Using big data to improve the performance management: a case study from the UAE FM industry

Abstract

Purpose This paper aims to explore how big data analytics (BDA) collected and stored through specific data software [Construction Operations Building Information Exchange [COBie], integrated workplace management systems [IWMS], computer aided facilities management (CAFM), etc.] can play an essential role in improving the performance management system in the facility management (FM) industry. It defines the big data components and explores the benefit of BDA in any business through an extensive literature review and a pilot case study in the UAE. Design/methodology/approach The research was carried out based on a qualitative approach. It attempts to identify through a case study how the data collected and the technologies that go along with will increase the functionality and the efficiency of the FM services. The research studies the implementation of a big FM organization, hereafter referred as “M” of software that exports the data collected from COBie and the computer aided facilities management (CAFM) system and shapes them into input to improve the performance of the FM service providers. The study includes two components in anticipation of providing a complete picture: first, five semi-structured interviews with industry experts and company employees representing the hierarchy of the staff, i.e. top, middle and operational levels; one director, two managers and two operational-level employees were interviewed to determine the current situation of the company in terms of BDA; and second, detailed documents and archives records review for the data collected on a randomly chosen sample of facilities for the period 2013-2015. The interviews were designed to achieve two specific objectives. Primarily, they were aimed at collecting empirical evidence on the existing status of big data within the UAE FM context and at investigating the importance of the data collected for performance measurement in the industry as supported in the literature. Second, these interviews sought to identify any critical issues that need to be addressed within the data collection process when devising the big data platform for FM. Findings The paper seeks to provide a guideline to the service providers in the FM market to understand the importance of big data to be shared from the design and construction to the operational phase as it improves their operational performance. Originality/value This paper studies the impact of big data on the FM performance management, a very recent topic where only few researches were conducted earlier.

Drivers and Challenges Associated With the Implementation of Big Data Within U.K. Facilities Management Sector: An Exploratory Factor Analysis Approach

“…Structured data are normally referred in Building Information Models (BIM) but a typical facility maintenance project also generates additional volumes of unstructured data, captured in photos, graphics, videos and scanned documents etc. [17]. Data systems usually used by FM staff could include Computer Aided Facility Management (CAFM) systems for the evaluation of an organization's operational efficiency and performance, BIM containing spatial information, as well as a variety of sensors and controllable devices (e. g. Heating, Ventilation, Air Conditioning -HVAC, security systems) integrated into the Building Management System (BMS) [38].…”

“…In particular, for the FM industry, there is a mismatch between traditional engineering education and the skills required to develop analytics-driven building O&M strategies [42]. Multiple new roles are required to mediate between statisticians, business managers and IT specialists [17].…”

Lean Management Framework for Healthcare Facilities Integrating BIM, BEPS and Big Data Analytics

“…Development of energy management system by integrating BDA, GIS data, weather data, smart meter, IoT devices to identify energy usage, potential savings, implement different scenarios, energy cost estimation and increase energy awareness [79,80] Usage of BDA in which smart meter data is processed to perform consumption analysis, the discovery of energy consumption patterns, customer segmentation and anomaly detection (by comparing historical data and neighborhood) [81] Development of Big Data integrated cloud technology to optimize building energy consumption [82] Investigation of the real-time flexibility of the electrical devices in the buildings [83] Analysis of smart meter and sensor data to monitor building energy consumption, consumption pattern discovery, segmentation, forecasting, online anomaly detection, feedback service (such as alarms for emergency conditions), benchmarking energy consumption with neighborhoods, remote control and optimal schedules for electricity usage of home appliances and bill tracking [81,[84][85][86] Controlling of HVAC systems by using IoT data to enable energy efficiency in the buildings [87] Integration of BMS/BAS and BDA to enable energy savings and to detect an abnormal condition [88,89] Development of an intelligent data mining model to reveal energy consumption patterns [90] BDA Integrated BIM Development of cloud technology to query BIM and dynamic data which comes from sensors with the integration of BDA [91] Development of a framework for the integration of BIM and BDA to monitor energy consumption [92,93] Integration of BIM and BDA to monitor energy consumption in BIM [94] Integration of BIM and BDA to take a measure for the safety of occupants [95] Integration of BIM and BDA to identify facility performance [96] Maintenance Management Usage of BDA in predictive maintenance analysis for railway facilities [97] As a consequence, the existing studies showed that the wastes related to maintenance, FM queries, energy optimization, energy consumption, prediction of future energy demand [98], detection of anomalies in buildings [89], energy-saving decisions [82], the discovery of energy consumption behaviors of users [99] and HVAC system optimization are a working area for BDA. However, the proposed systems do not offer comprehensive data analytics opportunities on ...…”

Utilizing of Big Data and Predictive Analytics Capability in Crisis Management

“…Proper exploitation of this data contributes to the enhancement of an organization's status and its competitiveness by providing the company with ample information about the external environment in which they operate. Mawed and Al-Hajj (2017) argue that Big Data Analytics (BDA) through their software plays a critical role in enhancing the performance of administrative systems, facility management and crisis predictions. In the past, information, data and trends were complicated to assimilate.…”

“…Many organizations need to acquire further expertise in statistical techniques and analysis as well as in developing and implementing big data strategies. There is a need for IT specialists with the ability to choose the best big data techniques and algorithms, experience in dealing with data privacy issues and knowledge of integrating BDA activities with enterprise business processes (Mawed and Al-Hajj, 2017). Gupta and George (2016) identify some of the abilities needed to accumulate, integrate and deploy organizational big data into precise resources, which they define as BDPA capability.…”