  Data quality is vital for user experience in products reliant on data. As
solutions for data quality problems, researchers have developed various
taxonomies for different types of issues. However, although some of the
existing taxonomies are near-comprehensive, the over-complexity has limited
their actionability in data issue solution development. Hence, recent
researchers issued new sets of data issue categories that are more concise for
better usability. Although more concise, modern data issue labeling's
over-catering to the solution systems may sometimes cause the taxonomy to be
not mutually exclusive. Consequently, different categories sometimes overlap in
determining the issue types, or the same categories share different definitions
across research. This hinders solution development and confounds issue
detection. Therefore, based on observations from a literature review and
feedback from our industry partner, we propose a comprehensive taxonomy of data
quality issues from two distinct dimensions: the attribute dimension represents
the intrinsic characteristics and the outcome dimension that indicates the
manifestation of the issues. With the categories redefined, we labeled the
reported data issues in our industry partner's data warehouse. The labeled
issues provide us with a general idea of the distributions of each type of
problem and which types of issues require the most effort and care to deal
with. Our work aims to address a widely generalizable taxonomy rule in modern
data quality issue engineering and helps practitioners and researchers
understand their data issues and estimate the efforts required for issue
fixing.
