Big Data for Sustainable Development Goals: Theoretical Approach

FATIMA LALMI and RAFIKA BENAICHOUBA2

JP/iD in Economic Sciences, Senior Lecturer, University ofAbdelhamid Ibn Badis, Mostaganem, Algeria, E-mail: This email address is being protected from spam bots, you need Javascript enabled to view it

2PhD in Economic Sciences, Senior Lecturer, University of Djillali Bounaama, Khemis Maliana, Algeria, E-mail: This email address is being protected from spam bots, you need Javascript enabled to view it

ABSTRACT

Big data applications became increasingly important in different fields. This study aims to show the role of big data in achieving sustainable development purposes, using documentary and literary sources for gathering and analyzing information about the topic. This study reached several results, mainly: big data provides opportunities in sustainable development like improvement of different services levels, promotion of innovation, and motivation of investment and creation of new sustainable jobs, improvement of institutions’ performance, and facilitation of making a decision.

20.1 INTRODUCTION

The unsustainable practices based on increased consumption of nonrenewable resources to achieve economic purposes led to increasing negative environmental impacts such as pollution and climate change. In this circumstance, global orientation has been made towards sustainable development based on economic, social, and environmental dimensions. However, achieving sustainable development goals requires the availability of various data and information for the formulation and follow-up of effective policies.

Recently, by observing the development of data, we found that a tremendous amount of data produced, stored, and made available from multiples

sites has become a force source of any society based on knowledge, as well, the capacity to address data and complex analyses has also increased to obtain important information.

Thereby, big data is becoming a very famous concept in academia. It has been used in many fields of modem society and is widely used in banking, education, logistics, and other fields [1].

Its applications offer the ability for collecting and analyzing information in real-time from different states for policies related to the 2030 Agenda’s 17 goals and their 169 targets and help decision-makers to devise strategies in order to improve the economies of societies, achieve competitiveness, preserve the environment and health, protect the community and meet the needs and improve living standards [2].

Hence, big data is needed to help governments make effective progress at various levels to become sustainable [3]. Therefore, in this context, the purpose of this research is to demonstrate the importance of big data and its positive effects on the economy, society, and environment by focusing on its role as a tool for policy planning and sustainable development.

The problem of this research steams from the following question:

How can Big Data contribute to achieving sustainable development goals in light of Big Data applications ’ opportunities for development planning and evaluations?

The chapter is structured as Section 20.2 contains big data definition, characteristics, dimensions, and sources. Section 20.2.2 gives information about the sustainable development concept and its pillars and goals. Section

  • 20.3 discusses the relationship between big data and sustainable development goals. Section 20.4 is the conclusion.
  • 20.2 THEORETICAL BACKGROUND
  • 20.2.1 BIG DATA: DEFINITION, CHARACTERISTICS, DIMENSIONS, AND SOURCES [1]

enable enhanced decision making, insight discoveiy, and process optimization" [4].

This definition refers to the three basic characteristics of big data, also known as the 3V’s:

  • • Volume refers to the large scale of big data, which requires innovative tools for collection, storage, and analysis.
  • • Velocity represents the speed at which data is created or updated, pointing to the real-time nature of big data.
  • • Variety signifies the variation in types of data. Big data comes in diverse and dissimilar forms from multiple sources.

After a few years, IBM added another characteristic or “V” on the top of Laney’s 3V’s notation, which is known as 4V’s of big data. It describes each “V” as following [5]:

i. The volume stands for the scale of data.

ii. Velocity denotes the speed of data transfers.

iii. Variety means different forms of data.

iv. Veracity indicates the uncertainty of data.

In 2013, Yuri Demchenko extended this 4V model to a 5V model by including the value dimension, which refers to the process of extracting valuable information from large sets of social data, and it is usually referred to as big data analytics [6]. It means the core of big data that address the cost/ benefit proposition.

Other organizations and big data practitioners proposed a 6V’s model by adding: Veracity, Variability, and Visibility. The first V, Veracity, means the degree of reliability and credibility of data sources. The second V, Variability, refers to the complexity of the data set. The third V, Visibility, emphasizes the data accuracy in order to make a decision [5].

All the above definitions agree that big data is a large volume of high velocity, complex, variable, and visible data that require advanced techniques to enable the collection, storage, distribution, management, and analysis of information to facilitate decision-making.

Hence, big data aims to gain hindsight from historical data, insight from understanding the issues, and foresight by forecasting in the future in a cost- effective manner.

Moreover, Timo Elliott stated that each definition of big data focuses on a particular issue from one aspect of big data, so he classified these definitions into seven types, shown in Table 20.1 [5].

TABLE 20.1 Seven Types of Big Data Definitions by T. Elliott

No.

Types

Description

1.

