Methodology for allocating resources for data quality enhancement
Communications of the ACM
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Toward quality data: an attribute-based approach
Decision Support Systems - Special issue on information technologies and systems
Communications of the ACM
A product perspective on total data quality management
Communications of the ACM
Communications of the ACM - Supporting community and building social capital
Managing Business Process Flows
Managing Business Process Flows
On Data Reliability Assessment in Accounting Information Systems
Information Systems Research
Dual Assessment of Data Quality in Customer Databases
Journal of Data and Information Quality (JDIQ)
Managing Data Quality Risk in Accounting Information Systems
Information Systems Research
Data Quality of Query Results with Generalized Selection Conditions
Operations Research
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This paper addresses the issue of data quality management in information systems within an enterprise. Motivated by legislative mandates such as the Sarbanes--Oxley Act of 2002 on the reliability and integrity of the data and the enterprise systems from which the data are produced, we propose a process-based modeling framework to assess the impact of data errors in the business process information flow and the resulting data quality metrics. This framework is then integrated with a business control framework in which the placement and effectiveness of control procedures alter the propagation of errors and, ultimately, the quality of the data in the business process. This integrated framework enables mathematical formulations of managerial problems that lead to effective data quality control strategies. We develop a two-stage multiple-choice knapsack model as a special case, and we illustrate the model and analysis through a revenue realization process.