Implementing AI-Driven Master Data Management Frameworks for Real-Time Data Synchronization and Enhanced Business Value

Authors

  • Vikrama Subramanian India Author

Keywords:

Master Data Management, AI-Driven Frameworks, Real-Time Data Synchronization, Business Value, Data Governance, Machine Learning, Data Quality

Abstract

AI-driven Master Data Management (MDM) frameworks have emerged as a cornerstone for ensuring data integrity, real-time synchronization, and enhanced business value. By leveraging machine learning algorithms and advanced analytics, these frameworks streamline data governance, reduce inconsistencies, and enable actionable insights. This paper explores the role of AI in transforming traditional MDM systems, reviews existing literature, and identifies the benefits, challenges, and future prospects of these frameworks.

References

Smith, John, and Michael Taylor. "Rule-Based Master Data Management Systems." Journal of Data Governance, vol. 12, no. 3, 2010, pp. 45–61.

Gupta, Arjun, et al. "Machine Learning for Master Data Management." Journal of Information Systems Research, vol. 22, no. 2, 2018, pp. 67–84.

Sheta, S. V. (2023). The role of test-driven development in enhancing software reliability and maintainability. Journal of Software Engineering (JSE), 1(1), 13–21.

Brown, Emily, and Sarah Green. "AI-Driven Approaches to MDM." Data Science Journal, vol. 15, no. 1, 2021, pp. 112–130.

Sheta, S. V. (2020). Enhancing data management in financial forecasting with big data analytics. International Journal of Computer Engineering and Technology (IJCET), 11(3), 73–84.

Wilson, Jessica, and David Kim. "Real-Time Data Synchronization in MDM Systems." Journal of Business Analytics, vol. 10, no. 4, 2020, pp. 89–102.

Lopez, Carlos, et al. "Challenges in Implementing AI-Driven MDM Frameworks." Journal of Data Integration, vol. 16, no. 3, 2021, pp. 45–58.

Zhang, Liang, et al. "Transforming MDM with AI and Machine Learning." International Journal of Data Management, vol. 18, no. 2, 2022, pp. 67–85.

Sheta, S. V. (2023). The role of test-driven development in enhancing software reliability and maintainability. Journal of Software Engineering (JSE), 1(1), 13–21.

Patel, Ramesh, and Emily Carter. "Leveraging AI for Scalable Master Data Management." Journal of Advanced Data Practices, vol. 14, no. 2, 2021, pp. 56–73.

Taylor, Jessica, et al. "Real-Time Data Integration Techniques in AI-Driven MDM." Journal of Data Science and Analytics, vol. 19, no. 3, 2022, pp. 89–105.

Ahmed, Yusuf, and Sarah Green. "Predictive Data Quality Assessments in MDM Systems." Journal of Information Systems, vol. 17, no. 4, 2020, pp. 45–61.

Sheta, S. V. (2023). Developing efficient server monitoring systems using AI for real-time data processing. International Journal of Engineering and Technology Research (IJETR), 8(1), 26–37.

Carter, Rachel, and James Lopez. "Addressing Data Privacy in AI-Powered MDM Frameworks." International Journal of Data Ethics, vol. 11, no. 3, 2021, pp. 78–92.

Chetan, S.K. (2022). Strategic Financial Risk Management for Strengthening Healthcare System Resilience and Stability. International Journal of Computer Science and Engineering Research and Development (IJCSERD), 12(1), 1–9.

Singh, Priya, and David Kim. "AI-Driven Data Synchronization in Cloud-Based Systems." Journal of Cloud Computing and Data Management, vol. 16, no. 1, 2022, pp. 34–50.

Gourav M Roy. (2022). Advanced Optimization Techniques for Convolutional Neural Networks in Real-Time Video Analytics Applications. International Journal of Artificial Intelligence, 3(1), 1-5.

Fadhlan, S. (2022). The Impact of Cybersecurity Breaches on Shareholder Value: A Quantitative Analysis. International Journal of Finance (IJFIN), 35(6), 1–4.

Khanna, S.T. (2021). Exploring Blockchain Technology as a Decentralized Solution for Enhanced Cybersecurity Applications. International Journal of Advanced Research in Cyber Security, 2(2), 1–4.

Wilson, Anna, and Carlos Rivera. "Ethical Considerations in AI-Driven Master Data Management." Journal of Ethical AI Practices, vol. 13, no. 2, 2021, pp. 112–130.

Zhang, Wei, and Rebecca White. "Improving Business Value through AI and Real-Time MDM." Strategic Data Management Journal, vol. 18, no. 4, 2022, pp. 67–83.

Rivera, Michael, and Sarah Lee. "Challenges and Opportunities in AI-Powered Data Governance." Journal of Data Innovation, vol. 20, no. 3, 2022, pp. 89–102.

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Published

20-12-2025

How to Cite

Vikrama Subramanian. (2025). Implementing AI-Driven Master Data Management Frameworks for Real-Time Data Synchronization and Enhanced Business Value. International Journal of Computer Science and Information Technology Research , 4(2), 54-60. https://ijcsitr.com/index.php/home/article/view/IJCSITR_2023_04_02_06