In a universe of huge risk diversity, organisations are frequently faced with the need to accept risk with little or no data upon which to base a decision. Without a systematised way to conduct a risk analysis, the response is often to produce a list of things that people anticipate could go wrong, which is rather like itemising the features of a new building instead of preparing blueprints. One obvious problem with this strategy is that it is hard to be certain your list is complete. It is also virtually impossible to determine which risks are more important than others.
Arium’s approach is different. Unlike many modellers, whose analytical framework of a problem is generally driven by the data available, Arium focuses first on understanding the nature of the problem at hand. Data often relate to what can go wrong – so many losses, so many aircraft crashes, so many hurricanes, and so on. Yet in many cases – particularly where there are new risks, or risks that are too infrequent to be analysed using historical data – data about what has gone wrong in the past will not prove overly useful for anticipating future events. To overcome this difficulty, Arium frames the question more positively: rather than focusing on the perils to be avoided we look at what is necessary for success. By re-framing the problem in this way we can discover new sources of data to attack the problem from a fresh angle, leading to solutions to problems that have not previously been modelled.