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The future of Big Data in facilities management: opportunities and challenges
Vian Ahmed, Algan Tezel, Zeeshan Aziz, Magda Sibley,
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The future of Big Data in facilities
management: opportunities
and challenges
Vian Ahmed,Algan Tezel and Zeeshan Aziz
School of the Built Environment, University of Salford, Manchester, UK, and
Magda Sibley
School of Architecture, University of Sheffield, Sheffield, UK
Abstract
Purpose –This paper aims to explore the current condition of the Big Data concept with its related barriers,
drivers, opportunities and perceptions in the architecture, engineering and construction (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 an FM focus.
Keywords Big Data, Supply chain, Facilities management, AEC, Business analytics, Database
Paper type Research paper
Introduction
Over the past 20 years, data have significantly expanded in a large scale in various
dimensions. According to a report from International Data Corporation (IDC), in 2011, the
overall created and copied data volume in the world was 1.8 ZB (1021 bytes), which had
increased by nearly nine times within five years (Gantz and Reinsel, 2011). This figure will
double at least every other two year in the near future (Chen et al.,2014). Every day, 2.5
quintillion bytes of data are created and 90 per cent of the data today were created within the
past two years (IBM, 2012). Zikopoulos et al. (2013) expect data volumes to reach 35
zettabytes (270 bytes) by 2020. Manyika et al. (2011) cite that in 2009, nearly all sectors in the
US economy had at least an average of 200 terabytes of stored data per company with more
than 1,000 employees, and many sectors exceeded more than one petabyte in mean stored
data per company.
Big Data in
facilities
management
725
Received 6 June2016
Revised 21 November2016
Accepted 10 January2017
Facilities
Vol. 35 No. 13/14, 2017
pp. 725-745
© Emerald Publishing Limited
0263-2772
DOI 10.1108/F-06-2016-0064
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-2772.htm
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In parallel with this data explosion, the Big Data concept gained momentum in the early
2000s when industry analyst Doug Laney articulated the now-mainstream definition of Big
Data as 3V (volume, velocity and variety), and correspondingly, IDC defined it:
Big data technologies describe a new generation of technologies and architectures designed to
economically extract value from very large volumes (into petabyte volumes) of a wide variety of
data, by enabling high-velocity capture (streaming data), discovery, and/or analysis (Gantz and
Reinsel, 2011).
Four other characteristics that are relevant to Big Data are:
(1) value (the usefulness of data in making decisions);
(2) variability (data flows can be highly inconsistent with periodic peaks);
(3) complexity (the degree of interconnectedness and interdependence in Big Data
structures spread over data warehouse systems); and
(4) veracity (data reliability) (Kaisler et al., 2013;Katal et al., 2013;Zikopoulos et al.,
2013).
Generally built on Relational Database Management Systems (RDBMS), small data sets on
the other hand, are in low volumes (usable chunks, in the Gigabyte-Terabyte range), batch
velocities, from more limited resources, more structured and accessible to answer a specific
question or addresses a specific problem, rather than providing explorative insights
(Sagiroglu and Sinanc, 2013). This data continuum, from Small Data to Big Data, can be seen
in Figure 1.
The distinctive characteristics of Big Data (e.g. the 3Vs and more) presents many
technical and social challenges for business analytics (BA), which are often referred to as the
techniques, technologies, systems, practices, methodologies and applications that analyse
critical business data to help an enterprise better understand its business operations and
Figure 1.
Data continuum
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make timely business decision (Chen et al.,2012). However, its major strengths are
discovering new insights and hidden values, and these also come from a simultaneous
analysis of those multi-sourced and unstructured data flows (Chen et al.,2012). For instance,
in an article published by Ginsberg et al. (2009), the authors explain the use of Big Data
analytics by Google to successfully forecast the spread of an influenza epidemic based on
Internet search entries. In summary, discovering explorative insights and complex-
interrelations that cannot be seen from smaller data sets, the Big Data information comes
from multiple, heterogeneous, autonomous sources with complex and evolving relationships
and keeps growing (Wu et al., 2014).
Business analytics and Big Data
According to Boyd and Crawford (2012), Big Data analytics is a cultural, technological and
scholarly phenomenon that rests on the interplay of technology, analysis and mythology;
the widespread belief that large data sets offer a higher form of intelligence and knowledge
that can generate insights that were previously impossible, with the belief of truth,
objectivity and accuracy. To turn the mythology into a reality, a significant value for
business and operations, return of investment through Big Data analytics, with an effective
deployment of both the technology and analysis should be attained. Otherwise,
organisations risk owning huge data chunks without properly utilising those data as Big
Data.
