Improving Data Consistency in Official Statistics: Application in the Central Bank of Portugal

Central banks play a crucial role in ensuring the reliability and accessibility of official statistics, which are fundamental for economic analysis, policymaking, and public transparency. However, as data volumes expand and statistical dissemination becomes increasingly digitalized, maintaining consistency across multiple sources remains a challenge. This study presents the development of an automated tool designed to validate the consistency of statistical data republished by Banco de Portugal (BdP). Leveraging APIs from primary sources, including the European Central Bank (ECB) and Eurostat, the tool systematically compares datasets to detect inconsistencies and streamline quality control processes. By automating statistical verification, the solution enhances efficiency, reduces reliance on manual checks, and strengthens data reliability. The study adopts a Design Science Research (DSR) methodology, integrating theoretical foundations with practical implementation. The developed tool successfully identified discrepancies in key statistical series, notably in effective exchange rate indices, where methodological revisions influenced data values. Despite challenges related to system integration and adaptability to structural changes in datasets, the tool demonstrated significant improvements in statistical consistency monitoring. Future research avenues include incorporating machine learning techniques for anomaly detection, broadening the tool’s applicability across various economic indicators, and aligning its framework with evolving international data standards, such as SDMX. This research underscores the transformative impact of automation in statistical quality assurance, reinforcing transparency, accuracy, and trust in official economic data.