A Comparison of Smart City Development and Big Data Analytics
Adoption Approaches
Zohreh Pourzolfaghar
1
, Christian Bremser
2
, Markus Helfert
1
and Gunther Piller
2
1
School of Computing, Dublin City University, Dublin, Ireland
2
Business Information Technology, University of Applied Science Mainz, Mainz, Germany
Keywords: Smart City, Information Communication Technology, Big Data Analytics, Technology Adoption.
Abstract: This paper intends to elucidate the similarities between smart city development and big data analytics
adoption. Both concepts promise new opportunities: smart cities to improve citizens’ life quality and big data
analytics to drive companies towards the competitive edge. Consequently, the number of organisational big
data initiatives and efforts to implement smart city concepts are increasing. In the context of big data analytics
adoption, it could be shown that there are two distinct approaches companies follow. They either focus on the
search for potential use cases or on the development of a technology infrastructure. Based on a comparison
of various smart city and big data analytics use cases, this paper discloses that both of these approaches either
concentrate on developing new service development or providing the required infrastructures for future
services.
1 INTRODUCTION
A smart city is an innovative city that uses ICT to
improve citizens’ quality of life and efficiency of the
urban services (Booch 2010; ITU-T FG-SSC 2014;
Anthopoulos and Janssen 2016). To achieve this goal,
the produced information in various city systems are
combined to provide effective services. This
information is produced by variety of sources, e.g. by
sensors and internet of things (IoT) devices installed
in the buildings, streets, vehicles and so on. Based on
the definition by Pourzolfaghar and Helfert (2017a),
smart services should have a goal in line with the
ultimate goal for smart cities, to facilitate daily
activities of citizens and improving their quality of
life. To achieve this goal, governments are working
in a higher level to provide the infrastructures. Also
as Ryazanova and Pétercsák (2016) stated, private
companies use the produced data by the devices in the
infrastructure level to develop the services in smart
cities. As these researchers stated, cooperation
between public and private sectors in smart cities
leads to develop smart zones in the cities. However,
there are some difficulties to take fully advantage
from this type of collaborations. By increasing
number of the installed IoT devices and sensors,
smart cities are facing difficulties in terms of
heterogeneity of the stored data from various sources.
For instance, Pourzolfaghar and Helfert (2017b) have
introduced this issue as one of the barriers to take
advantage from integration of information from
different sources in buildings, to provide useful
services by facility management companies.
Big data analytics is experiencing a situation
similar to the smart city development. In the era of
advancing digitisation of whole industries, the
potential benefits and challenges associated with big
data are important topics for companies. Big data
promises new data-driven services to improve
processes and enable innovative products and
business models (Sivarajah, 2017). Therefore,
growing number of enterprises focus their
investments on the adoption of big data. Against this
backdrop, Bremser et al. (2017) investigated the
process how companies examine the possibilities of
big data. Based on a multiple case study, they found
two generic approaches that are pursued by
organisations. These approaches focus either on the
search for potential business opportunities or on the
need to develop technology infrastructure. This paper
examines, whether similar approaches can be
identified in smart cities too.
The paper is structured as follows: In the next
section, we introduce big data analytics and big data
adoption approaches. This will be followed by
Pourzolfaghar, Z., Bremser, C., Helfert, M. and Piller, G.
A Comparison of Smart City Development and Big Data Analytics Adoption Approaches.
DOI: 10.5220/0006674501570164
In Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2018), pages 157-164
ISBN: 978-989-758-292-9
Copyright
c
2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
157
examples how organisations use these approaches to
introduce big data analytics. In the following sections
smart cities use cases are introduced to describe how
smart cities are developed. In the discussion section
we will compare the use cases from both domains to
identify the similarities between the adoption of big
data analytics and the development of smart cities.
2 BIG DATA ANALYTICS IN
ENTERPRISES
The TechAmerica Foundation defines big data as
(TechAmerica 2012) “a term that describes large
volumes of high velocity, complex and variable data
that require advanced techniques and technologies to
enable the capture, storage, distribution,
management, and analysis of the information.” In
accordance with many other definitions, they use
volume, velocity and variety to describe the
challenges in data management. However, the
literature shows that there are ambiguities about
limits on the three Vs (Mikalef et al., 2017). These
depend on size, industry and location of a company
and result in a “three-V tipping point”. Beyond this
tipping point traditional data management and
analysis technologies become inadequate for deriving
intelligence within a sufficient period of time.
Therefore, this tipping point poses a threshold
beyond which firms start dealing with big data and
examine the value of new technologies (e.g. complex
event processing, in-memory data processing,
NoSQL databases) compared with their present
implementations (Gandomi and Haider 2015).
