Mastering the Data Lifecycle for Governed AI-BI in the Cloud: From Ingestion to Auditable Deletion

• Published in: European Journal of Computer Science and Information Technology Vol. 13; no. 49; pp. 1 - 21
• Main Author: Ravva, Karthik
• Format: Journal Article
• Language: English
• Published: 26.06.2025
• ISSN: 2054-0957; 2054-0965
• DOI: 10.37745/ejcsit.2013/vol13n49121

The rapid evolution of AI-powered Business Intelligence (BI) solutions demands robust data governance frameworks that span the entire data lifecycle in cloud environments. Organizations face intensifying regulatory pressures, particularly from GDPR requirements concerning data erasure and storage limitations. The successful implementation of data governance requires integrated solutions addressing ownership, classification, ingestion, storage, and retention management. Through cloud-native tools and automated processes, enterprises can achieve both regulatory compliance and operational efficiency. The adoption of sophisticated data lifecycle management strategies, leveraging advanced capabilities from major cloud providers, enables organizations to maintain control over their data assets while supporting innovative AI-BI implementations. The integration of automated classification systems, intelligent storage management, and comprehensive audit mechanisms provides organizations with the necessary foundation to address evolving regulatory requirements while maximizing the value of their data assets. These frameworks enable seamless adaptation to changing compliance landscapes, ensuring sustainable growth and innovation in AI-powered business intelligence solutions.