AI-Driven Framework for Ensuring Data Integrity and Consistency Across Heterogeneous Multi-Source Systems

Abstract

In today's data-driven landscape, organizations rely on heterogeneous multi-source systems to gather and analyze information from various origins. This diversity often leads to challenges in maintaining data integrity and consistency. This paper proposes an AI-driven framework designed to address these challenges by leveraging advanced machine learning techniques to ensure data integrity and consistency across diverse systems. The framework's effectiveness is demonstrated through case studies and empirical analyses, highlighting its potential to enhance decision-making processes and operational efficiency.