Enhancing Asset Management Through Integrated Facilities Data, Digital Asset Management, and Metadata Strategies

This research explores the potential of Digital Asset Management (DAM) to enhance asset management practices through the utilization of facility data. The study specifically investigates the integration of metadata sourced from 2,340 facility-related issues reported at a public school in Saudi Arabia. Data spanning a two-year period was extracted from the school's system and subjected to quantitative data analysis to assess the frequency and significance of reported facility issues. This analysis, conducted on data extracted from the school's system in .xls format, aimed to identify patterns and trends in problem types, severity, and reported frequency, offering insights into prevalent challenges encountered in asset management and informing the research objectives. Furthermore, a thorough review of existing literature on digital asset management and metadata integration in Asset Information Modeling (AIM) was conducted. This review involved creating a mind map to organize and visualize key concepts, themes, and relationships identified in the literature. Insights derived from the mind map and broader literature review served as the groundwork for comprehending current practices, challenges, and potential solutions in DAM and AIM. The study aims to identify and evaluate key challenges in asset management based on the analysed facility data, devise a strategic framework for integrating metadata into AIM analysis, with a focus on effective metadata management practices, and design an advanced Digital Asset Management system to integrate facility data and enhance decision-making in asset and facility management. Notably, the research proposes a strategic framework for metadata integration in AIM analysis, highlighting effective metadata management practices such as defining metadata requirements, establishing standards, and optimizing capture, storage, and governance practices. The framework also advocates for leveraging metadata for continuous analysis and improvement. Anticipated outcomes encompass improved decision-making in asset and facility management, leading to heightened operational efficiency and effectiveness. This research seeks to contribute to advancements of asset management and optimized metadata usage, ultimately setting a new standard for excellence through the better use of facilities data.