This article investigates the economic consequences of data errors in the information flows associated with business processes. We develop a process modeling-based methodology for managing the risks associated with such data errors. Our method focuses on the topological structure of a process and takes into account its effect on error propagation and risk mitigation using both expected loss and conditional value-at-risk risk measures. Using this method, optimal strategies can be designed for control resource allocation to manage risk in a business process. Our work contributes to the literature on both ex ante risk management-based business process design and ex post risk assessments of existing business processes and control models. This research applies not only to the literature on and practice of process design and risk management but also to business decision support systems in general. An order-fulfillment process of an online pharmacy is used to illustrate the methodology.
The quality of data contained in accounting information systems has a significant impact on both internal business decision making and external regulatory compliance. Although a considerable body of literature exists on the issue of data quality, there has been little research done at the task level of a business process to develop effective control strategies to mitigate data quality risks. In this paper, we present a methodology for managing the risks associated with the quality of data in accounting information systems. This methodology first models the error evolution process in transactional data flow as a dynamical process; it then finds optimal control policies at the task level to mitigate the data quality-related risks using a Markov decision process model with risk constraints. The proposed Markov decision methodology facilitates the modeling of multiple dimensions of error dependence, captures the correlated impact among control procedures, and identifies an optimal control policy. A revenue realization process of an international production company is used to illustrate this methodology.