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The Era of Big Data and Path towards Sustainability
Vijay Victor, Szent István University, Gödöll, Hungary, vjvictor@gmail.com
Dr. Fekete Farkas Maria, Gödöll, Hungary, Farkasne.Fekete.Maria@gtk.szie.hu
"Give me a little data and I’ll tell you a little. Give me a lot of data and I’ll save the
world."
- Darrell Smith, Director of Facilities and Energy
Microsoft
Abstract
This paper is an attempt to throw light on the applications of big data analytics in nurturing
sustainable develoment through a descriptive metadata study. Big data is widely used in many areas
such as hospitality, transporataion, health, governance, e-commerce etc. Common applications of big
data include consumer profiling, personalised pricing, marketing, advertising and predictive analysis.
One of the formidable challanges confronted by the businesses in the contemporary period is to
reconcile the conflicting interests of profit maximisation and fostering sustainability. The
unprecedented magnitude of data generated within the organisations do have the potential to bring
gainful insights for efficient resource utilisation and waste minimisation. The advent of big data aids
in making these conflicting interests complementary by providing efficient and precise predicitions.
A number of studies are going on to explore possible options avialable to leverage big data analytics
to create social and environmental value. Novel analytical approaches, enormous amounts of data and
new technology would help in gaining insights to frame more agile and efficient policies to protect
the environment. This paper discusses how big data is applied in different areas to foster
sustainability.
Keywords
: Big data, Sustainability, Gartner’s model
Introduction
According to Brundtland Commission Report, the term sustainability can be defined as "meeting the
needs of the present without compromising the ability of future generations to meet their own needs."
The foundation of sustainability is laid on four catenated aspects namely social, cultural,
environmental and economic aspects. The social aspect of sustainability asserts the need to treat
ourselves as well others with fairness and respect. The cultural aspect acknowledges the need to
nourish and share the diverse attitudes and values. Environmental sustainability is about protecting
the biophysical system that maintains and nurtures life on earth. Finally, economic sustainability
asserts the need to use available resources in the most efficient way to make products and services
which could add value to the lives of human beings. The socio economic systems in the
contemporary period have their foundations laid on the notions of linear economy which follows the
practice of use and dispose. The high rate of depletion of the resources and growing environmental
issues call forth the need of circular economic models to replace the former. Circular economy can be
defined as ’a system which is regenerative and restorative by design and intention’. In the recent
decades, the business models evolved inculded business innovations with environmental concerns.
Big data analytics is a major breakthrough in business innovation in the present century. This paper
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studies the implications of big data with special regard to the corporate, socio-economic and
environmental aspects of sustainability.
Although the term big data is relatively novel, collecting large amount of data for the purpose of
analysis is practiced from old times. As per dictionary, the definition of big data can be quoted as
’extremely large data sets that may be analysed computationally to reveal patterns, trends, and
associations, especially relating to human behaviour and interactions.’ The process of analysing big
data to figure out underlying patterns and correlations, trends, preferences etc which would be helpful
for organisations to make more efficient and informed business decisions is known as big data
analytics.
One of the most commonly accepted definitions of big data is in terms of its three charactersitic
features. The 3 V’s i.e. Volume, Velocity and Variety. Volume here means the size or amount of
data; usually in terms of petabytes, exabytes and terabytes. velocity implies the speed of the data
gathered and variety portrays the heterogenous nature of the data. (Russom, 2011) (Edosio, 2014).
Additionaly, the term Veracity can be added to the definition which shows the property of precision
or conformity of the data to facts (Abbasi, Sarker, & Chiang, 2016). With the rapid growth in
technology, the effiecieny in generating and transferring information has improved from 0.3 exabytes
in 1986 to 65 exabytes in 2007 (Manyika et al., 2011). Many of the companies use huge amount of
information to make business decisions more efiicient and profitable. For example, the retailer giant
Walmart used about 2.5 Petabytes of customer related data in 2012 to modify their pricing strategies.
