Using Well-Known Techniques to Visualize Characteristics of Data Quality

Previous work has identified more than 100 distinct characteristics of data quality, most of which are aspects of completeness, accuracy and consistency. Other work has developed new techniques for visualizing data quality, but there is a lack of research into how users visualize data quality issues with existing, well-known techniques. We investigated how 166 participants identified and illustrated data quality issues that occurred in a 54-file, longitudinal collection of open data. The issues that participants identified spanned 27 different characteristics, nine of which do not appear in existing data quality taxonomies. Participants adopted nine visualization and tabular methods to illustrate the issues, using the methods in five ways (quantify; alert; examples; serendipitous discovery; explain). The variety of serendipitous discoveries was noteworthy, as was how rarely participants used visualization to illustrate completeness and consistency, compared with accuracy. We conclude by presenting a 106-item data quality taxonomy that combines seven previous works with our findings.