figures and use statistical methods to find connections between them, then you are talking about 'HR analytics'. Analysing these connections helps you to improve your HR policy, so that your employees perform better, function better and stay longer in your company.

How do you go from HR data to HR analytics?

Ask the right questions
Technological progress makes it easier to measure and store your HR data. The question then arises: what can you do with this data? You can start to carry out analyses at random, hoping to find action points in the results. However, if as an organisation you want to gain concrete benefit from these analyses, it doesn't work that way. Instead of starting from HR data, you should start by asking the right questions. After all, asking the right questions makes it possible to take targeted action based on various results. But how do you start asking the right questions?
Approach the problem from different angles
The first question to be asked is: "What business problem do you want to solve?". By starting from the source of a problem, it becomes clear where the priorities should lie.

According to Alec Levenson, researcher and consultant in HR optimisation, the focus is still too often on the individual. He advocates a multilevel approach that also takes teams and business units into account in your HR analysis. This gives you, as a company, a more complete picture of the situation, which in turn should lead to more concrete solutions. Individual problems often have underlying reasons that we have to look for at a higher level, namely the functioning of the company.
Example: Why do you have to take the bigger picture into account?
Suppose you want to tackle absenteeism and your HR analytics show that Mark is often absent. By focusing only on his data, you might decide that he is no longer motivated and act accordingly.

If you also look at his team, you might see that relations within the team have soured. By taking a further step back, you come to the conclusion that there is no real bullying policy in the company. In this case, previous actions are only temporary solutions. To ensure that such situations do not occur again, you need to make changes at the highest level.

In this way, HR analytics helps you to arrive at results and action points to have a real impact on business performance.

How can you implement the findings?

What do we ultimately do with the results of these HR insights? Although it seems obvious that something should be done with them, this is the step that is often forgotten. The results are stored somewhere, but remain there as forgotten data. With the result that no concrete action is taken.

People analytics should not be isolated from the rest of the business. Consistent cooperation between different parts of the company is therefore necessary to achieve positive results.

Alec Levenson believes that if we want to achieve results with HR analytics, engineers and HR managers must work together. In this vision, engineers help optimise the analytical processes and HR takes care of the communication.
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What about personal data of employees?

Because HR analytics uses personal data from employees, among other things, it is important that it is handled ethically. This means that the process must be fair and transparent. Always ask yourself: what do I want to achieve with the results and who will benefit from this?

Furthermore, decisions must always be accountable. Rules such as the GDPR ensure that employees remain protected against unethical practices.

2. Why is HR analytics valuable?

HR analytics helps you achieve your goals

Just like the company's other data-driven departments, your department contributes to the bottom line. The benefits of HR analytics can therefore be extremely valuable to other departments, and vice versa. After all, new insights help you to make more targeted choices.

Look for cause-effect relationships between HR data and data about the organisation's core business. This will give you an insight into the impact of training on the company's results, for example, or a better understanding of why a particular department is not achieving its objectives. All information that you can use strategically to take bigger steps forward as a company.

Data analysis is also very useful within HR. It can support the decisions and initiatives that you want to take on the basis of your gut feeling, or not, with objective knowledge. But HR always remains a people business: the information you obtain by talking to employees cannot be replaced by a database.

Bottom line is that both make each other more relevant: the value of HR increases with data, and HR data is strengthened by employee data.

Translate data into insights

Now that you know what HR analytics is, the insights it provides can help you make choices in your business strategy. Note: however useful HR analytics may be, you don't have to go all the way. Find out what your company needs most. Or get to work with the data you are already collecting. You can get more out of it anyway.

Operational reporting
Collect data on what you do. How many people have you recruited in the past year? What training did the employees go through? How much does everyone earn? Keeping track of these figures systematically, in an orderly way, is step 1.

Advanced reporting
Make it a habit to keep track of information that indicates how things are going in and with your organisation. Nowadays, there are tools that monitor the evolution day by day for you. This saves you the time of pouring all the figures into reports. For example, measure inflow and outflow, absenteeism, salary, costs and so on.

Strategic analysis
With statistical models you learn how HR data affects other data and vice versa. You link the activities of the HR department to the company's results. For example: what impact does sick leave have on customer satisfaction, or do satisfied employees ensure better sales results?

Predictive analysis (predictive analytics, predictive HR analytics)
With more advanced statistics and centralised data, you can also predict what you can expect in the coming period. You combine patterns and trends in the data with models that provide a view of the future. On the basis of this predictive analytics, you can plan better: is it useful to repeat a certain course of study? For which functions will you need to find new people in a year's time? Do you need to take action to maintain the satisfaction level of your employees?

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Translating insights into actions

Back to the reason for starting with HR analytics: to make your business more successful. So don't sit back once your report is on the table. Also important: clearly inform your colleagues and employees of what the analysis has taught you. The better they understand why the policy is evolving in a certain direction, the smoother adjustments will be made.

HR analytics in Officient

HR analytics is not one of the main modules in Officient. But although we mainly focus on HR administration, we do believe in the value of HR insights. We find that they make it easier to report and, more importantly, to detect problems early on. That's why we also offer insights in data on salaries, persons and absence.

