Revolutionizing Data Strategy: The Impact of AI/ML-Driven Data Governance and Master Data Management in Health, Pharma, and Financial Industries

Authors

  • Naveen Sri Harsha Rellu, Vinodkumar Reddy Surasani, Raghuvaran Reddy Kalluri, Nagaraju Devarakonda

Keywords:

AI/ML, Data Governance, Master Data Management, Healthcare, Pharmaceuticals, Finance, Compliance, Data Accuracy, Digital Transformation.

Abstract

The rapid growth of data in healthcare, pharmaceutical, and financial industries has intensified the need for robust data governance and master data management (MDM) systems. This study explores how Artificial Intelligence (AI) and Machine Learning (ML) are transforming traditional data strategies by enhancing data quality, regulatory compliance, and operational efficiency. Employing a mixed-methods approach, the research combines a structured literature review, expert interviews, and a sector-specific survey across 150 professionals. Results reveal that the financial sector leads in AI/ML adoption and benefits, including a 52% improvement in data accuracy and reduced time to compliance. Healthcare and pharmaceutical sectors, while advancing, encounter integration and regulatory challenges. Statistical analysis using ANOVA confirms significant sectoral differences in return on investment (p < 0.001). Thematic insights further highlight key trends such as automated compliance, real-time data monitoring, and metadata discovery. The findings emphasize that AI/ML-driven governance is a strategic necessity, not just a technological advancement. This study contributes to the evolving discourse on intelligent data systems and provides practical insights for organizations aiming to build scalable, compliant, and efficient data infrastructures.

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Published

10.06.2024

How to Cite

Naveen Sri Harsha Rellu. (2024). Revolutionizing Data Strategy: The Impact of AI/ML-Driven Data Governance and Master Data Management in Health, Pharma, and Financial Industries. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 4935 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7416

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

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