n the first article in a short series explaining how to use mathematical concepts to bring more accuracy to risk and impact assessments made for business continuity and enterprise risk management, Alejandro Aristizábal Correa looks at ways to calculate the probability of events.
Introduction
The emotional stability that certainty brings to us is so deep and lasting that it is difficult to compare to another concept of the natural order.
When it comes to information, no factors have such an impact and powerful consequences as those that allow reliable estimates of upcoming situations or future effects of decision-making.
Continuity and risk planning and organizational decision-making depend not only on the reliability of the information but also on the consistency and suitability of the methodologies and tools used. This means that the accuracy of information on which we base our decisions and projections is not enough. Rather, the models used must accurately fit the situation so that they can produce consistent results in every experiment and business situation.
Unfortunately, predictive models cannot always be tested within a realistic scenario. It is unusual for this to be the case; therefore, it is not expected that in practice the results will be the same as the theoretical calculations. Further, the models must ensure a sufficient degree of precision, so that we don’t need to cross our fingers when the expected time comes.
The tool used by enterprise risk management (ERM) and business continuity management (BCM) for the estimation of future events is known as a risk analysis. Despite the fact that its use is recurrent and its methodology is similar throughout the world, the variety of approaches used to calculate the probability of risks of the same phenomenon is wide enough not to consider the average of an interruption probability calculated independently by two risk experts, using the same model, as a professional practice.
