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Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

Purpose The purpose of this paper is to investigate the level of implementation of Six Sigma (SS) in the construction industry of Pakistan along with the current state of affairs and the challenges, and opportunities for a successful implementation. Design/methodology/approach The research is purely exploratory in nature. Based on published work, critical success factors are gathered, and a number of questionnaire surveys and interviews are conducted to refine and quantify their impact. A system dynamics framework to assess the SS influence on project success is developed and case study project are simulated. Findings The construction industry of Pakistan is still functioning in a traditional way; marred with low level of awareness and ad hoc approaches, the findings point to a huge improvement opportunity. Further, when under planning projects are exposed to SS, the chances of project success improve better than under execution projects. Research limitations/implications The limited level of awareness possessed by the respondents constrains the possible outreach of this work in industrially developed contexts. However, this work may become an impetus for further research in managing quality in construction industry. Practical implications The findings can be used to improve the quality provision of construction projects. Originality/value This work may trigger an important debate over the research and implementation of SS in the construction industry of developing countries that may greatly benefit by improving the quality of their projects and rectify their diminishing reputation for project success.

Purpose This paper aims at collecting and reviewing the published literature on the Six Sigma in construction along with its critical success factors (CSFs). Design/methodology/approach The research is based on literature review. Based on the keyword and semantic search techniques, papers published on the topic of Six Sigma during 2000-2015 are retrieved. Frequency analysis is performed to find out significance of identified CSFs, and zoning is performed based on the product of frequency of appearance and parties affected by the CSFs. Findings A total of 69 CSFs are identified as published in the literature. Based on an inclusion criterion of minimum 15 appearances, 22 CSFs are shortlisted for further analysis. Of these CSFs, around 32 per cent fall into red zone (most critical), 50 per cent into yellow and 18 per cent into green zone (least critical). Research limitations/implications This work is limited by partial identification of CSFs. Though based on an extensive search, the retrieved CSFs may not be all the published ones. However, more thorough search techniques can be applied to improve upon this work. Practical implications The findings can be used to facilitate the decision-making in the context of project success. Originality/value This work is an original attempt at gathering Six Sigma CSFs applicable to construction projects. It may be used for further research and development to help ensure project quality and success.

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