Japkowicz and Stefanowski (2016) draws a comparison between the traditional data
mining in Small Data and Big Data analysis, as illustrated in Table I. Highlighting that
traditional data mining and Big Data are two different concepts, the table essentially
summarises the key technical differences between Small Data and Big Data from a BA
perspective.
Big Data has the potential to generate business value. From a survey of 325 commercial
enterprises, Russom (2011) indicates that Big Data analytics can generate value in the form
of better targeted influencer marketing, more varied and accurate business insights,
recognition of business opportunities, automated decisions for real-time processes,
definitions of customer (user) behaviours, customer retention, detection of fraud,
quantification of risks, trending for market sentiments, understanding of business
(behaviour) changes, better planning and forecasting, resource optimisation, identification of
root causes of cost and understanding consumer(user) behaviours. Pries and Dunnigan
(2015) give similar examples of real-life Big Data benefits on meta knowledge discovery
from vast amount of academic publications, curriculum analysis and design in education,
maintaining and increasing sales and market share through predictive analysis, increasing
security (password control, penetration detection, copyright and patent violation, etc.),
customer-centric approach, risk-centric and finance-centric insurance applications,
customisation of travel experiences, optimisation and tracking of logistics assets (freight,
vehicles, etc.), and GIS-based city management, crime fighting and resource optimisation
examples from New York, Chicago, Los Angeles and Baltimore in the USA. In parallel with
this, a recent research by Gartner (2014) conducted in 2014 indicated that of all the surveyed
302 organisations in the USA, 73 per cent of the respondents had invested or planned to
invest in Big Data in the next 24 months, up from 64 per cent in 2013. The survey also
highlights that the number of organisations stating they had no plans for Big Data
investment fell from 31per cent in2013 to 24per cent in2014.
However, according to Davenport and Dyché (2013), very few organisations have taken
the steps to rigorously quantify the return on investment for their Big Data efforts, and that
the proof points about Big Data often transcend money represented by cost savings or
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revenue generation for the long term. The authors instance a comparative return on
investment study in two different environments in 2011 in similar business contexts. The
first environment was a high-speed data warehouse appliance using traditional data
warehouse usage (ETL) and data provisioning processes. The second environment was Big
Data running on a newer Big Data technology using massively parallel (MPP) hardware.
The Big Data project surpasses the traditional project in cumulative cash flow, net present
value and internal rate of return by significant margins with a shorter break-even point
(Davenport and Dyché, 2013).
While implementing Big Data analytics, those technical issues can be potential barriers:
data presentation;
data redundancies;
data lifecycle management;
data security and confidentiality;
energy management;
interdisciplinary cooperation;
Table I.
Traditional data
mining (Small Data)
vs Big Data analysis
Traditional data Mining (Small Data Sets) Big Data Analysis
Memory access Stored in centralised RAM and can be
scanned efficiently several times
The data may be stored on highly
distributed data sources and single scans
are common
Computational
architecture
Serial, centralised processing is sufficient Parallel and distributed architectures may
be necessary
Data types The data source is relatively homogeneous.
The data is static and, usually, of
reasonable size
The data may come from multiple data
sources which may be heterogeneous and
complex. The data may be dynamic and
evolving
Data
management
The data format is simple and fits in a
relational database or data warehouses
Data formats are usually diverse and may
not fit in a relational database. The data
may be greatly interconnected and needs to
be integrated from several nodes
Data quality The provenance and pre-processing steps
are relatively well documented. Strong
correction techniques were applied for
correcting data imperfection
The provenance and pre-processing steps
may be unclear and undocumented. There
is a large amount of uncertainty and
imprecision in the data
Data handling Security and privacy are not of great
concern
Security and privacy may matter. Data may
need to be shared and the sharing must be
done appropriately
Data processing Only batch learning is necessary. Learning
can be slow and off-line. The data fits into
memory. All the data has some sort of
utility
Data may arrive in a stream and need to be
processed continuously. Learning may need
to be fast and online. The scalability of
algorithms is important. The data may not
fit in memory. The useful data may be
buried in a mass of useless data
Result analysis Statistical significance results are
meaningful. Many visualisation tools have
been developed Interaction with users is
well developed. Answers to specific
questions are obtained
With massive data sets, non-statistically
significant results may appear statistically
significant. Traditional visualisation
software may not work well with massive
data. Explorative discoveries are common
Source: Adopted from Japkowicz and Stefanowski (2016)
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inexpert staff;
investment and maintenance costs;
lack of business leadership;
hardships in designing analytic systems; and
lack of current database software in analytics (Chen et al., 2014;Sagiroglu and
Sinanc, 2013).
