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Information quality is critical to successful asset management decision-making. Substandard quality information will likely cause significant negative short- and long-term consequences. The ongoing digital transformation in the Architecture, Engineering, and Construction (AEC) industry has influenced ways to manage physical assets. Yet many asset owners lack a clear understanding of identifying indispensable quality dimensions that satisfy the business, system, and technical requirements. This paper aims to comprehensively analyse asset information quality management with a systematic literature review. The study reveals that the quality dimension of ‘accuracy’ alone cannot support various asset management functions. Additionally, quality deficiencies remain in Building Information Modelling (BIM)-based project delivery handover documents, establishing insufficient asset baselines for future planning. Moreover, the limited knowledge of the quality complications of information generated through technical solutions suggests additional work is required to gain insights into vital quality dimensions. The findings of this study underpin the basis for classifying quality dimensions to support essential asset management processes while pointing to future study areas.

Information Management is crucial in the Asset Management domain. Well-structured information management processes allow to access the needed data, in the right format, and enables the development of cross-domain Asset Management services. Several studies can be found in literature, on the capabilities and applications of digital tools in Architecture Constructions and Operations (AECO), demonstrating how digitisation has changed the traditional processes. Moreover, many data modelling approaches and standards can be used to support digital Asset Management applications. Due to the advancement of Digital Twin related research, the interest for the ontological and data modelling approaches have increased in recent years. This article aims at organising the body of knowledge on data modelling in AECO, through a critical review of existing approaches and standards. The literature is studied and the main trends are highlighted. The most relevant articles are selected and a contents analysis is carried out. The study shows that digital Asset Management applications require interdisciplinarity and data access across different domain; two main approaches for the development of intermediate data models can be identified in literature and a unique data model able to represent multiple scales and domains cannot be found. Thus, some future research works are proposed as a conclusion of the literature review.