Systematic Mapping of Data Quality in the Public Sector: Dimensions, Metrics and Practices - Silva, Maria Inês Vale; Luna, Ramon Nicolas Gomes; Cavalcante, Amanda K. B.; Lelli, Valéria; Andrade, Rossana M. de Castro; Monteiro, José Maria; Santos, Ismayle de Sousa (Anais do Simpósio Brasileiro de Qualidade de Software (SBQS); 2025: Anais do XXIV Simpósio Brasileiro de Qualidade de SoftwareDO - 10.5753/sbqs.2025.15065, 2025)
Data quality plays a critical role in ensuring the reliability and effectiveness of information systems in public sector organizations. However, fragmented data ecosystems, legacy systems, and a lack of standardized assessment practices challenge the implementation of consistent data quality strategies. This study presents a comprehensive systematic mapping of data quality dimensions, metrics, practices, and challenges across public institutions. A total of 53 peer-reviewed studies were analyzed, revealing the most frequently used data quality dimensions and associated metrics. The research highlights a wide spectrum of techniques, ranging from traditional validation rules to emerging applications of machine learning for anomaly detection and data imputation. In addition, it identifies key barriers — including limited automation, lack of governance, and scarce resources — and proposes actionable recommendations for public sector entities. The findings serve as a conceptual and practical foundation for improving data quality management in complex government environments, with the ultimate goal of supporting the implementation of data quality assessment strategies in a state-level public finance institution in Brazil, the Ceará State Treasury Department (Sefaz-CE).