Visualization of Multi-Level Data Quality Dimensions with QuaIIe

Data quality assessment is a challenging but necessary task to ensure that business decisions that are derived from data can be trusted. A number of data quality metrics have been developed to measure dimensions like accuracy, completeness, and timeliness. The tool QuaIIe (developed in our previous research) facilitates the calculation of different data quality metrics on both, schema- and data-level, and for heterogeneous information systems. However, to gain meaningful results from the automatically calculated metrics, it is key that humans understand the results of these metrics. This understanding is specifically important when contextual information needs to be considered, which is not encoded in the data. In this paper, we present a visualization approach to enable human-centered data quality assessment across multiple dimensions and arbitrary complex data sources. The approach has been implemented as graphical user interface in QuaIIe.