Integrating Distributed Data Resources: Artificial Intelligence Approaches for Cloud-Based Interoperability

Abstract

The digital transformation has generated unprecedented quantities of distributed data resources across organizational ecosystems. Yet, these valuable assets frequently exist in isolation, creating significant barriers to comprehensive intelligence gathering and decision-making processes. This article introduces a conceptual framework that views disconnected data repositories as isolated islands and positions artificial intelligence technologies as bridge-building mechanisms for achieving cloud-based interoperability. By examining the technical and organizational factors contributing to data fragmentation, this article identifies the substantial operational inefficiencies and strategic disadvantages stemming from information isolation. The conceptual foundation extends through practical application methodologies, including API integration, microservice architectures, and machine learning algorithms that facilitate intelligent data connections. Drawing parallels between data harmonization and culinary practices, the article illustrates how diverse information sources can be effectively combined to create cohesive, valuable insights under appropriate human guidance. Additionally, the semantic layer concept receives detailed attention as a universal translator mechanism enabling communication between disparate enterprise systems. The transformative potential of AI-driven integration culminates in organizational considerations, success determinants, and ethical dimensions essential to implementing effective cross-functional data sharing initiatives within cloud environments.