The future of big data in facilities management : opportunities and challenges

Purpose: This paper explores the current condition of the Big Data concept with its related barriers, drivers, opportunities and perceptions in the AEC industry with an emphasis on Facilities Management (FM). Design/methodology/approach: Following a comprehensive literature review, the Big Data concept was investigated through two scoping workshops with industry experts and academics. Findings: The value in data analytics and Big Data is perceived by the industry; yet the industry needs guidance and leadership. Also, the industry recognises the imbalance between data capturing and data analytics. Large IT vendors’ developing AEC industry focused analytics solutions and better interoperability among different vendors are needed. The general concerns for Big Data analytics mostly apply to the AEC industry as well. Additionally however, the industry suffers from a structural fragmentation for data integration with many small-sized companies operating in its supply chains. This paper also identifies a number of drivers, challenges and way-forwards that calls for future actions for Big Data in FM in the AEC industry. Originality/value: The nature of data in the business world has dramatically changed over the past 20 years. This phenomenon is often broadly dubbed as “Big Data” with its distinctive characteristics, opportunities and challenges. Some industries have already started to effectively exploit “Big Data” in their business operations. However, despite many perceived benefits, the AEC industry has been slow in discussing and adopting the Big Data concept. Empirical research efforts investigating Big Data for the AEC industry are also scarce. This paper aims at outlining the benefits, challenges and future directions (what to do) for Big Data in the AEC industry with a FM focus

Big data analytics as a management tool: An overview, trends and challenges

Innovative digital technologies and ever-changing business environment have and will continue to transform businesses and industries around the world. This transformation will be even more evident in view of forthcoming technological breakthroughs, and advances in big data analytics, machine learning algorithms, cloud-computing solutions, artificial intelligence, internet of things, and the like. As we live in a data-driven world, technologies are altering work and work-related activities, and everyday activities and interactions. This paper is focused on big data and big data analytics (BDA), which are viewed in the paper from organisational perspective, as a means of improving firm performance and competitiveness. Based on a review of selected literature and researches, the paper aims to explore the extent to which big data analytics is utilized in companies, and to highlight the valuable role big data analytics may play in achieving better business outcomes. Furthermore, the paper briefly presents main challenges that accompany the adoption of big data analytics in companies

A Systematic Literature Review : Big Data Analytics Impact on Firm Performance

The master thesis provides a systematic literature review on the relationship between big data analytics and firm performance. Despite the increasing adoption of big data analytics among practitioners, the current studies on this topic still need to be completed, especially in the social sciences context. The paper aims to fill the gap in the literature by categorizing the diverse models of big data analytics and identifying the main drivers of its successful implementation in firms. The research methodology used in this study is a systematic review of papers on the Web of Science (WoS) database that focuses on using big data analytics to improve firm performance. The systematic literature reviewed, analyzed and summarized sixty published articles in the field. The main contribution of this paper is twofold: first, identifying the current state of research on big data analytics and its impact on firm performance, and second, determining the BDA factors that positively improve firm performance. The results of the literature analysis are presented, showing the frequency-related findings of the selected papers. The paper concludes with a discussion, directions for future research, and a solid conclusion

Big data analytics in e-commerce: A systematic review and agenda for future research

There has been an increasing emphasis on big data analytics (BDA) in e-commerce in recent years. However, it remains poorly-explored as a concept, which obstructs its theoretical and practical development. This position paper explores BDA in e-commerce by drawing on a systematic review of the literature. The paper presents an interpretive framework that explores the definitional aspects, distinctive characteristics, types, business value and challenges of BDA in the e-commerce landscape. The paper also triggers broader discussions regarding future research challenges and opportunities in theory and practice. Overall, the findings of the study synthesize diverse BDA concepts (e.g., definition of big data, types, nature, business value and relevant theories) that provide deeper insights along the cross-cutting analytics applications in e-commerce

Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review

This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives-for application-based opportunities, with emphasis on those that address big data with geospatial components

Big Data and Dynamic Capabilities: A Bibliometric Analysis and Systematic Literature Review

Purpose–Recently, several manuscripts about the effects of big data on organizations used dynamic capabilities as their main theoretical approach. However, these manuscripts still lack systematization. Consequently, this paper aims to systematize the literature on big data and dynamic capabilities. Design/methodology/approach–A bibliometric analysis was performed on 170 manuscripts extracted from the Clarivate Analytics Web of Science Core Collection database. The bibliometric analysis was integrated with a literature review. Findings–The bibliometric analysis revealed four clusters of papers on big data and dynamic capabilities: big data and supply chain management, knowledge management, decision making, business process management and big data analytics (BDA). The systematic literature review helped to clarify each clusters’ content. Originality/value – To the authors’ best knowledge, minimal attention has been paid to systematizing the literature on big data and dynamic capabilities

Big Data Supply Chain Analytics (BDSCA): Ethical, Privacy and Security challenges posed to business, industries and society

This study conducted a comprehensive review of big data supply chain analytics (BDSCA). The paper explored the application of big data in supply chain management and its benefits for organisations and society. The paper also examined the ethical, security, privacy and operational challenges of big data techniques, as well as the potential reputational damages to businesses. The review outlined four principal facets, namely: Big data analytics, applications, ethics and privacy issues, and how organizations employed this emerging tool to anticipate and even predict the future and direct their operations. These principle facets are built across the multiple levels and unique conceptual standpoints indicated by 7 themes and 14 sub-themes. These themes were generated based on 120 articles (2005−2020) drawn mainly from leading academic journals. Overall, there is a considerable consensus across current literature that big data analytics extend far beyond just reinventing the supply chain. It has the potential to support more responsive next-generation of global companies who are operating in an increasingly challenging and uncertain environment

Big Data Analytics for Wireless and Wired Network Design: A Survey

Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions. In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks’ control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial efforts toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks