Year: 2025
Type: article
Abstract: Artificial intelligence transforms traditional data management paradigms by implementing librarian-like intelligence for organizing and integrating cloud-based information systems. The exponential growth of digital data across enterprise environments creates unprecedented challenges for conventional storage and retrieval mechanisms. AI-driven data integration systems address these complexities through sophisticated Extract, Transform, Load processes that mirror systematic library cataloging methods. Machine learning clustering algorithms automatically categorize vast datasets into logical groupings, enabling intuitive navigation and discovery of related information. Modern streaming platforms demonstrate practical applications of these technologies, processing millions of user interactions to generate personalized content recommendations through intelligent pattern recognition. Natural language processing capabilities enable semantic understanding that goes beyond keyword matching, while distributed computing architectures provide the scalability necessary for enterprise-scale implementations. Edge computing integration reduces processing latency while maintaining centralized learning benefits. The evolution from passive data repositories to active intelligence systems represents a fundamental shift in organizational data strategy, transforming information assets from storage burdens into strategic competitive advantages.
Author: Manigandan Aravindhan
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4411255135