Background: The surge of emerging infectious disease outbreaks underscores the need for robust Early Warning Systems (EWS), and the widespread use of electronic health records has increased the demand for automated disease surveillance processes. Using administrative data for surveillance reduces the burden on teams while ensuring timely outbreak detection. This cost-efficient approach enhances the ability to detect outbreaks, particularly in low-resource settings. However, effective EWS based on administrative datasets requires real-time data quality assessment. Although metrics for evaluating surveillance systems exist, they have not been applied to administrative data used for epidemiological surveillance purposes. This paper presents the development and implementation of a Data Quality Index (DQI) to assess administrative data quality in epidemiological surveillance systems. Objective: The objective of this study is to develop and implement a Data Quality Index (DQI) for assessing the quality of administrative data used in epidemiological surveillance systems. Methods: We established a composite indicator measuring completeness and timeliness of Brazilian Primary Health Care (PHC) data from the National Information System on Primary Health Care. Completeness was defined as the proportion of weeks within an 8-week rolling window with any register of encounters, and timeliness refers to the time interval between the date of encounter and its corresponding entry into the system. Using the backfilled PHC dataset as a gold standard, we evaluated the impact of data quality in the EWS for influenza-like illness outbreaks across all 5,570 Brazilian municipalities from October 10, 2023, to March 10, 2024. Results: During the study, the backfilled PHC dataset registered 198,335,762 encounters due to influenza-like illness, averaging 8,623,294 encounters per week. Analysis of concordant warnings between the backfilled and the real-time dataset showed that 100% completeness and at least 80% timeliness were optimal for the highest concordance. Municipalities with at least 60% of weeks featuring a suitable Data Quality Index showed the highest concordance of warnings between datasets. Conclusions: Our study highlights the importance of data quality in enhancing the EWS performance using PHC data. In addition, we provide a practical approach for monitoring data quality in real time. Our findings demonstrate that optimal completeness and timeliness of data significantly impact the EWS's ability to detect outbreaks. Continuous monitoring and improvement of data quality should be prioritized to ensure the reliability and effectiveness of surveillance systems.