Unfolding Data Quality Dimensions in Practice: A Survey

TL;DR

This paper systematically maps functionalities of seven open-source data quality tools to high-level data quality dimensions, bridging the gap between theory and practice and aiding comprehensive quality assessment.

Contribution

It provides the first comprehensive mapping between data quality tool functionalities and quality dimensions, clarifying their many-to-many relationships.

Findings

Mapped functionalities to data quality dimensions across seven tools

Identified partial contributions of functionalities to multiple dimensions

Provided a unified view of the fragmented data quality landscape

Abstract

Data quality describes the degree to which data meet specific requirements and are fit for use by humans and/or downstream tasks (e.g., artificial intelligence). Data quality can be assessed across multiple high-level concepts called dimensions, such as accuracy, completeness, consistency, or timeliness. While extensive research and several attempts for standardization (e.g., ISO/IEC 25012) exist for data quality dimensions, their practical application often remains unclear. In parallel to research endeavors, a large number of tools have been developed that implement functionalities for the detection and mitigation of specific data quality issues, such as missing values or outliers. With this paper, we aim to bridge this gap between data quality theory and practice by systematically connecting low-level functionalities offered by data quality tools with high-level dimensions, revealing their many-to-many relationships. Through an examination of seven open-source data quality tools, we provide a comprehensive mapping between their functionalities and the data quality dimensions, demonstrating how individual functionalities and their variants partially contribute to the assessment of single dimensions. This systematic survey provides both practitioners and researchers with a unified view on the fragmented landscape of data quality checks, offering actionable insights for quality assessment across multiple dimensions.