Enhancing data quality through comprehensive governance: Methodologies, tools, and continuous improvement techniques

Abstract

In the era of data-driven decision-making, ensuring data quality is paramount for organizations seeking to leverage their data assets effectively. This paper explores comprehensive strategies for enhancing data quality through robust governance, methodologies, tools, and continuous improvement techniques. It highlights the critical dimensions of data quality, including accuracy, completeness, consistency, timeliness, validity, and uniqueness. It discusses various assessment techniques, such as data profiling, auditing, and quality metrics. The paper also examines the role of data cleansing, enrichment, integration, and interoperability in maintaining high data quality. Additionally, it provides an overview of leading data quality management tools, their evaluation criteria, and best practices for implementation. Finally, it underscores the importance of continuous monitoring, feedback loops, root cause analysis, and fostering an organization's data quality culture. By adopting these strategies, organizations can ensure the reliability and integrity of their data, leading to improved business outcomes. Keywords: Data Quality, Data Governance, Data Profiling, Data Cleansing, Continuous Improvement.