Transforming Live IoT Data into Actionable Information for Asset Management via Digital Twins

The rapid expansion of the Internet of Things (IoT) has generated an over-whelming amount of dynamic data, challenging asset management in the built environment. The sheer volume and complexity of these raw data often prevent managers from extracting actionable insights necessary for sustainable practices. This paper aims to transform live IoT data into meaningful information to enhance sustainable asset management. Focusing on the segment ’data-to-information’ of the pyramid of data-information-knowledge-wisdom, it explores how integrating IoT data with digital twin technology can facilitate environmentally conscious decision making. Authors propose a cohesive framework that leverages data analytics and machine learning algorithms within a digital twin environment. The methodology involves deploying a data processing pipeline that aggregates IoT data streams, enhancing relevance for asset managers focused on sustainability objectives. Visualization tools present this information in an accessible format, aiding informed decision-making. Through two case studies, it is demonstrated how this approach leads to improved operational efficiency and strategic asset utilization, all contributing to minimized environmental impact. The findings highlight the critical role of effective data interpretation in driving decisions that align with organizational values and green initiatives. By transforming raw IoT data into actionable insights within a digital twin framework, organizations can make informed decisions to promote sustainable asset management. This approach optimizes resource utilization and supports environmental sustainability goals, contributing to a greener future. The paper includes the design and implementation of the data processing pipeline, integration strategies with digital twin technology, application of machine learning algorithms for data analysis, and development of visualization tools. It provides detailed insights from the studies, illustrating the practical benefits and sustainability outcomes of the proposed framework in asset management. The issues beyond the data-to-information conversion and technical specifics of IoT hardware deployment and cybersecurity aspects related to IoT data transmission are not addressed.