QuaIIe: A Data Quality Assessment Tool for Integrated Information Systems

摘要

Data is central to decision-making in enterprises and organizations (e.g., smart factories and predictive maintenance), as well as in private life (e.g., booking platforms). Especially in artificial intelligence applications, like self-driving cars, trust in data-driven decisions depends directly on the quality of the underlying data. Therefore, it is essential to know the quality of the data in order to assess the trustworthiness and to reduce the uncertainty of the derived decisions. In this paper, we present Qualle (Quality Assessment for Integrated Information Environments, pronounced ['kvale]), a Java-based tool for the domain-independent ad-hoc measurement of an information system's quality. Qualle is based on a holistic approach to measure both schema and data quality and covers the dimensions accuracy, correctness, completeness, pertinence, minimality, and normalization. The quality measurements are presented as machine- and human-readable reports, which can be generated periodically in order to observe how data quality evolves. In contrast to most existing data quality tools, QuaIIe does not necessarily require domain knowledge and thus offers an initial ad-hoc estimation of an information system's quality.