However, big data is meaningless without analysis.
Its potential value can only be leveraged, when
companies extract meaningful insights from big data.
Therefore, new processes, tools and methods are
required that capture, analyse, and visualise the
underlying patterns in the data (Mikalef et al., 2017).
Hence, big data analytics refers to multiple
techniques (e.g. text analytics, social media analytics)
that are used to acquire intelligence from big data
(Gandomi and Haider 2015). Companies want to take
full advantage of these new opportunities and explore
the possibilities of big data analytics. In this regard
Bremser et al. (2017) has shown that there are two
distinct approaches companies follow to examine the
potentials of big data:
In the approach Business First, enterprises explore
big data potentials entirely from a business
perspective. They search for use cases with high
expected business value. These use cases span from
possible improvements of existing processes to
entirely new business services or business models.
Typically, these use cases are developed in cloud
environments as prototypes. In order to evaluate the
assumptions regarding business case, the prototypes
are tested in market segments with company-friendly
customers.
Organisations following a Platform Building
approach initially focus on an identification of key
activities for the development of a future-oriented big
data platform and not on the search for particular
application scenarios. Their goal is to provide a
technological starting point and integrate all existing
and company relevant data sources. Specific
application scenarios do not yet exist, but are
expected to come up eventually.
In the following subsections, we will introduce
four examples, how companies approached big data
analytics. The examples have been adapted from the
multiple case study of Bremser et al. (2017). The first
two cases illuminate successfully identified business
opportunities and emphasise how companies create
new business services through the application of big
data analytics. These examples will be followed by
cases where companies introduce new technologies in
order to enable themselves for future big data
analytics use cases.
2.1 Big Data Analytics - Business First
2.1.1 Predictive Analytics in Utilities
A deregulated market and the energy transition cause
uncertainties in the business of utilities companies.
Driven by this development, the top management of
one of the companies of the multiple case study of
Bremser et al. (2017) set up a digital IT unit to focus
on innovative and data-driven topics. One of the
identified big data use cases in this company is
predictive maintenance. Predictive maintenance
promises to prevent cost-intensive machine failures.
For this purpose, the investigated company starts to
use sensor data from production plants, machines and
other operating equipment. These sensors are
producing data about the conditions of the machines,
for example, voltage, temperature, rotation and
vibration. Through the continuous generation of
machine data, the processing of these large volumes
and fast-moving data is required. In order to carry out
first analyses, data is temporarily collected within a
cloud storage. They are then analysed by data
scientists for usage and fault patterns. Typically,
machine learning methodologies are used for these
studies (Susto et al., 2015).
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Observed patterns help to identify low quality
components and to monitor wear and condition of
machines in real time. Third party data, such as
weather data and data on other possible
environmental influence factors are also integrated
into the analysis to improve the prediction of possible
breakdowns. In order to validate possible predictions,
the company captures the uptime of various
machines. If the application of an algorithm leads to
a decreasing downtime, the algorithm is rolled out to
other machines and production sites.
After a successful validation, also the IT
infrastructure needs to be adapted to carry out the new
analyses in a stable and efficient manner. For this
purpose the company in question plans to integrate a
lambda architectures into their existing IT landscape.
This consists out of a speed layer for real time data
processing and a batch layer for handling large data
volumes (Kiran, 2015).
2.1.2 Demand Forecasting in Retail Trade
Right product in right places at right time is a key
challenge every retail trader is confronted with. The
triangulation of data from different sources, like
enterprise resource planning (ERP), supply chain and
web, allows retailers to predict customer trends and
needs. In the past, historical sales data have only been
used to determine future consumption. Current
research shows that anonymised online search data is
an accurate proxy for customer activities (LaRivier,
2016).
In addition, product characteristics, relationships
with other products and external information like
competitor prices or weather changes gain
importance in the identification of demand anomalies.
In order to analyse data from those different sources,
analyses of time series, multiple linear regressions or
machine learning methods like support vector
machines or artificial neural networks are used
(Carbonneau et al., 2007).The company from the
case study set up a data lab in order to explore and
evaluate the potential of demand forecasting. There,
data scientists combine data from potentially useful
data sources and develop algorithm which are tested
in single stores. If these tests succeed, the developed
algorithm will be integrated in the overall stock
replenishment system.
2.2 Big Data Analytics - Platform
Building
2.2.1 Data Lake Development in Pharma
In recent years pharmaceutical companies have
experienced a rapid increase of production and
customer data. Now available data sources reach from
anonymised electronic health records of patients to
sensor data from the production sites. These data
sources have different characteristics regarding
growth rate, structure and privacy requirements. In
order to gain valuable insights from these different
data, they need to be properly integrated. Therefore,
a company in the case study implemented a so called
data lake. Data lakes provide a scalable and schema-
less repository for raw data and provide an interface
to access the stored data (O’Leary, 2014).