Also, other more generic concerns include:
the objectivity and accuracy of Big Data resources;
the widespread belief that bigger data sets are always better without giving much
concern to methodological issues and data quality;
ethical concerns in data use;
the highly context-dependent nature of Big Data analytics; and
that limited access to large volumes of data by small groups in the society can create
information divides or concessions (a class of the Big Data rich) (Boyd and
Crawford, 2012).
According to McAfee et al. (2012), the main managerial challenges for Big Data are in:
creating leadership teams that set clear goals, define what success looks like and
ask the right questions;
talent management of the professionals competent in capturing, analysing, inferring
from and presenting Big Data sets;
management of technology; hardware, which are becoming cheaper (i.e. Big Tables
spread on parallel (cheaper) computers) and software that are becoming open source
(i.e. the Java-based Hadoop framework which incorporates data map reducing), but
also generally are out of the skill sets of IT departments;
effective decision-making through a flexible organisational structure; and
sustaining a company culture that underpins the data-driven organisation.
Big Data and smartness in the AEC industry
With the constant flow of large data sets generated by different organisations, individuals
and inanimate objects in many different data formats, the current nature of data in the
architecture, engineering and construction (AEC) industry is essentially of Big Data (Jiao
et al., 2013). Despite this, Brown et al (2011) identified in a report from McKinsey Global
Institute that the ease of capturing Big Data’s value and the degree of its potential for the
AEC industry is comparatively low, which highlights the magnitude of the challenge the
industry is facing to obtain a real value from Big Data analyticsfor its operations. Spithoven
et al. (2011) indicate that the traditionally low-tech AEC industry has lower knowledge
absorptive capacity to internalise external developments (e.g. academic research or Big Data
analytics) and R&D. On the other hand, instancing two possible implementation areas in
business information modelling (BIM)-based maintenance forecasting for facilities
management (FM) and project performance analysis, Hardin and McCool (2015) state that
the interest in Big Data has been rapidly growing in the AEC industry. Although still
immature, leveraging those vast amounts of unstructured data is an asset that can greatly
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benefit the industry in terms of better resource management and forecasting, risk mitigation
and efficiency improvements in their resource use (McMalcolm, 2015;Rijmenam, 2015).
Although Big Data-focused research in the AEC industry is currently very limited, some
empirical works highlighting the value of data mining over smaller data sets to discover
knowledge patterns exist in the literature. For FM, data mining was utilised to understand
facilities’use patterns (Peitgen et al., 1992), maintenance cost patterns and preventive
maintenance planning (Liew and Rosenblatt, 2003) and for energy-efficiency purposes
(Reffat et al.,2006). However, one should note that although Big Data uses some data mining
techniques, it goes beyond the analytics and technology used for smaller data sets as
explained in Figure 1 and Table I. Data management in the AEC industry also presents its
own challenges:
during the life cycle of an asset, different organisations collect and own data
fragmentally;
alongside conventional data media (i.e. paper files, old project drawings on papers),
existence of many digital data types and interoperability issues;
lack of standardisation in data management;
blurred contractual configurations as to the management of data;
skill shortages for effective data utilisation;
low penetration of and buy-in for innovation;
small and medium-sized organisations (SMEs) dominating the supply chain that are
risk averse and generally lacking the necessary funds for data management
investments;
hardships faced during the collection of correct as-is data from live construction
conditions; and
issues associated with demonstrating the business case and return on investment
for data management to the industry (Soibelman and Kim, 2002;Bakis et al., 2007;
Ergen et al., 2007;Chen and Kamara, 2011;Jiao et al., 2013).
The emergence of connected sensor networks and ubiquitous computing, often explained
under the term the Internet of Things (IoT), has recently added new dimensions to the
applications of Big Data. Chui et al. (2010) define the IoT as: sensors and actuators
embedded in physical objects –from roadways to pacemakers –are linked through wired
and wireless networks, often using the same Internet Protocol (IP) that connects the internet.
