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Sustainable Data Quality Management: AI-Driven Approaches for Reducing Technical Debt in Data Governance
Abstract
In today's data-driven world, organizations face increasing challenges in managing data quality effectively. As data volumes grow, so does the complexity of maintaining high-quality data across diverse systems, particularly in environments with legacy infrastructure and technical debt. This paper explores AI-driven approaches for sustainable data quality management, focusing on how artificial intelligence (AI) can help organizations reduce technical debt in data governance practices. By leveraging AI techniques such as machine learning, natural language processing, and anomaly detection, organizations can automate data cleansing, enhance data validation processes, and improve data integration. These innovations not only improve data quality but also reduce the ongoing costs associated with maintaining outdated and inefficient data management systems. The paper also discusses the role of AI in identifying and addressing technical debt, offering strategies for organizations .
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