High-quality data is a foundational requirement for reliable analytics and effective organizational decision-making, yet many enterprises continue to struggle with persistent data quality challenges. This study examines the effectiveness of data quality improvement strategies implemented under enterprise data governance systems. A mixed-method research design was adopted, integrating survey-based assessments, system-level data quality metrics, and organizational governance evaluations across multiple data-intensive industries. Key governance variables, including governance maturity, policy standardization, data stewardship effectiveness, metadata management, leadership support, and organizational data culture, were analyzed in relation to core data quality dimensions such as accuracy, completeness, consistency, timeliness, validity, uniqueness, and integrity. Advanced statistical techniques, including factor analysis, regression modeling, and structural equation modeling, were used to evaluate the relationships between governance structures and data quality outcomes. The findings reveal significant improvements in data quality performance following the implementation of governance-driven strategies, with notable reductions in error rates, duplicate records, and processing time, alongside increased user trust in organizational data. The study concludes that robust enterprise data governance systems are critical enablers of sustainable data quality improvements and provide a strategic foundation for achieving reliable analytics, operational efficiency, and long-term competitive advantage.