The original big data

(3 Vs)

The original type of definition is referred to Douglas Laney’s volume, velocity, and variety, or 3 Y’s, such as 4 Y’s, 5 Y’s, up to 6 Y’s

2.

Big data as technology

This type of definition is driven by new technology development, such as MapReduce, bulk synchronous parallel (BSPJHama), resilient distributed datasets (RDD, Spark), and Lambda architecture (Flink)

3.

Big data as an application

This kind of definition emphasizes different applications based on different types of big data. Barry Devin defined it as an application of process-mediated data, human- sourced information, and machine-generated data. Shaun Connolly focused on analyzing transactions, interactions, and observation of data. It looks for hindsight of data.

4.

Big data as signals

This is another type of application-oriented definition, but it focuses on timing rather than a data type. It looks for the foresight of data or a new “signal” pattern in a dataset.

5.

Big data as an opportunity

Matt Aslett: “Big data as analyzing data that was previously ignored becaitse of technology limitations.” It highlights many potential opportunities by digging-in the collected or achieved datasets when new technologies are variable.

6.

Big data as a metaphor

It defines big data as a human thinking process. It elevates BDA to a new level, which means BDS is not a type of analytic tool; it’s actually an extension of the human brain.

7.

Big data as a new term for old stuff

This definition simply means the new bottle (relabel the new term “big data”) for old wine (BI, data mining (DM), or other traditional data analytic activities). It is one of the most cynical ways to define big data.

Source: Adapted from Ref. [5].

  • 2. Big Data Dimensions: Big data as an integrated approach for development includes three dimensions, shown in Figure 20.1 [7]:
    • • Data generation generates and collects large volumes of data using smart technologies.
  • • Data analytics involves the organization and integration of various sources of data and the identification of previous patterns and associations in data, explained in Figure 20.1.
  • • Data ecosystem involves producers, analysts, regulators, and users of big data to combine big data analytics based on quantitative and qualitative analysis to ensure successful applications of big data, including interactions between humans and big data digital technology.
Data analytics applications for development. Source

FIGURE 20.1 Data analytics applications for development. Source: Reprinted from Ref. [14]. © UN Global Pulse, 2016

  • 3. Big Data Sources: big data has many sources such as social media, machine-generated data, sensor data, transaction data, and the Internet of things (IoT), which can be summarized as follows [8]:
    • • Social media is a web-based service that allows individuals to construct a public or semipublic profile within a bounded system, communicate with other users, and view the pages and details provided by other users within the system. This data source contains a lot of information which is generated using URL (Uniform resource language) to share or exchange information in virtual communities and network for an example: Facebook, Twitter, Linkedln, and YouTube.
    • • Machine-generated data information is automatically generated from both hardware and software, such as smart meters that continuously stream data about electricity, water, or gas consumption.
    • • Sensor data are collected from various sensing devices, and these are used to measure physical quantities. There are two types of sensor; the first one is fixed sensors like weather sensors, traffic sensors, and scientific sensors. The second one is mobile sensors such as mobile phone location (GPS) and satellite images.
    • • Transaction data involves a tune dimension to illustrate the data, for example, commercial transactions.
    • • Internet of things generates a huge amount of data from a lot of internet-connected devices such as smartphones and digital cameras.
  • 20.2.2 SUSTAINABLE DEVELOPMENT: CONCEPT, PILLARS, AND GOALS
  • 1. Sustainable Development Concept: The concept of sustainable development has undergone different developmental phases since its introduction.

Previous periods considered the development as a synonym for economic growth until the 1970s when it was obvious that economic growth has negative influences on the environment, such as climate changes, ecosystem disturbances, and natural disasters.

At the same time, the increasing consumption of non-renewable resources, especially the stock of fossil fuels, led to the deliberation of the needs of fimire generations and created a prerequisite for describing the attitude of long-term and rational use of non-renewable resources [9].

Under these circumstances, a group of scientists, economists, and humanists from ten countries gathered as an independent organization named “Roman Club” in Rome in 1968 in order to discuss the current problems and future challenges of humankind, including limited natural resources, environmental degradation, population growth, etc.

This club has published two editions, the first one titled: Limits of Growth in 1972, and the second one titled: Mankind at the Turning Point in 1974, containing the results of then research and warning that excessive industrialization and economic development would soon cross the ecological boundaries [10].

Different organizations participated in creating the concept of sustainable development; in particular. United Nations - which was established in 1945 - actively acted in this field by organizing many conferences and publishing various reports for achieving sustainable development goals.

Since the introduction of the concept, a lot of conferences, meetings, and congresses have been held, resulting in various reports, conventions, agreements, and dealing with the environmental problems, summarized in Table 20.2 [9].

TABLE 20.2 Overview of Important Activities Related to the Concept of Sustainable Development

Year

Activities

Description

1969

UN published the report titled: Man, and His Environment

Focusing on Avoiding global environmental degradation.