3. Concrete application of HR analytics: sick leave

It is impossible to imagine a contemporary HR policy without HR analytics. Measuring is more than ever knowing. And nothing is more important than knowing how your employees are doing. After all, the success of your company lies in their hands, and you want to avoid burn-outs at all costs. But how do you chart absenteeism? These 6 figures will help you on your way.

What does absenteeism mean?

Absenteeism is a behaviour in which an employee stays at home due to illness. It includes all types of illness for which an employee is absent. Usually, however, we speak of absenteeism only if someone is excessively reports sick. There are four different types:
  1. White absenteeism: the illness makes it impossible for the employee to carry out his/her work. For example, surgery, flu, pneumonia. The reasons for the employee's absence are justified: he/she simply can't come in.
  2. Gray absenteeism: the employee is indeed ill, for example, due to headaches or a cold. The choice of whether or not to work is strongly influenced by the employee and his/her doctor. Motivation plays an important role in this type of absenteeism.
  3. Black absenteeism: there's no question of illness, but the employee decides not to come to work anyway. Black absenteeism is sometimes also referred to as fraudulent absenteeism.
  4. Pink absenteeism: occurs when a sick employee still comes to work. This can cause colleagues to get infected too. Moreover, the employee cannot rest and therefore cannot heal. This brings reduced productivity with it.

HR analytics to measure absenteeism

To prevent absenteeism, you first need to know what is going on. Thanks to these 6 HR metrics, you can calculate the absenteeism rate for your organisation:
1. Absence rate
The absenteeism rate calculates on what percentage of working days absenteeism occured. This is an interesting figure to keep track of every quarter or year, so you can closely monitor its evolution.

The formula = (number of days absence x 100)/ number of days per year on which you should work.
2. Absence frequency
The absenteeism frequency will give you insights into how often employees report sick each year. It will show you the average number of sick reports per employee.

The formula = number of sick leave reports per year/number of employees
3. Average absenteeism
The average duration of illness will tell you how long your employees are absent due to illness on average in one year.

The formula = total number of days absence per year/ number of work resumes per year
4. Keep track of numbers
Map out how long your employees are absent. Are you mainly dealing with long-term sick people or are they more likely to be absent for short periods of time? Compare this with known figures in Belgium. For example, our partner Securex issues an annual report on absenteeism. Compare your company to the reports of the current year: Are those figures the same in your organisation, or do they differ? 
5. Look for possible patterns
Try to pay close attention to certain patterns in your absenteeism. Keep a record of the days on which people report sick. For example, a large number of sick reports on Wednesday afternoons may show that there is a problem with childcare.

Although you won't be able to change much about some things, it is also possible that the cause is work-related. For example, a poor work-life balance or a general dissatisfaction at work can cause your employee to choose not to come to work even though it may be possible. Therefore, keep a good record of these data, both on a personal and organisational level.
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The Bradford Factor

The Bradford factor starts from the observation that it is more costly for an organisation when an employee reports sick for several short periods, compared to less frequent but longer periods. Short, unplanned periods of illness are often more difficult for a team or organisation to deal with.

In addition to measuring the total number of sick days per employee, the Bradford score provides insight into the impact of absenteeism.

The name 'Bradford' comes from the Bradford University School of Management, where the formula was developed in the 80s.
How is the Bradford factor calculated?
The Bradford Formula is: B = S² x D

  1. B = the Bradford Score
  2. S = the number of different periods of illness for a given employee
  3. D = the total number of days of illness for a given employee
The reference period is usually the past year, below you will find some examples:

Stef has been absent for 10 consecutive days during the last year due to illness.
The Bradford Score for Stef = (1²) x 10 = 10.

Julie has been absent 2 times for 5 consecutive days during the last year.
The Bradford Score for Julie = (2²) x 10 = 40.

Kurt has been absent 10 times for 1 day during the last year.
The Bradford Score for Kurt is the highest = (10²) x 10 = 1000.

In Officient, the Bradford Score is automatically calculated based on the available sickness data of each employee. Each day of illness is kept in the calendar that is sent to the payroll provider at the end of the month.
What is a good or a bad Bradford Score?
In general, the higher the Bradford Factor, the worse. After all, multiple short, unplanned periods of illness weigh more heavily. Sometimes organizations link certain threshold values to the scores, each time with a corresponding action, for example:
  1. Score from 51: verbal warning
  2. Score from 201: written warning
  3. Score starting at 401: last warning
  4. Score starting at 601: sometimes leads to dismissal
How do you work with the Bradford Factor?
In addition to the total number of days of illness, the Bradford Factor offers an interesting measure of absenteeism. Nevertheless, the Bradford Factor is sometimes criticized, especially if it is the only measure used.

Tackling absenteeism fits best within a broader policy. Loose actions do not always have a sustainable effect and employees sometimes react differently to actions taken. So always view the Bradford Score as a useful first step towards further analysis of the problem and finding possible solutions.