Journal of Engineering Research and Reports

Continuous Data Quality Improvement (CDQI) is essential for maintaining the integrity, accuracy, and reliability of enterprise data. In today's data-driven organizations, ensuring high-quality data across various systems and departments is critical for decision-making, operational efficiency, and regulatory compliance. This review presents a model for CDQI within the framework of enterprise data governance, outlining best practices and implementation strategies for sustained improvements in data quality. The proposed model integrates key components such as data quality assessment, improvement strategies, automation tools, and the alignment of governance policies with data quality objectives. It emphasizes the importance of establishing clear data standards, roles, and responsibilities, including the role of data stewards in maintaining quality over time. By leveraging technologies such as AI and real-time monitoring tools, organizations can automate data cleansing, detect anomalies, and provide actionable insights through continuous feedback loops. Best practices for CDQI include fostering a data-driven culture, conducting regular audits, enabling cross-functional collaboration, and integrating data quality metrics into governance policies. The implementation strategy is designed to be phased, starting with pilot programs and scalable to larger enterprise systems. Additionally, the model addresses challenges such as organizational resistance, balancing privacy concerns, and managing complex data environments. By adopting this model, organizations can ensure ongoing data quality improvements, leading to more accurate insights, better compliance with regulations, and enhanced business outcomes. This abstract provides a foundation for organizations aiming to enhance their data governance frameworks through continuous improvement.