1972

First UN world conference about the human environment, Stockholm

Setting up a global environmental framework, known as the Belgrade Charter.

1975

International Congress of the Human Environment, Kyoto, Japan

Emphasizes Problems discussed earlier in Stockholm in 1972.

1979

The First World Climate Conference, Geneva

Focused on the creation of climate change research and program monitoring.

1981

The First UN Conference on Least Developed Countries, Paris

Resulted in a report containing guidelines and measures for helping the underdeveloped countries.

1984

Establishment of the United Nations World Commission on Environment and Development (WCED)

The commission’s task was the cooperation between developed/ developing countries and the adaptation of global development plans on environmental conservation.

1987

WCED report called: Our Common Future

A report contained the fundamental principles of the concept of sustainable development.

1990

The Second World Climate Conference, Geneva

Focused on the finther development of climate change and the creation of the Global Climate Change Monitoring System.

1992

UN conference on Environment and Development, Rio de Janeiro

The Rio Declaration and Agenda 21 created an Action Plan principle of sustainable development and the framework for future tasks.

TABLE 20.2 (Continued)

Year

Activities

Description

1997

Kyoto Climate Change Conference, Kyoto, Japan

The Kyoto Protocol was signed between countries in order to reduce CO, and other greenhouse gas emissions, with commencement in 2005.

2000

UN published Millennium Declaration

Declaration containing eight Millennium goals targeted by 2015.

2002

The World Summit on Sustainable Development, Johannesburg

The report contained the results achieved during the time from the Rio Conference and set the guidelines for implementation of the concept in the future.

2009

The Thud World Climate Conference, Geneva

Further development of the Global Climate Change Monitoring System with the aim of timely anticipation of possible disasters.

2012

UN conference Rio+20, Rio de Janeiro

Twenty years from the Rio conference resulted in a report: The Future we want to be renewed - the commitment to the goals of sustainable development and encouraged issues of the global green economy.

2015

UN Sustainable Development Summit, New York

The UN 2030 Agenda for Sustainable Development was established, setting up 17 Millennium Development Goals, which should be achieved by 2030.

2015

UN conference on Climate Change COP 21, Paiis

Agreement on the reduction of greenhouse gases in order to limit global wanning.

Source: Reprinted from Klarin, T. 2018. [Ref. 9].

Among various activities cited in the previous table, the history of the concept of sustainable development is divided into three periods. The first one covers the period from economic theories to the First UN Conference on the Human Environment held in Stockholm in 1972, which marked the introduction of the concept of sustainable development without a full association between environmental problems and economic development [9].

The second period extended between 1973 and 1990, marked by holding several conferences and many issued reports, especially in 1987, the United Nations World Commission on Environment and Development (WCED) published a report Our Coimnon Future contained the fundamental principles of the concept of sustainable development, based on analysis of the conditions in the world (socio-economic development) and order, environmental degradation, population growth, poverty, etc.

In this period, sustainable development is defined as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” [11]. The third period starts after a previous period and lasts until nowadays; it contains important events such as United Nations Conference on Environment and Development held in Rio de Janeiro in 1992, resulted in the following documents [9]:

  • • Rio declaration on environment and development, which contains 27-principles of sustainable development, forming the basis for future policy, decision making, and balance between socio-economic development and the environment.
  • • Agenda 21: a global program with the aims of sustainable development, action plans, and resources for their implementation. It provides guidelines for socio-economic development in line with environmental conservation.

From these documents, the three fundamental elements of sustainable development are identified:

  • First Element: The concept of development based on socio-economic development in line with environmental constraints.
  • Second Element: The concept of needs.
  • Third Element: The concept of the future generation.
  • 2. Sustainable Development Pillars: Sustainable development based on balancing the three pillars of sustainability: the first is ecological sustainability, which means to maintain the quality of the environment needed for economic activities and quality of life; the second is social sustainability based on preservation of society and cultural identity, etc. The third is economic sustainability signifies maintaining the various sources (natural, human, social) to achieve income and living standards. The balance between these pillars is difficult to achieve because each one in the process of achieving its goals, must respect the other pillars’ interests [12].
  • 3. Sustainable Development Goals: In 2015, the United Nations announced 17 sustainable goals which should be achieved by 2030; we can summarize most of them as follows: eradicating poverty and hunger, good health and education, clean energy and water, ensuring gender equality, ensuring environmental sustainability and global partnership for development [13].
  • 20.3 RESULTS AND DISCUSSION
  • 20.3.1 BIG DATA AND SUSTAINABLE DEVELOPMENT GOALS

Big data plays an active role in achieving sustainable development goals by the improvement of understanding issues or problems and offers policymaking support for the development in three main ways: the fust way is an early warning, which means early detection of anomalies to enable faster responses to populations in crisis times.