In the competitive world, the notions of improving speed and efficiency are associated with cost
minimisation strategies. Big data analytics offers the possibility to make quick and precise business
decisions thereby giving a competitive edge to the firms which they havent made use before. This
helps in cost reduction, making better decsions and in developing new products and services. Today,
many important sectors including healthcare, travel and hospitality, government, retail etc make use
of big data analytics for a wide variety of purposes. Collecting huge and heterogenous data regarding
a particular phonemenon makes sense when it is able to bring insights which is unimaginable with
small pieces of data.
One of the significant features of big data is that it brings the entire population under study for
analysis hence it overcomes the constraints of traditional sampling methods. This enables to include
heterogeneous perspectives, generate precise output and predict things with more certainity (Varian,
2014). Big data analytics offer a challenging context to researchers who follow the traditional method
of associating reults with errstwhile theories. In fact we require more theoretical ideas to figure out
the sophisticated reality portrayed by big data in most cases.
Everything which rely on technology is now impacted by big data analytics. Sustainibility concerns
are no different from this. Many governments have already started to look into the applications of big
data analytics to foster sustainable development in the smart cities around the world. Some of the
potential areas of application of big data analytics include governance, resilience, quality of life,
efficient utilisation of available natural resources and city facilities etc. (Khan et.al, 2013) Big data
analytics will be helpful to have a better diagnosis of the current issues and will assist in fostering
environmental sustainability.
Prospects Of Data Driven Sustainibility In The Corporate Sector
The growth of circular economy in the modern era is accelerated by digitalisation. The shift from
traditional "Take, Make, Waste" mode of production to "Recycle, Reuse, Restore" is happening now.
How can companies manage realising their profit maximsiation goals by fostering sustainability? The
answer is big data.
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Big data and sustainibility within the organisations
Businesses have now recognised the fact that effective corporate sustainability can be made into
practice only by understanding the outcomes of the impacts of business world and natural world on
each other. The complicated nature of interaction between natural and business worlds throw light
into potential areas for the application of big data analytics. Till last decade, businesses were
struggling to figure out the full picture of impacts of their own operations on environment. With the
advent of big data, they get access to multiple datasets combining wide variety of information which
could be used to improve their performance efficiency and to fulfill the sustainability goals as well
(John Hsu, 2014).
Profit is the enticing incentive that drives the business world. Unfortunately, it does not always
complement environmental progress. Big data has the potential to find solutions for some of the
troublesome problems faced by enterprises in the modern era. Businesses are now trying to integrate
sustainibility concerns in their business plans and strategies. With big data analytics, they can explore
different options available to them to maximise profits by protecting environment. This is more
significant in cases of big companies which are responsible for most of the environmental
degradation as they can be more responsible in their own operations thereby nurturing sustainability.
The scope of big data in ensuring sustainability is wide. Within an organisation, the applications of
big data starts from intra organisational operations. The application of smart sensors optimise the
systems to maximum efficiency and deliver the necessary components at times when and where they
are needed.
Most important application of big data with regard to businesses is that it provides more realistic and
reliable sales forecasts which would help to minimise the wastage of energy and resources and to
make the efficient utilisation of available stocks. Big data can also help to ensure environmental and
economic viability in the supply chain by establishing most efficient transit between raw material
allocation, production areas, warehouses and customers. In the same way, the movement path of
forklifts and other vehicles can be timely updated in the most efficient way through the real time
monitoring of stock despatches therby minimising fuel and energy consumption. (Nancy Master,
2017)
Big data and sharing economy
The concept of ’collaborative consumption’ is gaining momentum over the past few decades. Many
platforms like Uber, BlaBla Car, Ola Cars, Airbnb etc work based on the above concept and have
proved to be succesful. Multiple sources of data with similar interests are collected and utilised by the
companies in such a way to frame sustainable business plans to maximise profits. Dror E., & J.