The emergence of the IoT through data generating inter- and intra-connected sensor
networks in everyday objects coupled with Big Data analytics gives way to the collection of
and inference from an immense amount of data to create “smart”cities, the new urban
environment, one that’s designed for performance through information and communication
technologies (ICTs) and other forms of physical capital (Chen et al.,2014).
With the effective management of resources through intelligent management, visionaries
hope that cities will drive a higher quality of life for citizens, drive down waste and improve
socio-economic conditions (Stimmel, 2015). Hollands (2008) identifies five main
characteristics of a “smart city”of this nature from the literature:
(1) widespread embedding of ICT into the urban fabric;
(2) business-led urban development and a neoliberal approach to governance;
(3) a focus on social and human dimensions of the city from a creative-city
perspective;
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(4) the adoption of smarter communities agenda with programmes aimed at social
learning, education and social capital; and
(5) a focus on social and environmental sustainability.
Many city governments now use real-time analytics in their data centres to manage the
aspects of how a city functions and is regulated in dynamic traffic management (adjusting
traffic lights and speed limits), efficient use of the police force, environmental monitoring (air
pollution, water levels and seismic activity) and general city information visualised on the
city map (e.g. energy monitoring, weather, air pollution, public transport delays, public bike
availability, river level, electricity demand, the stock market, Twitter trends in the city,
traffic camera feeds, the happiness level, etc.) (Kim et al.,2012;Kitchin, 2014).
Another closely related use of those technologies is in “smart grids”, in which electrical
grids for power delivery are managed through sensors, readers and data analytics for
optimised energy load balancing and forecasting, improved efficiency and sustainability in
energy management (Amin and Wollenberg, 2005). Also, in the maintenance of industrial
facilities and power plants, where the reliance on sophisticated electro-mechanic equipment
is high, Big Data analytics can yield significant savings through predictive maintenance
(Song et al., 2013;Chappel, 2014).
From the AEC industry’s perspective, Big Data analytics, with other emerging
technologies, such as theIoT, can translate into:
reduction in asset maintenance costs;
better tailored or customised services to individuals and groups;
more accurate forecasting;
risk mitigation;
resource levelling and optimisation;
performance evaluation; and
informed investment decisions, and, on a greater scale, “smarter”cities with
predictive solutions (Reffat et al., 2006).
Alongside “smart cities”and “smart grids”, it can be inferred that Big Data will soon
cascade down to the realisation of “smart”design offices, construction sites and facilities.
Despite the potential of Big Data, there is little research conducted to understand the Big
Data phenomenon in the AEC industry. The rest of this paper therefore looks into the
barriers, drivers and potential applications of such data in the AEC industry, particularly for
FM.
Methodological approach
To explore the potential drivers and challenges of Big Data within the AEC industry, two
industry workshops were held, bringing together a total of 200 participants in partnership
with key industry players and professional bodies. Table II shows further details about
these workshop events.
Workshop 1 –Big Data challenges and opportunities in the AEC industry
This workshop was held in collaboration with Asite Solutions, which is an IT solution
provider within the AEC industry. The workshop brought together an average of 130
people, 70 per cent of which were from the AEC industry, while 20 per cent were from
academia and 10 per cent from the IT sector. The workshop aimed at assessing the level of
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understanding of Big Data, identifying the drivers and challenges perceived by the
attendees. Amongst the participants, 73 per cent considered themselves neither novice nor
expert in Big Data, while 18 per cent considered themselves as experts and 12 per cent as
novices. The day-long workshop invited a number of key speakers to share their
understanding of Big Data from the technological and industrial perspectives and to share
some of the applications used in the AEC industry. The participants were then asked to
engage in a clicker exercise to give their views of the Big Data drivers and challenges. This
workshop identified FM as a discipline with the greatest potential for the integration that
can benefit from the utilisation of Big Data in the AEC industry. The outcome of this
workshop provoked the organisation of a follow-on industry event, hence, Workshop 2.
Workshop 2 - Big Data in facilities management
This workshop was held in collaboration with a number of industry partners and sponsored
by the CIOB and RICS. An average of 20 per cent of the attendees were from academia, while
70 per cent of the attendees were from the AEC industry (with the majority of FM expertise)
and 10 per cent from other industries. This paper shares the outcome of the focus group
discussions, and analysis of the participants’feedback identifies around the opportunities
and challenges of Big Data in FM.
Results and analysis
Workshop 1 –Big Data challenges and opportunities in the AEC industry
Following a number of presentations around Big Data, its theories and concepts, the
workshop participants were asked to select their preferred choice by using the clickers in
relation to the questions below. These results were further discussed with the support of a
panel of experts.
