Just imagine if risk professionals had the ability to isolate symptoms of risk – rendering risk professionals as “neuro-scientists” of uncertainty. Does this seem far-fetched? I have been experimenting by breaking down known risk events with empirical data into aspects. Aspects are elements of an activity, product, or service with the potential to cause an impact, either positive or negative, which we can term “symptoms”.
The interesting thing about these symptoms is that they are traceable, like a riskchain that has its origins in a contextual realm comprising internal, external, and both known and unknown factors. A riskchain presents itself as a series of interrelated elements that – when combined – manifest as a real risk which has a causal implication, be it positive or negative. Once we understand the riskchain, we can build up a library of configurations by sector (e.g. bottle manufacturing, offshore gas drilling, tunnel boring etc). The process of configuring riskchains is tedious, and currently painfully manual, but it has the potential to be machine learned.
While uncertainty continually morphs, it leaves behind a traceable scent that risk professionals can isolate – building a library of riskchains that, over time, can be utilised to augment decision-making with high success rates faster than we can currently process or postulate.
As stated in ISO.EIC 31010:2009, there are already various tools and techniques at our disposal that could help in estimating the level of uncertainty and the decisions to take based on qualitative, semi-quantitative, and full-on quantitative modelling.
From here, we can conclude that the future of risk management is veering from traditional thinking based on milestones, schedules, and activities, towards a focus on understanding the symptoms of uncertainty and its collective construct.
Originally published as LinkedIn Pulse, “Uncertainty: the eternal shapeshifter” by Simon Van Wyk.