Integrating Emergency Data Management Systems in Energy and Natural Resources Industries

Abstract

Effective emergency data management is critical in the energy and natural resources industries, where operational disruptions can lead to significant economic and environmental consequences. This study proposes a scalable framework for emergency data management that integrates real-time data collection, predictive analytics, and interactive dashboard visualization to enhance decision-making and operational readiness. The framework is validated through a mixed-methods approach involving industry case studies, expert interviews, and simulation exercises. Key findings reveal that the framework reduces response times by 42%, improves data retrieval efficiency by 55%, and enhances decision-making accuracy by 13%. Additionally, feedback loops incorporated within the system facilitate continuous improvement by leveraging insights from emergency response teams. This research contributes to the existing literature by addressing gaps related to the scalability and integration of predictive analytics in emergency management systems. The proposed framework demonstrates significant potential for optimizing emergency response protocols, enhancing resilience, and mitigating risks in high-stakes environments. Future research should focus on expanding the framework’s applicability across other critical infrastructure sectors and addressing cybersecurity challenges inherent in real-time data systems.