August 9, 2025

Data Quality Assessment and Verification Methods.

Key Points

MAIN FINDING: Sixteen dimensions for data quality evaluation were identified, focusing on both qualitative and quantitative indicators.
KEY EVIDENCE: Qualitative indicators include currency, comparability, and interpretability, while quantitative measures focus on completeness and plausibility.
APPROACH: The assessment is based on literature review and practical experience in Taiwan, outlining a multifaceted evaluation framework.
SIGNIFICANCE: Understanding these dimensions of data quality is essential for ensuring accurate and reliable data usage in various applications.

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.

Data Quality Assessment and Verification Methods.

Key Points

Abstract

Cite This Study

Also Consider

Also Consider