Taxonomy of data quality problems in multidimensional Data Warehouse models

The impact of poor data quality on the process of decision making, organizational trust and customer satisfaction is well known. In today's organizations, Data Warehousing (DW) have offered support to business management and become part of natural and logical evolution for any decision support systems. In this scenario, DW solutions deployments have failed due to poor data quality implementations. Although some research has been devoted to investigating these problems, none has performed complete and thoroughly focused on examining quality issues manifests in its core component, i.e., the DW repository. This article presents a taxonomy for data quality issues, in the context of multidimensional data models, characteristic of DW. Finally, this work demonstrates that the proposed Taxonomy provides a more appropriate coverage to the problems of data quality and proposes its use in future works for quality measurements.