Details of Research Outputs

Workflow is an important technology in managing business processes. As unintentional human errors, such as data entry error, and intentional errors that may constitute a fraud can be introduced through human interaction with workflow systems, ensuring data quality has become a prominent issue in workflow management. This has been echoed by the passage of the Sarbanes-Oxley Act that requires public companies to ensure that their processes, especially those involve financial transactions, to be strictly controlled. However, even with existing built-in workflow controls and rule engines, it is difficult to capture data errors and to detect fraud. To fill this void, we need to incorporate data quality assurance process into workflow systems. This paper creates a framework to determine proper workflow step for data quality review and approach for case sampling. We first investigate the data quality issues that are critical to workflow management. We then present an analytical model for allocating data quality review and case sampling in workflows. We use a real world example to demonstrate the proposed method. Moreover, we propose to apply the Statistical Process Control methodology, which has been widely adopted in manufacturing, for monitoring and reducing the variations of data quality in workflows. In addition, two different methods to implement the proposed method are discussed.