Data has become a core strategic asset in modern enterprises, driving decision-making, customer engagement, innovation, and operational efficiency. Traditional Enterprise Architecture (EA), originally designed for process standardization and IT governance, is now undergoing rapid transformation to support real-time analytics, distributed data ecosystems, cloud-native architectures, and AI-driven decision systems. This paper analyzes the evolution of enterprise architecture in data-driven organizations and proposes the Data-Driven Enterprise Architecture Evolution Framework (DDEAEF), consisting of four evolutionary phases: Technology-Centric EA, Process-Driven EA, Capability-Centric EA, and Data-Driven Intelligent EA. Conceptual figures, architectural layers, and maturity models are presented. The study highlights theoretical foundations based on socio-technical systems theory, organizational learning, and digital capability literature. The paper concludes with implications, limitations, and directions for future research.