The Resiliency Equation

We live in a risk-filled world. What comes to mind when you think about “Risk”? Mostly likely, it conjures a negative implication that is to avoid or reduce the impact of risk. Risk is an ever-present part of our daily life. Understanding and managing operational risks are essential to the future survival and prosperity of an enterprise. Enterprise can mean corporation, government entity or an organization. Operational risks are based on an enterprise’s internal operational environment.  With the regulatory spotlight on operational risks, there has been an ever-increasing attention devoted to the quantification of these risks.  With the emphasis on predictive analytics, more real-time performance monitoring and faster analysis, the application of Business Intelligence (BI) has come into play.

There is a wide array of statistical methods for measuring, modeling, and monitoring operational risk. Determining how all these statistical tools relate to one another and which to use is the challenge.  Unless, a risk manager is proficient at applying the myriad of statistical methodologies to assess operational risks, the task can be daunting.

This white paper is part of The LSH Group Diamond Series of reports.  It utilizes The LSH Group Diamond Model and an Enterprise Risk Management (ERM) methodology that is directed at the time-starved executive or manager who must quantify potential risks and other business impacts, and then set appropriate budgets aligned with strategic and business continuity plans at the enterprise level. It combines Business Intelligence and a simplified probability [P(x)] approach to quantify Enterprise Risk Management (ERM) that results in Business Resiliency. Thus, The Resiliency Equation: BI + P(x) + ERM = Resiliency.