The second way is real-time awareness; signifies fine-grained representation of reality through big data that can inform the design and targeting of programs and policies. The third way is real-time feedback; concents adjustments that can be possible by real-time monitoring the impact of programs and policies [14].

Big data can improve current official statistical systems in many ways, such as: providing complementary statistical information in the same statistical domain but from other perspectives, improving estimation from statistical sources, and providing new statistical information in a particular statistical domain [15].

Moreover, big data contributes to the management of aerial issues at the local, national, and international levels through their applications in enterprise development and in various economic sectors such as health, agriculture, energy, and water resources, which can be explained as follows:

• In enterprise development, big data enables these enterprises to develop accurate analyses of current and potential clients, which could address a lack of efficiency in industrialization. For example, the first product of microinsurance has been established and distributed in Africa using a mobile phone network.

  • • In the health sector, big data can contribute to improving health care through the provision of various data about patients and its illness from which can detect early evolution and treatment in time. Data maps also support the response to the spread of diseases. For example, the Ugandan Ministry of Health used data maps in the spread of typhoid disease to facilitate the decision concerning the allocation of medicines and the distribution of health teams.
  • • In the agriculture sector, big data applications for agricultural crops contribute to achieving food security and eliminating hunger. For example, the implementation of software produced by the Indian company Cropln has facilitated crop management by providing data on crop growth at various stages of production.
  • • In the energy sector, big data applications contribute to the balance between demand and supply of energy through the provision of available data, which enable energy institutions to better respond to demand changes, supply costs of electricity, avoid power outages as well as the exploitation of renewable energy to meet of increasing demand for electricity.
  • • In the water field, big data applications facilitate the management of water resources. For example, AQUASTAT is FAO’s global water system, which collects and analyses data on water resources, water uses, and other information [7].

Furthermore, the United Nations published a report about how big data applications can be used to achieve sustainable development, shown in Figure 20.2 [13].

Big data applications for sustainable development

FIGURE 20.2 Big data applications for sustainable development.

20.4 CONCLUSION

This study tries to give an overview of big data characteristics, dimensions, sources. We also tried to show the essential role of big data applications in the improvement of decision making and achieving sustainable development goals, using enormous reports and studies in order to illustrate how big data is currently being used to develop help for disaster relief, mine citizen feedback and map population movement to support response to crises outbreaks.

Big data applications provide many opportunities in achieving sustainable development goals. The study reached several results, which can be summarized as follows:

  • • Providing accurate data to improve the level of different services as best health care, clean energy.
  • • Usage of data in different economic fields as using databases in investment motivation and creating jobs, thereby increasing the efficiency and quality of sendees and goods.
  • • Promote innovation by using big databases in detailed studies to innovate new services contributing to improving institutions’ performance.

Big data applications face several challenges, such as a lack of human resources competencies, which enable the optimization benefits of using big data, its privacy, and security.

KEYWORDS

  • • big data
  • • sustainable development
  • • goals
  • • decisions-making

REFERENCES

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2. Singh, S. K., & Abdulnasser, E. K., (2018). Role of big data analytics in developing sustainable capabilities. Journal of Cleaner Production, 213,1264-1273.

  • 3. Song, M., Cen, L., Zheng, Z., Fisher, R., Liang, X., Wang, Y., & Huisingh, D., (2016). How would big data support societal development and environmental sustainability? Insights and practices. Journal of Cleaner Production, 142,489-500.
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  • 10. Meadows, D., Randers, J. & Meadows, D., (2004). Limits to Growth: The 30-Year Update (p. 218). Earthscan Edition, UK.
  • 11. UnitedNations WCED, (1987). Our Common Future (p. 247). United Nations, New York. https://sustainabledevelopment.un.org/content/documents/5987our-common-future .pdf (accessed on 22 October 2020).
  • 12. Strange, T., & Bayley, A., (2008). Sustainable Development: Linking Economy, Society>, and Environment (p. 82). OCDE insights series.
  • 13. United Nations, (2017). The Sustainable Development Goals Report (p. 64). New York. https://unstats.im.org/sdgs/files/report/2017/thesustainabledevelopmentgoalsreport 2017.pdf (accessed on 22 October 2020).
  • 14. UN Global Pulse, ‘Integrating Big Data into the Monitoring and Evaluation of Development Programmes,’ 2016. https://www.unglobalpulse.org/wp-content/uploads/ 2016/12/integratingbigdataintomedpwebungp- 161213223139.pdf
  • 15. Sriganesh, L., Thavisha, P. G., & Shazna, Z., (2017). Mapping Big Data Solutions for the Sustainable Development Goals (p. 81). IDRC Edition, The international development research center, Canada.

CHAPTER 21

  • [1] Definition and Characteristics: There are various definitions of bigdata available in the literature. Among them is one from Gartner in2012, defined as: “big data is high volume, high velocity, and highvariety information assets that require new forms of processing to
 
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