Intelligent Metadata and Context-Aware MDM for Dynamic Decision-Making
DOI:
https://doi.org/10.32628/CSEIT25112460Keywords:
Artificial Intelligence, Master Data Management, Predictive Analytics, Data Governance, Digital TransformationAbstract
Master Data Management (MDM) is experiencing a transformative evolution through the integration of artificial intelligence and advanced analytics capabilities. This comprehensive article explores how AI-driven metadata management and context-aware systems are revolutionizing traditional MDM approaches, enabling organizations to make more informed and dynamic decisions. The article examines the implementation of intelligent MDM systems across various industries, highlighting improvements in data quality, operational efficiency, and strategic decision-making. The article demonstrates how predictive analytics and contextual intelligence enhance data governance, customer experience, and supply chain management. Furthermore, it analyzes the technical requirements and organizational readiness factors crucial for successful MDM implementation, providing insights into the future of enterprise data management practices.
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References
Giovanni Rossi, "A Comprehensive Study on the Role of AI and ML in Master Data Management for Healthcare," Innovative Management Journal, 2024. Available: https://meridianjournal.in/index.php/IMJ/article/view/32
Pedro Monteiro, et al., "Context-Aware System for Information Flow Management in Factories of the Future," Applied Sciences, 2024. Available: https://www.mdpi.com/2076-3417/14/9/3907
Michael Burke, "Step up to continuous data quality with modern master data management" Reltio Resource Center, 2022. [Online]. Available: https://www.reltio.com/resources/blog/step-up-to-continuous-data-quality-with-modern-master-data-management/
Wenli Yang, et al., "Impact and influence of modern AI in metadata management," arXiv preprint arXiv:2501.16605, 2025. Available: https://arxiv.org/abs/2501.16605
Dan Everett, "How AI Improves Master Data Management (MDM)," Informatica Blog, 2025. [Online]. Available: https://www.informatica.com/blogs/10-ways-ai-improves-master-data-management.html
Dr. Elena Rodriguez, "Predictive Analytics and Master Data Management in Healthcare: An AI-Driven Perspective," International Journal of Research and Creative Technology Development, 2024. Available: https://jrctd.in/index.php/IJRCTD/article/view/36?articlesBySameAuthorPage=2
Ramachandran Mahesh Iyer, "How master data is foundational to business transformation," TCS White Paper, 2024. Available: https://www.tcs.com/what-we-do/services/data-and-analytics/white-paper/master-data-management-bolsters-business-transformation
Robert Thomas, et al., "The Impact of Master Data Management on Business Intelligence and Analysis," International Conference on Data Management and Analytics, 2024. Available: https://easychair.org/publications/preprint/9Xh4/open
Siva Karthik Devineni, "Maximizing Business Efficiency: Strategic Insights into Master Data Management Implementation" ResearchGate Technical Report, 2021. Available: https://www.researchgate.net/publication/378490936_Maximizing_Business_Efficiency_Strategic_Insights_into_Master_Data_Management_Implementation
Gartner Research, "Implementing the Technical Architecture for Master Data Management," Gartner Technical Professional Advice, 2023. Available: https://www.gartner.com/en/documents/4391599
Daniela Invernizzi, et al., "MES Implementation: Critical Success Factors and Organizational Readiness Model," ResearchGate Publication, 2019. Available: https://www.researchgate.net/publication/335342713_MES_Implementation_Critical_Success_Factors_and_Organizational_Readiness_Model
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