Challenges facing Big Data. The participants were asked to select one of the factors that
they perceived to be the most challenging for utilising Big Data in the AEC industry. The
results showed that 55 per cent of the participants found that obtaining, structuring and
managing the data are one of the most challenging factors. Further, 24 per cent thought the
need for change in the culture and attitude towards sharing project data, while 12 per cent
voted for the need to understand the business value (Figure 2).
It can therefore be argued that the majority of these challenges are somewhat interlinked,
whereby changing the way people work and appreciating the business value of the
processes they engage in and how these processes can be a vehicle to provide the
organisation with the relevant business intelligence, may then help with the way data is
handled, exchanged and stored.
Technological challenges. The participants were asked to select what they perceive to be
the most technologically challenging for Big Data. The responses showed that 67 per cent of
Table II.
Workshop details
Workshop Date Venue No. of attendees
Workshop 1
Big Data Challenge and
Opportunities in the AEC
industry
February 2015 In partnership with Asite Solutions and
CIOB –Google Campus, London
130
Workshop 2
Big Data in Facilities
Management
June 2015 In partnership with CIOB and RICS –
Media City Manchester
70
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the participants felt that making use of structured (which are in silos) and unstructured data
(in different formats) are the most technologically challenging factors, followed by 19 per
cent capturing the right data and real-time delivery to the right people, with 14 per cent
voting for having data privacy and security to access and deploy data (Figure 3).
Interestingly, factors relating to computational capacity for storing, analysing and
understanding the data, and data privacy and security to access and deploy data did not
gather any score. The panel discussions around these issues indicated that the participants
felt that the technology is available and it is out there; however, the main challenge is for
organisations to utilise these technologies to serve their organisational goals.
Operational challenges facing Big Data. When asked about the biggest operational
challenges that face the utilisation of Big Data, 42 per cent of the participants felt that
finding the right talent that is capable of working with new the technology and interpreting
Figure 3.
Technological
challenges for Big
Data in the AEC
industry
Figure 2.
Challenges facing Big
Data in the AEC
industry
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the AEC processes is the most significant barrier. This was followed by the cost of the
technology (26 per cent) and meeting the high cost of recruiting technologists (16 per cent)
(see Figure 4).
It therefore seems that organisations lack the technological talents that are capable of
understanding the balance between the AEC processes and aspects of the technology that
can enhance these processes, and that without a good understanding of the business value
that Big Data can bring, organisations may be missing out on the long-term investment in
Big Data.
Data challenges. The participants were asked to identify the most challenging issue
about the data. The response percentages were fairly close, whereby 37 per cent and 34 per
cent of the participants felt that the structuring of the data and the uniformity and
consistency of the data are the most significant challenges before Big Data; while a
relatively close percentage to these (29 per cent) thought the existence of paper-driven
documents is a major challenge (see Figure 5).
During the panel discussions of these results, strong arguments were put forward in
relation to the fragmented nature of the construction process, with the existence of the
project informationin different forms and formats.
AEC readiness for Big Data. The participants were asked to assess the AEC industry’s
readiness for Big Data. Only 33 per cent of the participants felt that the industry is ready to
utilise Big Data, while 67 per cent felt that the industry is not yet ready (see Figure 6).
Potential for integrating Big Data. The participants were asked in which of the project
aspects do they see the most potential in integrating Big Data in the AEC industry. The
results show that the use of Big Data has the biggest potential use for the facilities and
operations management aspect of the project life cycle, followed by the sustainability and
reduction of energy consumption aspect, with little discrepancy between this aspect and the
feasibility, design, procurement and construction aspect (Figure 7).
Figure 4.
Operational
challenges facing Big
Data in the AEC
industry
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These results were key to exploring the potential of Big Data in FM, which is the centre of
this paper.
Workshop 2 –Big Data in facilities management
Given the participants’responses to the earlier questions and their identification of Big Data
as a tool which can bring in value to the FM discipline, this section shares the outcome of the
second workshop on Big Data and FM. Following a number of keynote presentations by
discipline experts from the industry and academia during this workshop, the participants
were asked to provide writtenfeedback on what they perceived to be the:
Figure 5.
Data challenges for
Big Data in the AEC
industry
Figure 6.
Readiness of the AEC
industry for Big Data
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drivers for the integration of Big Data and FM;
challenges for the integration of Big Data and FM; and
steps the FM sector should take for a better use of Big Data
The rest of this section discusses thesis findings based on the three research questions
above.
