Samenvatting

Organizations face many challenges in maintaining the quality of data in their information systems. Often offline methods like surveys are used in existing data quality management solutions. These methods, which are usually used in infrequent time intervals, suffer from high costs, high delays, and low fidelity inherently. In this contribution, we propose an innovative data quality management framework to dynamically monitor and improve the quality of data within an organization. To this end, the proposed framework relies on a problem resolving process, where users of information systems, e.g., data analysts, use issue tracking systems to report on data quality related problems, as these problems arise in post implementation phase of such information systems. Generally these reported problems are implicitly related to data quality issues. Thus, our proposed framework offers an automatic mechanism to semantically link these problems to data quality issues. Through this semantic linking, the framework offers added values for both data quality management community – who has traditionally relied on classic inquiries of human experts to detect data quality issues – and for data analyst community – who has traditionally relied on own expertise, thus rarely on state of the art data quality solutions, to resolve encountered problems. The paper discusses the set of functions included in the proposed data quality management framework and presents a proof of concept realization of the proposed framework.