Organizations identified the opportunities of big data analytics to support the business with problem-specific insights through the exploitation of generated data. Socio-technical solutions are developed in big data projects to reach competitive advantage. Although these projects are aligned to specific business needs, common architectural challenges are not addressed in a comprehensive manner. Enterprise architecture management is a holistic approach to tackle complex business and IT architectures. The transformation of an organization's EA is influenced by big data transformation processes and their data-driven approach on all layers. In this paper, we review big data literature to analyze which requirements for the EA management discipline are proposed. Based on a systematic literature identification, conceptual categories of requirements for EA management are elicited utilizing an inductive category formation. These conceptual categories of requirements constitute a category system that facilitates a new perspective on EA management and fosters the innovation-driven evolution of the EA management discipline.