Visualization properties for data quality visual assessment: An exploratory case study

Abstract

Data quality assessment outcomes are essential to ensure useful analytical processes results. Relevant computational approaches provide assessment support, especially to data defects that present more precise rules. However, data defects that are more dependent of data context knowledge challenge the data quality assessment since the process involves human supervision. Visualization systems belong to a class of supervised tools that can make visible data defect structures. Despite their considerable design knowledge encodings, there is little support design to visual quality assessment of data defects. Therefore, this work reports a case study that has explored which and how visualization properties facilitate visual detection of data defect. Its outcomes offer a first set of implications to design visualization system to permit data quality visual assessment.