M.S. Bargh, R. Choenni, | Part of a book | Publication date: 21 July 2015
Maintaining the quality of data in information systems is an important issue that all organizations face. Existing data quality management solutions are based on offline methods carried out in infrequent intervals (like surveys). These methods suffer inherently from high costs, high delays, and thus low fidelity. In this contribution we propose an innovative data quality management framework that dynamically and indirectly monitors and improves data quality issues. The proposed framework, in turn, is based on a problem-resolving system, where users of databases (i.e., data analysts) report on data quality related problems as they arise in post implementation phase of such information systems. Almost all reported problems are implicitly related to data quality issues. Thus, our proposed framework offers a mechanism to link these problems to data quality issues dynamically. Through this 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 the encountered problems. The paper reports on a proof of concept tool and its evaluation.