Data Quality Assessment: Challenges and Opportunities
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 cl...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2403.00526 |