Exploring enhanced data quality assessment: a framework for asset data management of the built environment

The built environment is undergoing a significant shift toward digital practices, driven by the global momentum of digital transformation. Consequently, asset managers in the built environment are now responsible for handling significantly larger volumes of data to support the operational phase of asset management. This increasing pressure has made it imperative for asset managers to not only manage the influx of data but also ensure its inherent quality. Although industry guidance on data management and quality exists, it primarily emphasises the importance of assessing data requirements and ensuring that datasets are fit for purpose. However, there remains a lack of detailed, practical guidance on how to implement these principles effectively. This research addresses critical gaps in the domain of asset management within the built environment, focusing specifically on the unique characteristics of this discipline. It emphasises the significance of physical, real-world assets and their associated data, introducing solutions for evaluating their importance in alignment with strategic organisational functions. This evaluation is established as a prerequisite for conducting effective data quality assessments. The study explores industry perceptions and attitudes toward existing data quality while drawing conclusions from an in-depth investigation of data quality dimension concepts. The research process is deeply embedded within the domain, incorporating continuous feedback and expert validation from industry practitioners leading to the development of theoretical framework and proof-of-value application. This research addresses a critical gap in the existing literature and investigations related to asset management within the built environment and inherent poor data quality. It introduces practical methods that asset managers can apply to support data enhancement initiatives. By providing essential domain context, the study also explores the implications of emerging technologies and concepts such as artificial intelligence.

Name dc:type.qualificationname

PhD

Level dc:type.qualificationlevel

PhD thesis

Grantor dc:publisher.institution

Middlesex University

Year dc:date.issued

2025

Author dc:creator

• Fitz, D.V.

Identifier

oai:repository.mdx.ac.uk:27z10y

OAI identifier oai:identifier

oai:repository.mdx.ac.uk:27z10y