Big data: a new revolution in the UK facilities management sector

Background
In today’s competitive economy, Facilities Management (FM) organisations are facing increasing pressure from client organisations to manage facility portfolios with stringent goals for both cost efficiency and environmental impact. The ability to integrate, visualise and closely monitor building data (such as space demand and utilisation, energy usage and cost, air quality, temperature variations, lighting etc.) is critical to achieving these goals. While it is commonly accepted that big data (data that surpasses the processing capability of traditional database systems) and analytics present major opportunities to generate value for a wide variety of safety and maintenance operations,
many within the FM sector have yet to fully recognise this value. Therefore, this study was conducted to explore how Big Data Analytics (BDA) is currently utilised and the opportunities it offers for FM organisations. The aims of the research are twofold:
• to examine the drivers, challenges, opportunities and benefits of using BDA for FM
• to develop a methodological framework to implement BDA into FM organisations.

Methodology
A mixed methods research methodology was adopted with an exploratory sequential research design utilising both qualitative and quantitative inquiries. The qualitative aspect of research focused on a case-study approach to provide a context for understanding the novel research area and formulate a theory regarding BDA implementation in FM organisations. Three UK-based organisations implementing BDA were reviewed and semi-structured interviews were conducted with nine domain experts (three in each organisation). The interview responses were then analysed using cognitive mapping. The themes that emerged from qualitative research informed the development of both a quantitative research instrument (questionnaire)
and a methodological framework. The output variables were tested through a questionnaire survey from 52 respondents. Data triangulation was applied to ensure validity of the data and results.

Main findings
The descriptive statistical findings in general indicate a rising interest in applications of BDA and Internet of Things (IoT), with interest expected to further increase in the future. In terms of big data maturity, the majority of the respondents (72%) highlighted that their organisations were still in the early stages (i.e. pre-adoption 37% and early adoption 35%) of deploying big data and analytics. Given the incipient stage, operational systems like Computer-Aided Facilities Management (CAFM), Building Management System (BMS), Environmental Management System (EMS) and Building Information Modeling (BIM) were confirmed to be the key sources of BDA. The study also revealed that larger FM organisations have been the early adopters of this technology. 
In a fiercely cost-competitive market place, FM organisations are banking on automation and innovation to keep up with ever-expanding client demands and remain competitive. In a price-constrained market, BDA and IoT applications are deployed to:
• stay competitive
• reduce business risks
• maximise asset and equipment performance
• enhance customer-oriented services.

The combination of big data and analytics is driving opportunities to automate maintenance monitoring, maximise equipment uptime, minimise risks and streamline contracts. To efficaciously bring innovation driven strategies to fruition, focus must be placed on creating an absolute connection between broader strategic ambitions and downstream operational applications. 
Despite the growing interest in a data-driven approach, organisations are facing significant hurdles to extract value from big data usage. The key challenge currently facing organisations is securing access to reliable and potent big data. BDA implementation, more than anything, requires getting the basics right – ensuring that asset/facility data is consistent, accurate and complete. Other challenges include:
• the analytics talent gap
• issues with legacy systems
• establishing a clear business case for funding.
Given these challenges, the rollout and appropriation of BDA occurs when organisations commission pilot projects, proof-of-concepts (areas that can provide value to business) and evaluate ways of integrating big data with existing systems and processes in an agile manner. This will invariably require:
• training the existing workforce to put digitisation into place
• investing in sophisticated CAFM systems to digitise asset portfolio
• knowledge sharing/collaboration to refine predictive rules.
As such, the findings have illuminated the centrality of focusing on methodology, people, technologies and best practices to develop comprehensive and viable big data strategies. The drivers, challenges, strategies and outcomes are summarised in Figure 1 on page 9.

Conclusion and implications
Although this has been a ‘dipping toe in water’ kind of an exercise for most FM companies, it is clear from the results that BDA is becoming imperative for transforming maintenance engineering. The newly acquired ability to bring assets together in a smart network for efficient working is providing greater visibility into plant/asset features, trends, signatures and performance than ever before. The digital asset footprints are in turn serving to meet the information/compliance demands from clients, regulators and decision makers. In future, big data investments by FM organisations will continue to gain
traction as sophisticated and new technologies emerge. The respondents expect their respective firms to increase BDA investments by:
• developing comprehensive IoT platforms
• building analytical capabilities
• utilising artificial intelligence (AI) and augmented reality (AR).
This would require FM organisations to partner/collaborate with technology firms, consultants and higher education institutions to scale-up the applications for enterprise level adoption.