Drivers for the integration of Big Data and FM. Figure 8 shows a summary of the
participants’responses, which were grouped in three main categories:
(1) Category I –Drivers relating using Big Data in direct relation to the nature of the
FM discipline: The participants came up with a list of factors that they perceived as
the drivers for using Big Data for FM. These evolved around improving the
decision-making process, for example, the effective use of heating and cooling
systems within spaces, as well as providing more sustainable design solutions that
can help reduce energy consumptions. Big Data is also seen as a means for helping
improve the decision-making process, not only at the design stage, where working
on large-scale designs for smart cities, but also with making decisions on
managing the facilities in terms of predicting maintenance dates or identifying
faults and problems within the facilities, and the consequences of these decisions
on nearby facilities. Giving organisations a competitive advantage is also another
important driver for using Big Data.
(2) Category II –Drivers relating to the use of Big Data based on external factors: Some
of the external factors mentioned by the participants that could drive organisations
to integrate Big Data evolve around understanding the role of Big Data in FM
through live examples or shared case studies and practices. The participants also
felt that there are no current policies that force organisations to adopt certain
practices to develop an infrastructure for Big Data within organisations. Also, it
was identified that there is a need for more research to be carried out to show how
the benefits of Big Data can be utilised for FM.
(3) Category III –Drivers relating to the technological factors: The participants’
responses evolved around organisations being equipped with systems and
platforms that have the capacity and capability to capture large volumes of data,
with a level of intelligence that can react to this data input in real time, as well as
developing the right IT skills within organisations that will deal with the data.
Figure 7.
Potential for
integrating Big Data
in the AEC industry
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These results align with the nature of the FM discipline where, for example, maintenance
data or energy-level monitoring data can be actively and proactively captured by different
processes and systems, with intelligent simulations embedded within them that can assist
the end user with managing the facilities in a more efficient and effective way.
It is apparent from the participants’responses that there are a number of drivers that
recognise the relevance of Big Data to the FM discipline, to provide more sustainable and
easily managed facilities, but with the support of the relevant skills, platforms and policies.
Challenges for the integration of Big Data and FM. When asked about the challenges
facing the utilisation of Big Data in FM, the participants’responses were boxed in three
main categories (Figure 9):
(1) Category I –Challenges facing the FM discipline: A number of challenges were
identified under this category. This category evolved around producing a
meaningful value for FM out of extensive data that are generated from different
processes out of pre- and post-construction, and without doubt is closely linked to
the lack of knowledge and skills in differentiating between the value of these data,
and how it could be utilised for FM. One of the interesting points that was
highlighted is the inconsistency of data generated (i.e. the handover data from
construction to maintenance/operations), which makes its reliability questionable
under different circumstances, and that the field of Big Data in FM is still in its
infant stages and needs to grow in maturity. It was discussed that the FM
Figure 8.
Drivers for the
integration of Big
Data and FM
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management
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discipline itself tends to be known for its short-term thinking by dealing with the
problems there and then, and after the construction process had been completed, at
the same time (and to its nature) that FM discipline tends to lack collaboration and
inter-linkage of data with other processes and disciplines within the same project
or at a larger scale to deliver “smartly”managed facilities.
(2) Category II –Challenges based on external factors: Under this category the
participants identified some of the external factors that relate to ethical issues, one
of which being, who owns which data and how much of it can be used by FM
managers without compromising data protection acts. For example, the
government’s data protection act and ethical and privacy issues around the use of
personal data were underlined. That said, one of the main challenges mentioned
has to do with the possible bias in the generated data which could be because of
wrong inputs or environmental challenges. The participants also highlighted the
lack of good examples that can guide the discipline to implement Big Data in FM.
(3) Category III –Technological challenges: Similar challenges to the drivers for
adopting Big Data can be depicted under this category, which revolve around the
security and scarcity of the data to be instantly available via technological
solutions and channelling the data from the different stages of a project using
relevant technological solutions (for example sensors) so that the data can be
linked and connected to the FM stages, as well as the compatibility of the data
generated from other processes.
Figure 9.
Challenges for the
integration of Big
Data and FM
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Therefore, the main challenges facing Big Data for FM seem to focus mostly on the data
itself. While the inter-linking between the data generated from different processes seems to
pose one of the biggest challenges, the need for suitable platforms for data processing, data
protection, data accuracy and data security also seems to add to these challenges.
