Big data in construction project management : prospects and challenges

Abstract

‘Big data’ has been rapidly sprawling in various realms such as biology, ecology, medicine, business, finance, and public governance but its euphoria surrounding construction project management (CPM) is yet to be seen. The CPM community around the world is still generally relying on ‘small data’ that is carefully curated and collected via traditional approaches such as sampling and ethnographic methods. Construction is a traditional and heterogeneous industry. Construction outputs are unique commodities that are not transferable but are, by and large, of fixed, large, heavy, and one-off nature. Construction works are often organized as different projects, which are temporary organisations that will be dissolved after completion. Different professionals, such as architects, engineers, surveyors and constructors, join in and undertake a parcel of the works, leaving construction to discontinuous working processes. There are fragmentation issues such as isolation of professionals, and lack of coordination among professionals. All these characteristics, the temporary nature in particular, have given rise to the question of whether ‘big data’ is an effective proposition in CPM.

The aim of this doctoral research is to examine the concept of big data and its prospects and challenges in the context of CPM. It did so by firstly qualitatively identifying the opportunities of big data in the form of a conceptual framework, underlying which is the theory stance to deem CPM as making an array of decisions throughout its lifecycle. Challenges of its new application in CPM were also identified through qualitative approaches. Lying at the core of the research methodology is a mixed method approach to identify the conceptual framework, prospects and challenges of big data in CPM, which entails literature review, interviews, and a case study.

In contrast to the stereotype that CPM is based on ‘small’ data, the study discovered that construction generates rich, ‘big data’, which can be subsequently harnessed to facilitate achieving better CPM goals such as time, cost, quality, safety, and environment. The advantages of ‘big data’ over regular ‘small’ data in CPM are identified as the increased traceability and visibility, holistic visualization, reduced randomness, increase timeliness, and improved decision efficiency. It is expected that the global CPM community will take more proactive strategies in developing and harnessing big data. The ongoing promotion of BIM and integrated procurement models provide an opportunity, but challenges such as costs vs. benefits, managerial and ethical issues are remaining. The research provided one of the first attempts to demystify big data in CPM; with this research, further efforts to develop the domain will proceed with a more solid footing. It can also contribute to big data in general project management settings.