How data culture can save lives

Many companies strive for a “good” data culture in order to use data and AI successfully. The desired target state is usually described like this:

1. Employees understand the potential and limitations of data, analytics, and AI
2. They know how to work with data, share it, and share the insights derived from it
3. Leaders demand and actively support the use of data

You can wish for such a culture, but you can not mandate it. So if data culture can not be designed directly, what is the role of leaders?

The key is to use data culture as a seismograph that shows where value creation with data is getting stuck, and that helps reveal the root causes behind seemingly irrational employee behavior rather than fighting symptoms.

Once identified, you can design interventions that try to outwit the cultural hurdles you found. If these interventions work, the data culture evolves. Where exactly it develops, however, cannot be determined in advance. The organization is a complex system.

For interventions to have an effect, they must fit the current culture – they must connect to it. Such micro-interventions can look very different: from adjusting structural conditions such as KPI systems to introducing new data governance rules.

An example from our article (link in the comment): A hospital wanted to reduce medical errors and introduced a reporting and learning system (CIRS) for so-called critical incidents and near misses. But the system was hardly used (symptom). Appeals to staff did not help.

A data culture analysis uncovered the root causes: fear of blame and targets that prioritized treatment speed and left no time for documentation.

The interventions aimed to change the context: clear data governance guaranteed anonymity when submitting reports. At the same time, targets were adjusted to create time for documentation.

The consequence: critical incidents were documented and analyzed. New measures could be derived based on the insights.

The result: the error rate fell and fatal errors could be prevented. A new culture of safety and organizational learning based on data and analytics emerged – as a consequence of the changed conditions.

Does your data culture save lives, or does it keep people busy without moving anything forward?