Enterprise Architecture Analysis for Data Accuracy Assessments

Abstract

Poor data in information systems impede the quality of decision-making in many modern organizations. Manual business process activities and application services are never executed flawlessly which results in steadily deteriorating data accuracy, the further away from the source the data gets, the poorer its accuracy becomes. This paper proposes an architecture analysis method based on Bayesian Networks to assess data accuracy deterioration in a quantitative manner. The method is model-based and uses the ArchiMate language to model business processes and the way in which data objects are transformed by various operations. A case study at a Swedish utility demonstrates the approach.