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The rapid expansion of smart technologies in commercial real estate has led to a surge in building data, yet nearly 60%—so-called ‘grey data’—remains underutilized ( IBM: Smart building data utilization study (2023)). Although IoT devices and automated systems generate vast volumes of information, only 28% of facility managers implement effective analytics strategies (Deloitte: IoT and analytics in facilities management. Industry report. Deloitte Insights, London, UK (2024)), and this contributes to operational inefficiencies and up to 15% cost overruns (ADEME: Performance énergétique et coûts d’exploitation: Guide d’analyse (2023)). Increasing regulatory demands, such as the Tertiary Decree and BACS requirements, further underscore the need for structured data management across the building lifecycle. Implementing a Design Science Research (DSR) methodology, this study introduces a structured, replicable approach for managing smart building data through Building Information Modeling (BIM). The proposed framework comprises four stages: (i) data collection and model auditing; (ii) data modeling and mapping; (iii) BIM integration and quality control; and (iv) export to standardized, interoperable formats. The framework is supported by relevant BIM tools including Autodesk Revit 2022, Dynamo, DiRoots, and Dalux, and was refined through expert consultations. Applied to the Nanterre 3 smart building demonstrator at CESI Campus, the methodology enabled centralized data integration, real-time KPI visualization, and automated compliance reporting. Feedback from facility managers of the CESI N3 building highlights enhanced data accessibility and operational performance, while also supporting sustainability goals through enabling predictive analytics and proactive maintenance strategies. This research presents an original framework for BIM-based data management in smart buildings. It highlights pressing challenges such as data interoperability and the governance of complex data ecosystems, offering a practical foundation for future research and implementation in the built environment sector.

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Structuring and Integrating Building Facilities Management Data in Smart Built Environments: A Case Study from the CESI Nanterre Campus

  • 会议论文

  • 发表日期：

    2026年

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  • El Hadji Boubacar Seck,
  • Omar Doukari,
  • Serigne Mansour Diene,
  • David Greenwood

摘要

The rapid expansion of smart technologies in commercial real estate has led to a surge in building data, yet nearly 60%—so-called ‘grey data’—remains underutilized ( IBM: Smart building data utilization study (2023)). Although IoT devices and automated systems generate vast volumes of information, only 28% of facility managers implement effective analytics strategies (Deloitte: IoT and analytics in facilities management. Industry report. Deloitte Insights, London, UK (2024)), and this contributes to operational inefficiencies and up to 15% cost overruns (ADEME: Performance énergétique et coûts d’exploitation: Guide d’analyse (2023)). Increasing regulatory demands, such as the Tertiary Decree and BACS requirements, further underscore the need for structured data management across the building lifecycle. Implementing a Design Science Research (DSR) methodology, this study introduces a structured, replicable approach for managing smart building data through Building Information Modeling (BIM). The proposed framework comprises four stages: (i) data collection and model auditing; (ii) data modeling and mapping; (iii) BIM integration and quality control; and (iv) export to standardized, interoperable formats. The framework is supported by relevant BIM tools including Autodesk Revit 2022, Dynamo, DiRoots, and Dalux, and was refined through expert consultations. Applied to the Nanterre 3 smart building demonstrator at CESI Campus, the methodology enabled centralized data integration, real-time KPI visualization, and automated compliance reporting. Feedback from facility managers of the CESI N3 building highlights enhanced data accessibility and operational performance, while also supporting sustainability goals through enabling predictive analytics and proactive maintenance strategies. This research presents an original framework for BIM-based data management in smart buildings. It highlights pressing challenges such as data interoperability and the governance of complex data ecosystems, offering a practical foundation for future research and implementation in the built environment sector.