Typically, a data lake is a complex ecosystem of
different technologies to handle the challenges of the
three Vs (volume, velocity, variety) (Hai et al., 2016).
To meet privacy issues and treat data appropriately,
non-technical issues have also to be considered, like
master data management and data governance
(O’Leary, 2014). Therefore, also organisational
changes are necessary, like the creation of new data
related roles in IT (e.g. data steward, data engineer)
or the adoption of policies guiding the responsible use
of data.
This shows that the introduction of a data lake
concept requires far-reaching organisational changes
and can only be carried out by large organisational
projects. The pharmaceutical company of the case
study hopes, that the implemented data lake will serve
a broad range of future big data use cases of the
company. Typical examples are waste reduction in
drug production or individualisation of medication.
2.2.2 Introduction of Hadoop in Banking
Thousands of movements on millions of bank
accounts generate tons of data points. In order to meet
regulatory requirements, these account movements
need to be archived and easily accessible. In the past,
the data were archived on disk robots, but the
processing limits of these systems are exhausted and
the extension of disk space is costly. Beside these
challenges in data management, fintechs and a
changing customer expectation forces banks to deal
with the possibilities of big data. At the same time,
regulatory pressure burdens financial resources.
Against this backdrop, the investigated financial
institution started to search for opportunities to
replace existing IT components cost-neutrally with
A Comparison of Smart City Development and Big Data Analytics Adoption Approaches
159
big data technologies. To cope with the demands in
data management, the considered bank replaces
existing databases with Hadoop clusters. For this
purpose, traditional projects are carried out to
introduce the new technology and migrate the data to
the news systems. In addition, the introduction of
Hadoop offers opportunities to integrate further data
sources and provides a technological starting point for
the adoption of future big data use cases.
3 SMART CITY DEVELOPMENT
Smart cities are innovative cities which uses
information communication technologies to improve
citizens’ quality of life. The ultimate goal of smart
cities, meaning improving quality of life, is realised
through the provided services. In other word, services
in smart cities are responsible to facilitate daily
activities for the citizens. To achieve this goal, many
devices and sensors have been developed to collect
useful information to provide services. For instance,
traffic cameras are collecting real time information
about traffic status in different areas of the city. This
status is reported through the live radio programs and
assists the drivers to choose a better route to prevent
congestion.
Later, statistical traffic information can be used to
optimise transport services based on the real traffic
information. Similarly, useful information is
produced by IoT devices and sensors in smart
buildings. This information can be utilised by various
industries, e.g. facility management or utility
companies, to provide more effective services, e.g.
energy saving alarms, real time repairmen services.
Apparently, a huge amount of data is available and
should be considered to continuously improve smart
cities.
The produced information in various smart
systems (e.g. healthcare system, education systems,
smart buildings and etc.), can also be utilised to
develop smart services through combination. As an
example, the shortest time for an emergency case is
calculated by combining data from transport systems
about congestion, and data on the availability of
ambulances in the vicinity to an accident. Seemingly,
different types of information sources are utilised to
expand smartness in the cities. In the following
subsections we will introduce five use cases to
provide more insight into the different approaches
taken by smart cities.
3.1 Smart Services in Smart Cities
3.1.1 Smart Services in Facility
Management Industry
Facility management (FM) industry is responsible for
providing and delivering timely, professional
analysis, and consulting support services for the
customers (Rondeau et al., 2012). Nowadays many
buildings are equipped with smart devices, sensors
and cameras to provide more comfort environment
for the inhabitants. All these installed devices are
producing real time information to be utilised by
different industries like facility management for
various purposes, e.g. for energy management,
security management and etc. As an instance,
Pourzolfaghar and Helfert (2017b), illustrated how
the produced information by smart devices in
buildings assist FM department to achieve more
efficient energy management. According to their
research, the plan for efficiently use of energy was
related to the booked meeting room.
Based on the booking system for meeting rooms,
the heating systems are turning on at the booked time.
At the same time, a control system is working to look
after efficient energy usage. This system works based
on the received information from the motion
detection sensors, cameras and any other smart
devices installed in the room. In case that no
movement is detected in a given time, the heating
system will be switched off automatically. In this way
energy consumption will be managed in an efficient
way. Apparently, huge amount of the real time data is
produced by sensor and smart devices in the
buildings. However, the produced data is stored in
heterogeneous storages. To make the buildings there
is need to integrate the produced data from various
sources to provide useful services to the buildings
inhabitants.
