Ahmed, V; Aziz, Z; Tezel, A and Riaz, Z (2018) Challenges and drivers for data mining in the AEC sector. Engineering, Construction and Architectural Management, 25(11), pp. 1436-1453. ISSN 0969-9988
Abstract
Purpose: The purpose of this paper is to explore the current challenges and drivers for data mining in the AEC sector. Design/methodology/approach: Following a comprehensive literature review, the data mining concept was investigated through a workshop with industry experts and academics. Findings: The results showed that the key drivers for using data mining within the AEC sector is associated with the sustainability, process improvement, market intelligence, cost certainty and cost reduction, performance certainty and decision support systems agendas in the sector. As for the processes with the greatest potential for data mining application, design, construction, procurement, forensic analysis, sustainability and energy consumption and reuse of digital components were perceived as the main process areas. While the key challenges were perceived as being, data issues due to the fragmented nature of the construction process, the need for a cultural change, IT systems used in silos, skills requirements and having clearly defined business goals. Originality/value: With the increasing abundance of data, business intelligence and analytics and its related concepts, data mining and Big Data have captured the attention of practitioners and academics for the last 20 years. On the other hand, and despite the growing amount of data in its business context, the AEC sector still lags behind in utilising those concepts in its end products and daily operations with limited research conducted to explore those issues at the sector level. This paper investigates the main opportunities and barriers for data mining in the AEC sector with a practical focus.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | decision support systems; information and communication technology applications; technology |
| Index terms: | energy consumption, data mining, workshop, cultural change, cost reduction, construction process, methodology, practitioner, literature review, big data, process improvement, information and communication technology, market intelligence, decision support |
| Subjects: | sociology, decision analysis, building construction, information systems, economics, research methods, management, energy systems, data analysis and analytics, data science, construction type, strategic management, practitioner, computing systems |
| Topics: | Digital Applications, Organizational Design, Site Management, Research Practice, Business Strategy, Cost Management, Construction Technology, Sustainability, Risk Management, Roles and Professions |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here