Data management challenges in international projects applications of AI and machine learning for enhanced accuracy

Abstract

This paper investigates how Artificial Intelligence (AI) and Machine Learning (ML) can improve data accuracy, integration, and decision-making processes in response to the unique data management challenges posed by the growing complexity of international projects, with a focus on the volume, variety, and veracity of data. We examine the primary obstacles in managing cross-border data, including compliance with diverse regulatory frameworks, multilingual datasets, and varying data quality standards. Furthermore, we analyze real-world applications where AI and ML techniques such as natural language processing, predictive analytics, and anomaly detection are deployed to streamline data workflows. The study highlights the potential of these technologies to reduce errors, improve predictive capabilities, and facilitate collaboration across geographically dispersed teams. Our findings emphasize the importance of adopting advanced data management strategies to leverage the full potential of AI and ML, ensuring the success of international projects in an increasingly data-driven global landscape.