# Data Quality Assessment: Challenges and Opportunities

arXiv: https://arxiv.org/abs/2403.00526 · HTML: https://mlsanity.com/paper/2403.00526/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2024.

## Built on by (most cited first, 2 of 2)

- [Assessing socio-economic climate impacts from text data](https://mlsanity.com/paper/2605.20793/) (2026)
- [Data Quality Awareness: A Journey from Traditional Data Management to Data Science Systems](https://mlsanity.com/paper/2411.03007/) (2024)

## Abstract (arXiv)

Data-oriented applications, their users, and even the law require data of high quality. Research has divided the rather vague notion of data quality into various dimensions, such as accuracy, consistency, and reputation. To achieve the goal of high data quality, many tools and techniques exist to clean and otherwise improve data. Yet, systematic research on actually assessing data quality in its dimensions is largely absent, and with it, the ability to gauge the success of any data cleaning effort. We propose five facets as ingredients to assess data quality: data, source, system, task, and human. Tapping each facet for data quality assessment poses its own challenges. We show how overcoming these challenges helps data quality assessment for those data quality dimensions mentioned in Europe's AI Act. Our work concludes with a proposal for a comprehensive data quality assessment framework.