Improving financial data quality using ontologies
Decision Support Systems
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In SOA (Service Oriented Architecture) and RTE (Real-Time Enterprise) environment, an assurance of data quality is important. Because we do not assure data accuracy among dynamic clustering data set. Traditional methodology for assuring data quality is data profiling and data auditing. However, that is needed lots of time and cost to analysis of metadata and business process for integrating system before evaluating data quality. In this paper, we propose an efficient methodology of assuring data quality with considering dynamic clustering data set. To extract evaluate rules for data quality, we use ontology that has meanings of each word in itself. We gain the relationship among word in ontology, and then make SQL to evaluate data accuracy, especially focused on data meaning.