This study examines data governance issues arising from the expanding use of data and artificial intelligence (AI) in the performing arts through a stage-based data pipeline framework. The pipeline consists of six stages: collection, cleaning and processing, storage and management, AI training, generation and utilization, and reuse and sharing. Using seven domestic and international cases, the study applies pattern-matching analysis to identify dominant governance factors at each stage, focusing on six dimensions: legitimacy, quality, control, transparency, accountability, and usability. The findings show that governance issues emerge structurally along the data pipeline rather than from specific technologies. Legitimacy was dominant in AI-based creative systems, quality in real-time performance environments, control in digital archives, and usability in platform-based sharing systems. The study highlights the need to establish stage-specific governance frameworks suited to AI-driven performing arts environments.