License Usage Monitoring and Predictive Analytics for Sustainable Digital Resource Management

This paper introduces a telemetry-based approach that leverages license usage monitoring and predictive analytics to enhance operational efficiency and digital sustainability in the energy sector. As engineering and geoscience applications become central to digital transformation, many organizations still lack visibility into software utilization, leading to underused assets and unnecessary energy consumption.

The proposed framework collects detailed telemetry from license managers and cloud platforms, standardizes the data, and aggregates it into a structured dataset for analysis. By identifying utilization patterns, idle capacity, and recurring demand intervals, organizations can anticipate software needs, optimize license allocation, and minimize redundant procurement.

Industry case studies illustrate the method’s impact: one organization improved license utilization efficiency by 22% and reduced procurement waste by 10%, while another used predictive trend analysis to anticipate cloud-based software demand, improving cost predictability and access.

Ultimately, license usage analytics enable proactive, data-driven management of digital resources. This systematic monitoring transforms license governance from a reactive process into an evidence-based discipline that supports both operational performance and environmental sustainability through reduced computational waste and optimized energy use.