3.1.2 Urban Planning in Smart Cities
The energy usage of buildings can be utilised for
more effective urban planning in smart cities. In this
regard Rathore et al. (2016) has provided some
examples on how the collected data from various
sources, e.g. smart homes, may contribute to the
development of smarter cities. They believed that
enabling smart cities give benefits to the government
authorities and the citizens. For instance, Rathore et
al. (2016) explained that the citizens may save car
fuel by efficiently managing the route to reach the
destination, as well as protecting themselves from
environmental pollution. Likewise, Pourzolfaghar
SMARTGREENS 2018 - 7th International Conference on Smart Cities and Green ICT Systems
160
and Helfert (2016) emphasised that how energy
consumption is utilised for more efficient urban
planning. As an example, water consumption for all
the buildings in an urban area along with weather
forecast and rainfall information is essential to predict
possibility of flood occurrence. Consequently, this
information is useable for flood management in rainy
seasons. The water consumption information in the
buildings are produced by smart meters and their
changes can be tracked by daily manner.
3.1.3 Smart Commerce
Smart commerce is an approach in which customer
concerns is placed in the centre of the business. Yan
et al. (2010) elucidated that retailers could always
gain profit from having knowledge about customers’
needs and willingness to buy. They also emphasised
on the forecast information accuracy effect on the
profit of traditional and online retailers. The smart
devices with embedded sensors can provide
information this type of information about the
products for the related market. As such, this
information is of interest of the companies which
produce these devices for real time demand
management. By obtaining the produced data by the
embedded sensors in smart devices the companies can
provide on time services to the customers. Moreover,
based on the collected data about faulty devices, there
will be a chance to use statistical information to
improve the quality of their products. Moreover, the
received information about more demand for a
product in an urban area and crowd movement in that
urban area are important for market analyse and the
potential profits from a establishing a new service
centre in the area.
3.2 Infrastructure Development in
Smart Cities
3.2.1 Smart City Technologies in
Dublin-Dockland
Dublin Docklands Strategic Development Zone is an
extension of Dublin city centre to combine different
communities. Ryazanova et al. (2016) conducted an
exploratory research to specify the perception of
value creation and value capture among the
stakeholders. Based on the finding from their
research, it has been realised that the smart
technologies are similarly discussed across the public
and private sectors in terms of infrastructure needs in
the keeping with their responsibilities for the
development of this smart zone. As Ryazanova et al.
(2016) reported, public sector is mostly concentrated
on hard infrastructure such as bridges and transport
provision, including electric vehicles and cycling. At
the same time private sectors work on wired and
wireless networking infrastructure and the test-
bedding of sensors and software systems. As they
believe large corporations had the capacity to invest
in sensing networks with potential environmental
benefits such as flooding and air pollution sensors.
Clearly, plenty of devices in this project are
producing various datasets which are supposed to be
utilised to provide smart services in Dublin Dockland
smart zone. One of the potential difficulties which can
be predicted is the interoperability issues.
3.2.2 Smart City Infrastructure
Development in the River City
River city is a city with the intention to be smart based
on the drawn vision for it. According to this decision,
the authorities in this city decided to work on the
architecture to develop a data pool. In this way, they
will have efficient control on the provided data by
various sources. As an example, Pourzolfaghar and
Helfert (2017a) explored the footfall counter service
to find out the benefits of this service to the smart city
stakeholders. Based on their findings, the main aim of
implementing the required infrastructure for the
footfall counter is associated with their strategic
decision to transform the city to a smart city. One of
the recognised difficulties related to this project is the
inability to control the produces data by the devices.
To achieve the strategic goals, the authorities of the
River city are working to develop an architecture to
obtain the ability to control the produced data by the
implemented infrastructures.
4 DISCUSSION
According to Bremser et al. (2017), the main aim of
big data analytics is to provide data-driven services
that improve processes and enable innovative
products and business models. For this purpose, two
distinct approaches are followed to achieve big data
analytics goals. Companies focus either on the
implementation of certain big data use cases to
leverage business opportunities or on the
development of a technology infrastructure. As such
the main aim of smart cities is improvement of life
quality for the stakeholders, by providing effective
services. Referring to the explored smart city use
cases, the development of smart cities has been
approached in a similar way: either specific services
A Comparison of Smart City Development and Big Data Analytics Adoption Approaches
161
are implemented or infrastructure components are
established. To clarify these similarities, the
presented use cases in both areas are compared from
a use case driven and a platform perspective. For this
comparison we list as dimensions: aim of activities,
approach, data usage and infrastructure. A summary
is presented in tables 1 and 2.
