Impact of AI and Machine Learning on Master Data Management

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) affect the creation of new Master Data Management (MDM) systems, which have become so critical in MDM, an organization integrating critical data about customers, products, and employees into a single trusted repository raises the ability to make better decisions, comply with legal requirements, and be more operationally efficient like all levels of IT, healthcare, and supply chain management. The paper examines how AI and ML define MDM by automating data categorization, improving data quality, and making predictions for better decision-making. This enables such technologies to be used in data governance and integration processes to scale up and enhance MDM systems' accuracy and responsiveness in a timely and accurate mode. A healthcare example would be when an AI-powered system improves the accuracy with which patients are cared for through medical records. In such cases, they look at the ML model in SCM and define models that predict demand or optimize inventory management. The study discusses some challenges, including data privacy concerns, integration with legacy systems, and risks of bias in AI models. The conclusion illustrates the prospects of AI and ML in MDM. It seeks to enhance automation and efficiency and help decision-making based on a more informed approach to MDM in all industries. These technologies will evolve to redefined ways of managing the data, allowing organizations to use the data correctly in the fast-paced, data-driven world.