"Use data not as an answer, but as a starting point for asking better questions"

Absence is a system problem. It is influenced by factors such as workload, autonomy, job design, psychosocial strain, team dynamics, leadership and the balance between work and private life. If you only look at the absence rate, you miss those underlying patterns. Data can help to reveal them.

But data doesn't tell the whole story. I often put it this way: data shows you where the smoke is. Conversations show you where the fire is burning.

The strength of a data-driven approach

Suppose the data shows that a particular group of employees is at increased risk of dropping out. That doesn't automatically tell you why. Data makes a cause plausible. You then need to test that assumption against reality, for example in conversations with employees and managers. And that’s exactly the strength of a data-driven approach: not pretending that data provides all the answers, but using it to ask better questions.

Connecting sources

To do that, we first need to break down the silos that data often sits in today. Absence data is in one place. HR data on contracts, leave and training is in another. Information from engagement, satisfaction or psychosocial surveys is somewhere else again. Only when you connect those sources does a more complete picture emerge of what is happening within an organisation.

From signal to result

And that's where the interesting part begins. Because insight alone changes nothing. The real chain runs from signals, through insight and action, to results. If you put an intervention in place, for example, you don't just want to know how many employees took part. You want to know whether it actually changed anything. That's why a simple before-and-after comparison is often not enough. If absence falls after an intervention, that may also be due to chance, seasonal effects or a general trend. By comparing employees who received an intervention with a similar group who did not, you can judge much more reliably whether the difference is really down to the intervention.

Data as a foundation for learning

For me, that is the holy grail of data: not measuring as much as possible, but getting better and better at knowing what works, for whom and under what circumstances. In one large Dutch organisation, this approach delivered a return of more than eight times the investment: an investment of around €1.4 million against savings of approximately €12 million. We compared two groups of employees on their absence behaviour, meaning how often they called in sick and how long they were off. The group that had received an intervention showed 'better' absence behaviour than the other group. Our conclusion: there is reasonable evidence that we contributed to the improvement. In plain English: it isn't proven, but it is highly likely that the approach helped. This result is indicative and can’t be transferred one-to-one to other organisations. That is precisely why it is so important to measure effects within your own context.

So I don't see data as an end point. Nor as a cure-all: data doesn't tell you everything. There is always a part we can't see, a grey area. What someone doesn't tell their psychologist, for example. Data is the foundation you learn from. It helps us spot risks earlier, understand better what is going on and act in a more targeted way. With one big added bonus: data shows whether something works.

In the whitepaper 'From absence management to work wellbeing – Take the step with the Colbe Work Wellbeing Capability Model: get a grip on your approach, focus on prevention, demonstrate impact', you can read more about the added value of the data-driven approach Nèwel describes. Read it here: