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

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.

[1]  C. Eastman,et al.  BIM for Owners and Facility Managers , 2008 .

[2]  Marjan Sarshar,et al.  Application of the balanced score‐card concept to develop a conceptual framework to measure facilities management performance within NHS facilities , 2002 .

[3]  R. Kaplan,et al.  Using the balanced scorecard as a strategic management system , 1996 .

[4]  Alexandros Labrinidis,et al.  Challenges and Opportunities with Big Data , 2012, Proc. VLDB Endow..

[5]  Marjan Sarshar,et al.  Process improvement through performance measurement: the balanced scorecard methodology , 2001 .

[6]  R. Kaplan,et al.  The Balanced Scorecard: Translating Strategy into Action , 1996 .

[7]  Dilanthi Amaratunga,et al.  Moving from performance measurement to performance management , 2002 .

[8]  Murtaza Haider,et al.  Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..

[9]  David Baldry,et al.  A conceptual framework to measure facilities management performance , 2003 .

[10]  Joachim Denzler,et al.  Labeling Examples That Matter: Relevance-Based Active Learning with Gaussian Processes , 2013, GCPR.

[11]  Dilanthi Amaratunga,et al.  Case study methodology as a means of theory building: performance measurement in facilities management organisations , 2001 .

[12]  Bongsik Shin,et al.  Data quality management, data usage experience and acquisition intention of big data analytics , 2014, Int. J. Inf. Manag..

[13]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

[14]  Per Anker Jensen,et al.  The origin and constitution of facilities management as an integrated corporate function , 2008 .

[15]  Russell Walker From Big Data to Big Profits: Success with Data and Analytics , 2015 .

[16]  Karen Kensek,et al.  BIM Guidelines Inform Facilities Management Databases: A Case Study over Time , 2015 .

[17]  Bora Caglayan,et al.  Merits of Organizational Metrics in Defect Prediction: An Industrial Replication , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.

[18]  Deepa Gupta,et al.  Big Data Process Analytics: A Survey , 2014 .

[19]  David Sinclair,et al.  Effective process management through performance measurement: part I – applications of total quality‐based performance measurement , 1995 .

[20]  Xindong Wu,et al.  Data mining with big data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[21]  Geoff Hulten,et al.  Mining high-speed data streams , 2000, KDD '00.

[22]  Patrick Martin,et al.  Towards Cloud-Based Analytics-as-a-Service (CLAaaS) for Big Data Analytics in the Cloud , 2013, 2013 IEEE International Congress on Big Data.