Resistance to data-driven working is not a knowledge problem
5 psychological drivers you may be overlooking
- Article
- Data Strategy
Every data activation programme starts with the same assumption: colleagues are not using data insights because they do not know how. The usual interventions, such as training and communication campaigns, then fail because they are designed to address a knowledge gap. But what really causes resistance to data-driven working is rarely a lack of knowledge. It is a combination of psychological drivers that cannot be resolved simply by providing more information.
The five psychological drivers behind resistance to data-driven working
1. Loss aversion (resistance to loss)
Imagine you have reviewed the data, checked the analyses and made a well-founded decision, but in reality things turn out differently. That decision can quickly come back to you. You are the one held responsible.
A decision based on experience and gut feeling that turns out badly, on the other hand, is often seen as bad luck. The circumstances were unexpected, so nobody could have seen it coming.
Kahneman and Tversky described this mechanism as early as the 1970s: psychologically, losses carry more weight than gains. As a result, we are often more focused on avoiding mistakes than on achieving success.
People do not always choose what is rationally best. They choose what feels safest for them personally. Training will not change that. Culture might.
2. Cognitive load
New systems require more mental effort than familiar routines. Decisions you would normally make in seconds based on years of experience can suddenly take minutes when you have to use a new dashboard. Minutes you simply do not have on a busy Tuesday.
Under time pressure, the brain defaults to the automatic route. Not because people are lazy, but because it is efficient. The brain is designed to conserve energy. That is why a new system will almost always lose out to an established habit, unless it is easier to use than the current way of working.
3. Lack of perceived agency
Professionals who are good at their jobs rely on their own judgement. That judgement has been built through years of experience, context and nuance that no system can fully capture.
By agency, we mean the extent to which people feel they can make their own choices and influence how their work is done. When people experience agency, they feel more in control and more confident.
When a dashboard or algorithm replaces human judgement rather than strengthening it, resistance is a logical response. Data that tells you what to do without helping you understand why does not feel like support. It feels like control.
People want data that strengthens their judgement, not data that replaces it.
4. The wrong example (sociaal proof)
People do not look to policies to decide what behaviour is acceptable. They look at each other. If a manager does not visibly use data-driven approaches in meetings, or if the highest-performing colleagues in the team do not use them either, the social incentive to adopt the behaviour disappears.
Norms are shaped by the visible behaviour of people with status within a group. A training programme that fails to consider who sets those norms will almost always lose out to the informal culture.
5. Distrust triggered by a single moment
Trust in data is hard won and easily lost. One visible mistake at the wrong moment can be enough: a dashboard displaying demonstrably incorrect figures, a number that contradicts what everyone knows to be true, or a forecast that turns out to be wildly inaccurate. The result is not just doubt about that one outcome, but doubt about the entire system.
Data quality is therefore not only a technical issue. It is a psychological one too. Every quality issue users discover themselves costs more trust than a technical fix can restore later.
Where most data activation programmes go wrong
This is where most data activation programmes fundamentally miss the mark. They diagnose the symptom — low adoption — without understanding the underlying driver. They then choose the intervention that is easiest to organise: training. But the intervention should follow from the driver.
How to get people on board with data-driven working
Loss aversion requires a cultural intervention. How does the organisation distribute responsibility for decisions when data is available? That is a question about performance management, leadership and policy, not dashboards.
Cognitive load requires a design intervention. A solution should require less effort than the existing way of working, not more. That is a design challenge, not a training challenge.
Lack of agency requires a positioning intervention. Data should feel like a thinking partner, not an answer machine. What role does the user have, and how does the system support that role?
Social proof requires a leadership intervention. Who sets the norm? And do those people demonstrate the desired data-driven behaviour in situations where others can see it?
Distrust requires a quality intervention. Reporting problems needs to feel safe, and the response needs to be quick and visible. Trust is built through consistent behaviour over time, not through a communication campaign.
Why data-driven adoption should not be treated as an information problem
Organisations that treat data adoption as an information problem consistently invest in the wrong interventions. They provide training, run communication campaigns and are then surprised when usage figures have dropped again six months later. It is both ineffective and expensive.
Organisations that treat data adoption as an organisational and psychological challenge ask different questions. Not: do people understand the system? But: does the system make the desired choice easier, or does it ask more of people than it gives them in return?
That second question is harder. Answering it requires involvement from leadership and attention to culture, processes and product design. But it is also the only question that leads to lasting behavioural change.
Turn data strategy into lasting behavioural change
Getting people to work with data requires more than tools and training. We help you identify what is holding adoption back and translate your data ambitions into changes that work across leadership, processes and teams.
This is an article by Guus van Loon
With more than 10 years of experience in data, strategy and innovation, Guus helps organisations turn complex ambitions into sustainable business value. As Head of Research & Development at a market research scale-up, he worked on strategic challenges for international clients. He is a valuable bridge between technical specialists and business stakeholders, as well as a trusted adviser to senior management.
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