Risk management challenges #3: Data quality

As a risk management coach I see first hand some of the challenges organisations have when it comes to risk management. Many of the challenges are recurring and seem to be present regardless of times and trends. Other challenges come with changes in standards and expectations from stakeholders.

A challenge that will never cease to exist is the lack of good or sufficient data. In risk analyses, no matter how we look at it, we are somehow trying to predict the future. We do this because we think that if we understand what the future holds, we feel that we can control it. Further on, the feeling of control supports our certainty that we can better protect the values we manage and ensure we produce good results in our businesses.

In some instances, for example in a production line, or in a process with many repetitions and little variation in execution, we will be able to collect data about performance, non-conforming outcomes and the types of incidents that might occur. Based on such data we will be able to understand more of the uncertainty spectrum in our process, and by that we can be able to produce statistics and project assumptions about the future into our risk analyses.

Data quality in risk management

However, in many organisations, processes are performed on irregular basis, with a certain variation, because the production and services provided vary. Take for instance an organisation that provides road construction services. Each location and each project will entail different requirements. The ground conditions might require different approaches, the weather conditions might introduce different risks and different clients might have different requirements to how to perform the work.

On the same note, a health service provider will have the same challenge. Each patient will be different, both when it comes to psychological needs and physical needs. Each nurse will have a different effect on the patient as well as having slightly different techniques for performing different practical tasks. In instances such as both construction services and health services, getting data which has the quality needed to project future incidents, will prove difficult.

Based on this it is safe to say that performing a risk analysis of a construction site or a health service department where the variables are unlimited is different from the example with the produciton line. In the production line you can be more certain that the quality of the data you put into the risk analysis will give you information, which to a high degree may be projected intor valid assumptions about the future. The three scenarios require different approaches both in assessment technique and in communication to decision makers.

From my experience, the challenges with data quality are difficult to erase completely from the equation. However, I have found a few techniques that have worked for me in the environments I have experience from. Firstly, I focus very much energy into creating a facilitating ambience in risk meetings. By priming the group to being open, creative and reflective I find that more