They focus on information-rich information technology like those emerging. Cloud Computing, Artificial Intelligence (AI), and machine learning embed them in the existing enterprise data strategies to placate issues like data imperativeness due to faster executions. As a usage of Multi-Domain Master Data Management (MDM), it resonates. In several domains, such as customer, product, supplier, and employee data, MDM is required to organize and rule out crucial company data and provide a full and correct representation of the company if it has to operate with consistent data. The higher complex and wide volume of business data calls for the multi-domain approach that changes from the traditional single-domain MDM systems. In a multi-domain, MDM is used to integrate, provide real-time analytics, and make better decision-making via the resolving of problems like silos, system fragmentation, and regulatory compliance. These applications are pivotal to numerous industrial sectors, including healthcare, biotech, electric vehicles (EV), and renewable energy, because of data security, patient care, and supply chain management. In terms of healthcare and EV, the prospects of robust frameworks also play a key role in quality, security, and compliance, says the document. Specific challenges of multi-domain MDM are integration complexity, legacy, system compatibility, and compliance risk that the use of phased deployment and scalable architecture can smooth. Even in a highly competitive data-driven world, businesses need to find their way through in a very speedy manner without being stuck at a particular place, and that is where the future of MDM needs to adopt technologies such as the ones mentioned in this article like Anti AI or blockchain to solve the issues of data integrity, scalability and the limitations of the enforced restrictions arising from MDM compliance.

Multi-Domain MDM, Data Governance, Cloud Computing, Integration, Data Quality

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