Steps the FM discipline should take for a better use of Big Data. The participants’
responses were also categorised as shown below (Figure 10):
Category I –Steps within the FM discipline: The participants highlighted the need for
recruiting individuals who are equipped with the right discipline-special skills as
well as the relevant technological skills to help with the integration of the
technology and the processes that feed into FM. There is also a need for a change in
the culture of working and increasing the awareness of the application of Big Data
internally within the organisation and to draw lessons from other industries such as
the retail industry, where the Big Data concept has started to play an important role
in predicting patterns in customer behaviour and customising customer experience.
There also needs to be a well-defined strategy that allows for clear data inputs and
value of outcomes and benefits.
Category II –Steps reliant on external factors: Besides some of the factors that were
previously mentioned, such as the assurance around data protection issues and the
presence of good practice cases, there is a desire for the formation of a Big Data
industry group for FM to set the agenda for the discipline, in a similar way in which
it has happened for BIM. A regulatory body will also encourage utilising some
Figure 10.
Steps the FM
discipline should take
for a better use of Big
Data
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standardised ways for setting up Big Data sets for FM for a better integration of the
data through the project phases and processes.
Category III –Technological steps: One of the valuable points made under this point
is the need for developing FM-specific data assessment (analysis) models. These
models could be supported with distributed computer system architectures (grids)
and parallel processing of Big Data sets from different organisations in the industry.
Those connected systems can be used, for instance, for identifying the level of
occupancy within a facility and the associated energy consumption that is relevant
to such occupancy and how certain intelligent adjustments could be made
accordingly.
The results therefore call for a genuine need for cultural changes in the way the FM
discipline operates in terms of data sharing and handling, with a need for a regulatory body
or a focus group that can drivethis change and bring about new models of data analysis and
processing.
Discussions around Big Data in FM. Following the evaluation of the displayed
questions, the attendees engaged in open-ended discussions that evolved around these
challenges, drivers and opportunities, reaching some important conclusions as shown
below:
The majority of the attendees think that the Big Data concept is something new to
the FM sector.
Information and data should be treated as assets.
The role of big players in disseminating Big Data in FM was highlighted.
The attendees’expectation for the timeframe in which FM organisations can make
use of Big Data vary greatly from within 12 months to more than three years.
The attendees think that gathering accurate and comprehensive data is a challenge.
The attendees strongly agree that Big Data offers significant opportunities to
enhance FM processes.
The attendees underlined that Big Data would require considerable IT investments
and IT expertise.
It was discussed that the scope of this investment should have covered the
technology, business process and human resources aspects to Big Data.
The target should be to obtain a real value from Big Data.
Despite the considerable IT investment, the attendees questioned and were
somewhat suspicious of the reliability of the outcomes from Big Data (What if Big
Data conclusions are wrong or irrelevant?).
The need for a tool or a system for the measurement of the return of investment on
Big Data in FM was underlined.
The statement Big Data will only be valuable when all parties and stakeholders are
open and trusting enough to share data was strongly agreed.
The attendees found an imbalance between data collection and data analysis. Large
amount of data can be collected in various ways; however, the analysis of this data
for value is lagging behind.
Large organisations in the FM sector can lead the way for Big Data and take their
supply chains with them.
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Data sharing among organisations is a problem. The current contractor–
subcontractor relationship structure can be a barrier for Big Data.
Some ethical concerns and data privacy issues were discussed.
The benefits of the synergy between Big Data and FM should be better illustrated to
the industry with case studies, industry groups and best practices.
The positive relationship between FM and visualisation technologies was affirmed.
The participants were also asked to evaluate some Big Data-related statements based on a
Likert scale, ranging from “Strongly Agree”to “Strongly Disagree”.Theparticipants
strongly agreed that gathering accurate and comprehensive data within the FM sector is a
significant challenge. On the other hand, according to the participants, alongside
visualisation technologies, Big Data offers many opportunities for the FM sector. The
participants also thought that Big Data will only be valuable when all parties are open and
trusting enough to share data; even so, it will still require significant IT source and expertise.
The findings also highlighted the current imbalance between data gathering and data
analysis inthe FM sector. The complete survey findings can be seen in Figure 11.
