Why Data Management?
Digital twin technologies include not only a physical copy of a building but also a digital simulation of its operational processes. However, the true potential of this technology cannot be realized without proper data management. Research shows that 60% of the data collected in facilities remains unused because it is not properly analyzed (Kazeem et al., 2024). This increases operating costs and hinders sustainability goals.
The natural next step after BIM4FM is the management of data throughout its lifecycle and ensuring its sustainability in the digital twin environment.
1️⃣ The Relationship Between Data Management and Digital Twins
The fundamental requirement for the sustainability of digital twins is that the data is kept up-to-date, accessible, and compliant with standards:
Standards (COBie, IFC, ISO 19650): Ensures the consistency of data throughout its lifecycle. Cloud Integration: Enables real-time data sharing and multi-stakeholder collaboration. IoT and Sensors: Live data such as energy consumption, indoor air quality, and user mobility flows into the digital twin.
AI-Powered Analytics: Insights are extracted from large datasets, and maintenance and energy scenarios are optimized.
2️⃣ Contributions of Digital Twin Sustainability
Energy Efficiency: Up to 20% savings can be achieved with real-time energy monitoring.
Predictive Maintenance: Equipment failures are detected in advance thanks to sensor data, reducing costs by 15%.
Carbon Footprint Tracking: Material usage and operating processes are supported by carbon calculations.
Longer Lifecycle: Accurate data management increases the average lifespan of assets by 10–15%.
3️⃣ Real-Life Applications
Singapore Smart Nation Program: Traffic, energy, and water management were optimized using a city-wide digital twin approach. An 8% annual reduction in carbon emissions was achieved through data sharing.
Siemens Amberg Factory (Germany): The plant implemented digital twin-based data management for all machines on its production line. Result: losses due to production errors decreased by 40%.
Helsinki 3D+ (Finland): The entire city was modeled using digital twins to simulate energy performance, climate change scenarios, and infrastructure planning. This allowed the municipality to progress faster towards its 2030 sustainability goals.
4️⃣ Future Perspectives
Digital twins, thanks to data management, will address not only today’s problems but also future sustainability goals:
Autonomous Plants: AI-powered digital twins will automatically make energy and maintenance decisions.
Environmental Impact Assessment: The carbon footprint of all materials and processes will be measured in real time.
Circular Economy: Digital twins will guide the reuse of materials when buildings are dismantled. Information + Sustainability = The Standard of the Future
Data management is fundamental to the long-term sustainability of digital twins. Following the transformation of BIM4FM in facility management, a robust data strategy combined with digital twins presents a new paradigm in the triangle of energy efficiency, low cost, and high user satisfaction.
The question of the future is: Do you think the greatest contribution of digital twins to sustainability will be in energy management or in carbon footprint tracking?