Alberto (2016). The concept of collaborative consumption or sharing economy is an eco friendly one.
By reducing the number of cars on roads, It helps in limiting the carbon footprint. Big data made it
possible to apply the concept of collaborative consumption in the most efficient and profitable way.
Even the pricing strategy of these companies are based on big data. The real time data of the demand
for hiring cars and the availability of cars are used to update the prices at regular intervals with the
help of a complex algorithm.
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Big Data And The Socio-Economic Aspects Of Sustainability
Big data and Sustainable Development goals
The United Nations in its report UN Global Pulse published in 2012, explains how big data can be
used to measure the progess of the attainment of sustainable development goals. This implies that big
data has a vital role to play in improving the welfare of common people. Some examples of how big
data is used in this regard are as follows. To assess poverty levels in countries, spending pattern of
the people on mobile phone services are taken into account. Spending on mobile phone services can
provide proxy indicators of income levels, thereby getting an idea about the level of income of the
people in the country. Hunger index of countries can be assessed by tracking food prices online. It
helps to monitor food security in real time.
There are 17 sustainable development goals. Specific parameters and indicators have been developed
for measuring the progress of these goals. The enormous amount of data pertaining to each parameter
is then assessed to make policy decisions. One of the important constraints in this regard is that many
of the governments still do not have access to adequate data regarding the entire population. UN
Global Pulse (2012).
Big data and environmental aspects of sutainability
Monitoring and assessing environmental situations
One of the most important applications of big data analytics is to assess potential environmental
threats and risks and monitoring real environmental situations. Governments or non governmental
agencies can use the real time satellite imageries, drone assistance and sensors placed in the strategic
and sensitive areas to spot deforestation and other such environmental issues. There are a number of
tools which monitors and assess different environmental risks such as water risks, pollution levels etc
using a large number of indicators and parameters which consist of a large quantum of data.
Aqueduct is one such water risk mapping tool. This tool assesses the potential water risk anywhere in
the world based on various parameters. There are digital platforms based on big data dedicated for
agricultural tracking. A cloud based app named Farmeron provides platform to manage and track the
data of cattle, including health, milk production, reproduction, diet etc. By tracking the real time data
of the livestocks, farmers get necessary information for efficient management of the resources. IW
Financial Staff (2016)
Big data also aids in making agricultural practices more efficient. Automated management of
irrigation system and manure application based on a number of parameters such as weather forecast,
soil conditions, temperature and moisture levels, expected time of harvest etc is now possible using
big data analytics. Dror E., & J. Alberto (2016)
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Figure 1:
Big data analytics for environmental situations bas
The model given above is based on Gartner’
been brought forth to the original model to fit the
Basically, data analysis is the
process
happen, what will happen and how can we make it hap
culminates in prescriptive analytics.
model
which is conducted subsequently to explore options
Wi
th regard to big data and sustainability, the occur
human interventions
could be studied and effective solutions can be tak
have be
en taken to address the most discussed issues of gl
availability of huge amount of historical and real
to foster sustainable development
The Pre-emptive analysis is v
ery significant in this re
with the problems
which may come up in future
technical expertise is required to infer insights f
programming languages developed for this purpose.
the programming languauges used to handle the eno
Risks and Challanges
There are a number of challenges to overcome both i
big data for a sutainable future. Significant among
The macro level challenges are the di
been realsied that in order to use big data to fost
data representing socio-
economic and environmental conditions of countries
collected
(ICSU, 2015). The less economically developed coun
necessary technology and infrastructural facilities
divide among the nations in pursuit of th
Big data analytics for environmental situations based on Gartner’s model
The model given above is based on Gartner’
s model of stages of data analysis.
Slight changes have
been brought forth to the original model to fit the present study.
process
of finding
answers to the questions what happened, why did it
happen, what will happen and how can we make it happen. It begins
with a
descriptive analytics and
culminates in prescriptive analytics.
