A Unified Hierarchical Data Quality Taxonomy for Interdisciplinary Data Collaboration

Miller et al. (2024) point out the lack of standardization and proliferation of data quality terminology across domains, hampering collaboration and interoperability. This research would systematically aggregate, structure, and extend data quality concepts from diverse standards (ISO 25012, ALCOA+, DAMA, sector-specific guidelines) into a hierarchical, machine-readable taxonomy—linked to practical metrics, assessment tools, and domain-specific examples. The taxonomy would be instantiated as a knowledge graph to support automated mapping, translation, and recommendation services in multi-domain data projects (e.g., integrating medical, environmental, and social data for public health). This would fill a critical gap by providing a shared “language” and toolset, empowering cross-sector data initiatives and fostering innovation at the intersections of disciplines.

References:

1. A Framework for Current and New Data Quality Dimensions: An Overview. Russell Miller, Harvey Whelan, Michael Chrubasik, David Whittaker, Paul Duncan, J. Gregório (2024). International Conference on Data Technologies and Applications.