Data Analytics and Big Data in Construction Project and Asset Management

Construction projects generate a wealth of information, but data becomes useful only when it can be organized, interpreted, and applied. This special issue of Built Environment Project and Asset Management brings together research on that challenge, examining how big data and analytics can support project decisions and the management of buildings and other assets.

Across its guest editorial and research papers, the issue connects building information modeling (BIM), data analysis, asset information, and decision-making. The result is a research-led snapshot of emerging digital approaches in the architecture, engineering, and construction sector—along with questions that still need investigation.

Where BIM meets project analytics

One study explores integrating project correspondence, daily progress reports, and inspection requests with a BIM model, then applying descriptive analytics to identify patterns in construction data. Its approach includes association analysis, clustering, and trend analysis, with findings presented within the BIM environment. The contribution makes the practical problem clear: project records can be more useful when they are structured and connected to the elements they describe.

Following information into asset management

Other papers consider how information held in BIM models might be transferred into asset-management platforms, and how growing volumes and varieties of asset data affect management practice. Together, these topics draw attention to the handover between project delivery and the operation and maintenance of assets.

Methods, decisions, and research gaps

The issue also broadens the discussion beyond BIM integration. Its articles examine data-driven analysis of factors associated with US housing prices, review the use of multi-criteria decision-making methods in site selection, and assess the state of big-data research in construction—including areas where further work is needed. Another paper develops a conceptual model of factors related to big-data adoption in the construction industry.

For readers working with the built environment

This collection will interest researchers and students in construction and project management, BIM, data analytics, and asset management, as well as practitioners following the digital transformation of the built environment. Rather than treating data volume as an end in itself, the issue keeps the focus on the harder question: how can information support better understanding and decisions?