Industry 5.0 emphasizes resilience, sustainability, and human-centric approaches in industrial operations. However, a significant gap remains in integrating sustainability-related performance indicators into analytical systems. Many organizations lack essential data on aspects such as energy efficiency, material consumption, and operational costs, limiting their ability to evaluate and improve sustainable practices. This thesis aims to address this gap by proposing a methodological approach that incorporates Industry 5.0 performance indicators into analytical systems to support sustainable business practices. The approach includes the development of a meta-model that extends core analytical system concepts, integrating business, process, and sustainability indicators. Additionally, a taxonomy of relevant Industry 5.0 performance indicators will be created. To overcome data limitations, simulation-based scenarios aligned with sustainability goals will be designed and used to generate synthetic data. This research contributes to advancing data-driven sustainability by enabling better decision-making and aligning industrial analytics with the Sustainable Development Goals (SDGs) through integrated performance measurement.