Table 1 shows the comparison of use case driven
initiatives. Here, both concepts aim on the
provisioning of new data-driven services in order to
improve business processes or city management
services respectively. In big data analytics,
companies have already implemented use case
productively, while in smart cities most of the use
cases are still on the level of proposals. Nevertheless,
in both areas the approaches are quite similar. In big
data analytics enterprises typically set up lab
environments to identify and evaluate potential use
cases with high business value. In smart cities, use
cases are identified by public and private companies
based on the available data sources and in line with
the strategic goals to increase quality of life. The
comparison of data usage and infrastructure shows
that in both concepts a wide range of different data
sources is used. These sources need to be processed
in an integrated way. Therefore, in both areas
required technologies are implemented, e.g. real time
processing for sensor data of machines.
Table 1: Comparison of use case driven initiatives.
Use case
Aim
Approach
Data usage
Infrastructure
Utilities
Optimised
asset
management
processes
Digital IT identifies new use
cases; these are developed
by data scientists in cloud
environments and validated
for single production
machines
Sensor data from
production sites & external
data sources, e.g. weather
data
Real time processing
components
Retail trade
Improved
supply chain
processes
Lab environment is used to
identify use cases and
relevant data sources;
demand forecasting is
validated in single stores
ERP, supply chain data
and external data sources,
e.g. web search data,
social media data, weather
data
Real time processing
components, social
media screener,
interfaces to other
external data sources
Facility
Management
Improved
energy
management
efficiency
FM industry uses data from
buildings to develop smart
services
Motion sensors, cameras,
IoT devices
Realtime information
processing and
integration capabilities
to analyse the data
from installed smart
devices and sensors in
buildings
Urban
Planning
Flood
prediction
Government utilise data
from buildings data in an
urban area to mitigate flood
consequences
Smart meters in buildings,
weather forecast
information
Real time data
processing on water
usage and weather
forecast
Smart
Commerce
Improved
demand
management
Smart commerce uses data
from buildings to promote
their products to selected
consumers
Embedded sensors in
products and devices,
crowd movement
information
Installed smart and IoT
devices and sensors in
buildings
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162
Table 2: Comparison of platform driven initiatives.
Use case
Aim
Approach
Infrastructure
Pharma
Companywide,
centralised
data lake
Stepwise implementation of
new technologies and
integration of data sources;
carried out as part of
traditional projects
General technologies
for big data
management (e.g. real
time and batch
processing; scalable
storage) and
corresponding
interfaces to company
relevant data sources
Bank
Scalable
storage for
mass data
Implementation of Hadoop,
migration of data and
stepwise integration of
further data sources; carried
out within traditional
projects
Hadoop File System
Interfaces to existing
bank systems
Dublin
Dockland
Provide a
networked
infrastructure
Step by step integration of
networks and data into the
city infrastructure
Hard infrastructures
e.g. transport
provision, including
electric vehicles and
cycling
River City
Data pool
Step by step integration of
diverse data into the city
infrastructure
General technologies
to collect the data
various sources
Table 2 compares use cases belonging to the
platform approach. In both areas, the described
cases aim on the provisioning of general
infrastructure components. In the introduced big
data analytics use cases, the infrastructure focuses
on the creation of processing and storage
possibilities, while in the smart city examples also
data acquisition capabilities are considered. Overall,
in both areas an infrastructure is being developed
step-by-step and technologies and data sources are
integrated. Equally, in both areas, the stepwise
developed infrastructure shall serve as a
technological starting point providing data and
technologies for future use cases.
5 CONCLUSIONS
With the aim of comparison between big data
analytics adoption in enterprises and smart city
developments, this study explored and compared
use cases from both areas. We found that two
different approaches are followed in smart city
initiatives. The first approach focuses on the
development of smart services for citizens and their
implementation. The second approach centres on
the implementation of a basic infrastructure
components, e.g. embedding sensors in the city’s
infrastructure or providing a central data pool. In
this approach, governments develop and provide an
infrastructure and expect use cases to come up in
future from private companies using provisioned
data and technologies. This shows that similar
approaches are pursued in the development of smart
cities and big data analytics adoption in enterprises
Therefore, this paper concludes that the big data
adoption approaches Business First and Platform
Building are also present in the context of smart city
developments.
ACKNOWLEDGEMENTS
This work was supported by the Science Foundation
A Comparison of Smart City Development and Big Data Analytics Adoption Approaches
163
Ireland grant “13/RC/2094” and co-funded under
the European Regional Development Fund through
the Southern & Eastern Regional Operational
Programme to Lero - the Irish Software Research
Centre (www.lero.ie).
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