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Towards a Semantic Data Quality Management - Using Ontologies to Assess Master Data Quality in Retailing

A high quality of master data is vital for process automation and IS support in enterprises. Retailers and manufacturers have started improving their master data quality on a syntactical level. Many researchers address problems of syntactical master data quality (e.g. missing values, typo errors), but only few approaches target semantic issues of master data quality management. Poor semantic data quality can lead to a misalignment between real-world phenomena and data stored in the databases. Furthermore, semantic data quality relies on the current use of data, but is not limited to the present use of the data and its impact on existing business processes. In fact it includes the likely future uses of the data as well. In this paper, we discuss the contribution of ontologies for identifying and assessing semantic master data quality problems. We develop a conceptual approach and procedure model for addressing the semantic master data quality problem. The method is applied within a scenario for automated coupon clearing in the retail industry.