Developing Intelligent Systems That Continuously Monitor and Validate Data Quality Across Large Distributed Systems

Ensuring high-quality data in large-scale distributed systems is essential for the reliability of real-time analytics, automated decision-making, and regulatory compliance in data-driven enterprises. Traditional data quality techniques, largely based on static rule-based approaches, are insufficient to address the scale, velocity, and complexity of modern distributed environments. This study presents the design and evaluation of an intelligent data quality monitoring system that integrates rule-based validation, machine learning models, metadata analysis, and adaptive feedback loops. The proposed architecture supports both real-time and batch processing, and was implemented using distributed computing frameworks such as Apache Kafka and Spark. Empirical evaluations conducted using synthetic IoT sensor data and real-world NYC taxi trip records demonstrated that the system outperformed traditional methods in terms of precision, recall, F1 score, and scalability. Furthermore, the system exhibited adaptive capabilities through feedback-driven learning and self-healing mechanisms, enabling it to respond effectively to evolving data patterns. These results confirm the system’s practicality and effectiveness in maintaining trustworthy data within high-volume, dynamic distributed environments. The study concludes with recommendations for future enhancements, including the integration of explainable AI and decentralized validation techniques.