International Journal of Scientific Research and Engineering Development

Enterprise dashboards increasingly depend on data aggregated from multiple heterogeneous sources such as ERP platforms, CRM systems, operational databases, IoT devices, and external APIs. While these dashboards support critical decision-making, they are often undermined by poor data quality arising from schema inconsistencies, missing values, duplicates, temporal misalignment, and conflicting metric definitions. These issues weaken analytical accuracy, reduce user trust, and limit the operational value of business intelligence systems. This paper presents a structured SQL-driven data quality optimization framework designed specifically for multi-source enterprise dashboard environments. The framework incorporates SQL-based validation rules, automated anomaly detection queries, and ETL-stage cleansing processes to systematically identify and correct data defects. Standardized SQL models are introduced to harmonize key business metrics across sources, reducing interpretational discrepancies and ensuring consistent analytical outputs. Experimental evaluation using both simulated and real enterprise datasets demonstrates notable improvements in overall data quality. Results show enhanced data completeness, strengthened referential integrity, significant reduction of duplicate records, and improved alignment across asynchronous sources. Moreover, the framework decreases dashboard refresh errors and improves the stability of KPI reporting. By leveraging SQL as a universal transformation and validation layer, the approach offers scalability, transparency, and compatibility with existing enterprise data architectures. This research highlights how SQL-driven optimization can substantially enhance the reliability and usability of enterprise dashboards, providing a practical and adaptable foundation for future data governance and analytics initiatives.