Data Quality Assessments: A Theoretically Structured Overview of Approaches and Methods

Abstract

The quality of data is crucial for both practice and academia, and this holds for descriptive statistics, AI and advanced analytics alike. This study addresses an apparent gap in the literature and as such presents a full and theoretically grounded overview of data quality assessment approaches and methods, as well as their defining characteristics and inter-relationships. For this purpose a broad typology is introduced that employs two theoretical dimensions, namely the evaluation logic (formal versus informal) and the assessment driver (norms versus data). This yields four high-level approaches and 15 methods for assessing data quality. The research results are relevant for academia, as they provide a theory-based overview and definition of the ways that data quality can be evaluated. The study is also relevant for practice because it allows professionals to make informed decisions on using these methods, e.g. as part of an audit or broad data quality assessment strategy.

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BibTeX RIS

BibTeX

@misc{foorthuis2026data,
  title = {{Data Quality Assessments: A Theoretically Structured Overview of Approaches and Methods}},
  author = {Ralph Foorthuis},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2608.22474},
  eprint = {2608.22474},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.22474},
}

RIS

TY  - UNPB
TI  - Data Quality Assessments: A Theoretically Structured Overview of Approaches and Methods
AU  - Ralph Foorthuis
PY  - 2026
DA  - 2026/10/05
PB  - arXiv
DO  - 10.48550/arxiv.2608.22474
UR  - https://arxiv.org/abs/2608.22474
AB  - The quality of data is crucial for both practice and academia, and this holds for descriptive statistics, AI and advanced analytics alike. This study addresses an apparent gap in the literature and as such presents a full and theoretically grounded overview of data quality assessment approaches and methods, as well as their defining characteristics and inter-relationships. For this purpose a broad typology is introduced that employs two theoretical dimensions, namely the evaluation logic (formal versus informal) and the assessment driver (norms versus data). This yields four high-level approaches and 15 methods for assessing data quality. The research results are relevant for academia, as they provide a theory-based overview and definition of the ways that data quality can be evaluated. The study is also relevant for practice because it allows professionals to make informed decisions on using these methods, e.g. as part of an audit or broad data quality assessment strategy.
ER  -

Abstract

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