Intelligent Data Governance Frameworks: A Technical Overview

Babita Kumari

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 6, 141-154

Abstract: This comprehensive article explores the transformative potential of AI-driven data governance frameworks in addressing modern data management challenges. The article examines the limitations of traditional governance methods in the face of exponential data growth, regulatory complexities, and diverse data sources. It delves into the architecture, core components, and benefits of intelligent governance systems that leverage advanced AI technologies such as machine learning, natural language processing, and reinforcement learning. The article highlights significant improvements in operational efficiency, compliance accuracy, and data quality achieved through AI-driven solutions. Furthermore, it investigates future directions, including the integration of explainable AI, federated learning, and blockchain technology, while acknowledging the challenges that need to be addressed for widespread adoption.

Keywords: AI-driven Data Governance; Regulatory Compliance; Machine Learning; Cloud Infrastructure; Federated Learning (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT24106161
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT24106161 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT24106161/CSEIT24106161 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i6:id:402

DOI: 10.32628/CSEIT24106161

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma ().