Enhancing Data Integration Using AI and ML Techniques for Real-Time Analytics

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques has revolutionized data processing and analytics, particularly in the realm of real-time data integration. As businesses face an increasing volume and complexity of data, traditional data integration approaches often fail to meet the demands of real-time decision-making. This paper explores the role of AI and ML in enhancing data integration processes to enable efficient real-time analytics. By leveraging advanced algorithms for data cleansing, transformation, and enrichment, AI and ML improve the accuracy and speed of data integration pipelines. We examine how AI-driven automation and ML-based predictive models enable seamless integration of heterogeneous data sources, reducing latency and increasing the scalability of analytics systems. Additionally, the paper discusses the practical applications of these technologies in industries such as finance, healthcare, and retail, where real-time insights are critical for business success. The findings highlight the transformative potential of AI and ML in data integration, paving the way for smarter, more agile decision-making frameworks.