What Skills Matter in Data Quality

Abstract

Research in data quality is a highly interdisciplinary field. Our community of data quality researchers and practitioners could benefit greatly from having a shared understanding of how diverse data quality research and skills complement one another, and to which area of data quality our attention should be directed. In this paper, we present a conceptual framework based on General Systems Theory, in order to facilitate collaborative discourse among data quality researchers and practitioners. In addition, we present an empirical measurement model based on General Systems Theory. We conducted an exploratory survey study at the 6 th International Conference on Information Quality held at MIT in November 2001 (ICIQ-2001). Based on these survey data, we demonstrate how diverse data quality skills could be classified and contrasted. Our preliminary findings suggest that Adaptive Capabilities---the ability of identifying user requirements and measuring the user satisfaction and data quality---is perceived as most important. On the other hand, we also found that, among academics, executives, and managers, Interpretive Capabilities---the ability of identifying and articulating organizational implication of data quality---is considered as most important. In this paper, we discuss implications of these findings.