Discussion and conclusion
This paper takes an overall look at the data mining and Big Data concepts with an emphasis
on FM in the AEC industry, which was found missing in the current literature. First, the
identified BA opportunities for the industry to empirically record their benefits or challenges
can be applied in future research efforts. Second, further discussions on how to overcome
those identified barriers, some of which are more generic and some of which are more AEC
specific, are needed from an AEC industry perspective. Third, this paper focuses more on
the FM side of BA. Thus, more in-depth analysis on the use of data mining and Big Data for
Figure 11.
Evaluation of Big
Data in FM
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the others phases in the asset life cycle, such as design and construction, can be addressed in
future research.
The AEC industry essentially operates in a data-rich business context and those
business data are becoming bigger. Therefore, with developing technologies and analytical
techniques, data mining and Big Data opportunities hold great potentials to add value to the
industry’s asset life cycle (end products/”smart”assets) and business processes. In many
other industries, those BA concepts have already become a part of their core business efforts
with recorded benefits. By shifting from the current-intuitive decision-making culture to
data-driven decision-making, to better exploit the opportunities, those BA concepts should
be truly perceived as business competencies by the AEC industry. However, from the
findings in this paper, it can be argued that the value in data analytics and Big Data is
perceived by the industry, yet the industry needs guidance and leadership for an established
data-driven decision-making culture. Another important finding is the recognition of the
imbalance between data capturing and data analytics, which indicates that the industry
needs to make a better use of their data in hand. Also, there is a degree of scepticism as to
whether the necessary investment for Big Data, both technology-wise and training-wise, can
be justified through any significant return on investment. For those concepts to be better
utilised in the industry’s day-to-day business operations, end products and decision-
makings and to overcome those doubts, they should be supported by benchmarking efforts
from other industries, best practice examples, knowledge-transfer partnerships between
academia and the industry, community of practice organisations as the concepts’
champions, awareness increasing events and publications and governmental guidance and
impetus. These efforts are particularly relevant to disseminating Big Data, as an emerging
concept, in FM. Some challenges captured for Big Data are also very much in-line with the
generic issues identified from the literature on data management in the AEC sector, such as
fragmented data structures, skill shortages and the lack of the business case for the
necessary Big Data investment. However, the study also revealed some more Big Data-
specific challenges, such as the need for parallel computational processing and ethical and
legislative issues.
As for the ICT side, large vendors’developing AEC industry-focused analytics solutions
and better interoperability among different vendors will contribute to the BA culture in the
industry. Also, those vendors can direct their attention more to the AEC industry with
publications, workshops and training events to raise awareness and clarify confusions.
The general concerns for Big Data analytics (i.e. data privacy, data protection, data bias,
etc). mostly apply to the AEC industry as well. Additionally, however, the industry suffers
from a structural fragmentation with many small-sized companies operating in its supply
chains. The data generally come from those smaller fragments. This fragmentation and the
industry characteristics can lead to a significant barrier before an extensive penetration of
BA. In this regard, as underlined in the findings, the leadership of large organisations and
clients in the industry can be of vital importance in unifying those data resources and
promoting a data-sharing culture among their supply chains. Expecting small-medium sized
companies to embark on extensive BA efforts in short-term does not seem viable. However,
some sort of a mutually beneficial data analytics alliance between large organisations and
smaller organisations in the industry, in which the analytics from the larger organisations
on the data provided by the smaller organisations, can be formed.
In parallel with the data fragmentation issue, BIM can present a collaborative, unified
data repository for data sharing in the AEC industry. As the use of BIM has been extending
through the asset life cycle, from design to demolition, the integration of data analytics with
BIM models can yield valuable business insights and help to overcome the fragmentation
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problem. Also, BIM models can present a data visualisation platform for data mining and
Big Data analytics. Nevertheless, it should be underlined that this integration will bring
along further complexities in terms of ICT integration and compatibility, business analytics
and training needs for the industry’s professionals. Although the BIM concept presents
many data manipulation opportunities (e.g. simulation, ERP integration, automated safety
and regulatory code checks, etc.), those opportunities also considerably increase the amount
of data the industry needs to cope with.
Similar to the government’s BIM mandate (i.e. BIM Level 2 to be achieved in public
projects by 2016), which has helped the BIM technology to disseminate in the AEC
industry in the UK, the government can also gradually lead the direction to data mining
and Big Data in the AEC industry. This will contribute to the industry’s catching-up with
other industries in terms of BA. Also, the data protection act may need to be discussed,
reviewed and modified in cooperation with industry bodies to allow BA to disseminate
further in the AEC industry.
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Corresponding author
Vian Ahmed can be contacted at: V.Ahmed@Salford.ac.uk
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