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The rise of Digital Twin (DT) and Discrete Event Simulation (DES) technologies in manufacturing underscores the critical importance of accurate data. Without key data attributes, DT models become unreliable for system optimization and decision-making support. Through a case study, this research contributes to the knowledge domain by identifying essential data attributes for effective DES-based DT implementation and categorizing them according to their availability and relevance to various optimization objectives. To achieve this, a mixed method approach was employed, combining a literature review, semi-structured interviews, and consultations with industrial practitioners, including simulation specialists, manufacturing execution system experts, and shop floor managers. The study's findings reveal significant data gaps when using DES-based DT in the manufacturing sector and, through Quality Function Deployment (QFD) analysis, provide industry practitioners with actionable insights for prioritizing data collection efforts. Ultimately, this research facilitates data-driven decision-making in large-scale manufacturing environments by offering a structured framework for identifying key data attributes necessary to enhance DES-based DT.