MODELING OPERATIONAL RISK IN DATA QUALITY (Practice-oriented paper)

Abstract: In this paper, we address how data quality (DQ) is likely linked to failed business processes that pose operational risks to the Enterprise system. Operational value at risk (OPVAR), which is used in the finance literature to mean how much we might expect to lose if an event in the tail of the loss probability distribution does not occur, can be used to conduct Enterprise software reliability and damage function analysis. This paper explores (a) how to combine distributional assumptions for event frequency and severity to derive software loss cost estimates using the familiar example of software processing errors and (b) how to utilize the estimates of this distribution to estimate OPVAR-based losses. The empirical results show (a) that it is possible to fit DQ problems, such as the daily mishandling event data, to a distribution and to use maximum likelihood analysis to derive a consistent set of critical event count thresholds and (b) that the resulting OPVAR-based losses can be used by DQ managers to ascertain the real costs of mitigating DQ problems