Wrangling Data Issues to be Wrangled: Literature Review, Taxonomy, and Industry Case Study

TL;DR

This paper reviews existing data quality taxonomies, identifies their limitations, and proposes a new, comprehensive two-dimensional taxonomy to improve issue detection and resolution in data management.

Contribution

It introduces a novel two-dimensional taxonomy of data quality issues based on attribute and outcome dimensions, addressing overlaps and ambiguities in previous taxonomies.

Findings

Redefined categories improve clarity and mutual exclusivity.

Labeled issues reveal distribution patterns and effort estimates.

The taxonomy enhances understanding and handling of data quality problems.

Abstract

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.