Collecting data and qualitative risk management

Cathrien Heusinkveld

I recently took part in the NICOLE fall workshop in Bristol, the theme of which was "Data & Risk". I was given the opportunity to present qualitative risk management as a method for substantiating choices which, within the framework of managing unacceptably high risks, have to be made in the targeted collection of data. Although I expected to be the odd one out by making a presentation in which research and data gathering are critically assessed, the struggle of dealing with large amounts of data proved to be a recurring issue. Given the ever-increasing scope of collectable data, we now more than ever need to make clear choices between the types of data that should be collected and why.

When I read the notice inviting me to contribute to the workshop, I noticed that, notwithstanding the scope of the “Data & Risk” theme, the main focus would lie on the risks involved in data usage and data collection. Nowhere did I read about a focus on my area of expertise, namely qualitative risk management in which risks are first assessed and prioritised and subsequently managed in a transparent, structured and cyclical manner. This almost withheld me from submitting an abstraction. So, why decide to go for it anyway?

The added value of qualitative risk management

I went for it because I believe that qualitative risk management generates added value in virtually every aspect of our work, especially when it underpins the choices we make in dealing with soil contamination. As soil experts, we tend to use soil surveys as a means of dealing with the unknown, for example in respect of soil quality, defining the contamination and establishing the extent of contamination. This makes sense and is often necessary. However, I make the case that we should refrain from immediately resorting to additional surveys whenever gaps are found in the conceptual site model. We should first stop, think and ask ourselves: “What do we want to achieve, and what do we need to achieve it?” In other words, what we need first is a risk assessment in which soil contamination and remediation are viewed in relation to the reason for remediation, the physical environment and the virtual environment (the stakeholders).

During a risk assessment we adopt a variety of perspectives from which to chart the risks of soil remediation (and yes, this includes the loopholes in the conceptual site model). We then prioritise the risks. Which risks are likely to occur? Which risks have potentially significant consequences for the planning, the budget, image, etc.? And which risks might have a major impact on the industrial site and the developments taking place there (often the reason for soil remediation)?

Prioritising the risks enables us to identify the highest risks, for which a plan of action can then be drawn up. The plan of action typically deals with risk control, limiting the risks or mitigating the possible consequences of those risks. This is when we have to consider whether or not to collect additional data as a means of controlling an unacceptably high risk. If so, then the need to collect data has been substantiated.

The highest risk in collecting data

During the various workshop presentations, many of my fellow participants presented impressive 3D animations and digital reporting methods with which the data behind tables and graphs where explained. Much attention went out to the manner in which highly diverse types of data (often data collected over the course of decades) can be linked and used. From all these presentations I was able to identify one overarching issue: the highest risk in collecting data is that the data is not or inadequately used due to the number of sampling points, the amount of gigabytes, the lack of gigabytes (analogue data), the variety in types of data and not knowing how to interpret data.

I believe that we can significantly reduce the risk of leaving (large amounts of) data unused by making risk-based choices in the data we acquire. We can then purposefully collect data within the framework of managing unacceptably high risks.

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