Data Quality Assessment and Verification Methods

Abstract

With the rapid growth of data volume, data quality has become increasingly critical. This study, based on literature review and practical experience in Taiwan, identifies 16 dimensions for multifaceted data quality evaluation. These include qualitative indicators, such as Currency, Comparability, Comprehensiveness, Relevance, Informed Consent, Accessibility, Immediacy, Understandability/Interpretability, Correctness, Vocabulary and Dictionary, Standardization, Security, Concordance, and Interoperability, and quantitative indicators including Completeness, Plausibility, and Conformance.