(Taras Kaduk, 2016) Pre-
emptive analytics is an add on
which is conducted subsequently to explore options available for prevention.
th regard to big data and sustainability, the occurence of various environmental phonemona
could be studied and effective solutions can be taken to
address the
en taken to address the most discussed issues of global warming, pollution etc.
availability of huge amount of historical and real time data,
it is now possible to use
big data analytics
and to deal with the
current and potential environmental situations.
ery significant in this re
gard as it would help to frame policies to deal
which may come up in future
associated with the phenomenon.
High level of
technical expertise is required to infer insights from big data. There are many sophisticated
programming languages developed for this purpose.
R programming, Python, SAS etc are some of
the programming languauges used to handle the eno
rmous amounts of data.
There are a number of challenges to overcome both in micro and macro level to make the most use of
big data for a sutainable future. Significant among them are outlined here.
The macro level challenges are the di
fficulties faced in dealing wit
h big data in the global level.
been realsied that in order to use big data to foster sustainability, a diverse set of data is require
economic and environmental conditions of countries is
required to be
(ICSU, 2015). The less economically developed countries are still not equipped with the
necessary technology and infrastructural facilities for data driven decsion making. This has made a
divide among the nations in pursuit of th
e global goals.
ed on Gartner’s model
Slight changes have
answers to the questions what happened, why did it
descriptive analytics and
emptive analytics is an add on
to the
ence of various environmental phonemona
due to
address the
m. Efforts
obal warming, pollution etc.
With the
big data analytics
current and potential environmental situations.
gard as it would help to frame policies to deal
High level of
rom big data. There are many sophisticated
R programming, Python, SAS etc are some of
n micro and macro level to make the most use of
h big data in the global level.
It has
er sustainability, a diverse set of data is required. The
required to be
tries are still not equipped with the
for data driven decsion making. This has made a
Innovation Management and Education Excellence through Vision 2020
565
The prospects of data driven sustainability depends highly on the potential of the international
community to muster the data revolution in the desired path. This requires the integration of local,
national and international schemes. But in the first place, the challenges like lack of proper
technology, infrastructural facilities and capacilty needs are to be addressed (Charles et.al, 2016)
The micro level challenges are the difficulties pertaining in the company level. One of the most
difficult challenges faced by the companies is with regard to storing and analysing the enormous
amounts of data generated on a daily basis. Most of the data is unstructured in nature, which means
that they are in the forms of photos, documents, audio-video files etc. Moreover, storing petabytes of
data on a daily basis requires the adoption of most modern technology and related infrastructural
facilities which indeed is a costly affair. Big data analytics requires special expertise. In order to
generate insights from the data, companies have to hire professionals who are big data experts. Talent
shortage in this field is very accute. Another most discussed issues in this regard is the privacy and
security of the data donors. If the identity of data donors could be leaked in some ways, it is a serious
privacy threat. Anonymity of the data donors should be ensured throughout all stages of data analysis.
Big data going to the wrong hands is also a major secuirty threat as it can be abused for personal
interests.
Conclusion
From the study, it could be summarised that the companies are using big data to frame business
strategies which foster environmental sustainability. The pro environmental attitude of people has
forced the companies to shift from the traditional methods of operation that are harmful to the
environment to eco friendly methods which have higher application costs. With cut throat
competition and sustainibility issues, big data provides the most efficient business strategies which
merge environmental, cost and profitability concerns of the enterprises. The application of big data
analytics to assess the progress of sustainable development goals and in predicting hazardous
environmental situations give us the hope that a more people centered or more preceisely,
environment centered big data revolution is about to happen soon. It could be also aspired that the
business world would shape and leverage the emerging big data eco system in such ways to promote
the welfare of people with special consideration to the vulnerable segments in societies. Special
emphasis should be given in embarking on regional open data platforms which would manage the
issue of mobilising data for attaining global goals. More studies should be undertaken to figure out
the ways to prevent abuse of big data.
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