IV: BIG DATA AND SUSTAINABLE DEVELOPMENT

CHAPTER 19

Big Data Analysis and Sustainable Development

DEHBIA EL DJOUZI

Senior Lecturer and Researcher, Faculty of Business Economics and Management, Djillali Bounaama University, Theniet El Had Street, Khemis Miliana, Algeria, E-mail: raison81 @yahoo.fr

ABSTRACT

Through this chapter, we will explore how big data analysis supports sustainable development efforts. In fact, big data projects can make a significant contribution to health care by improving and reducing health sendees costs, preventing disease and supporting innovation, research, and development in medicines, treatment, and improvement of public health.

Big data also helps educational institutions in the understanding of their beneficiaries, their requirements, and the reduction of the educational process costs. Big data projects can also contribute to fighting crime, ensuring security, coping with and predicting natural disasters, as well as supporting environmental conservation efforts.

At the economic level, analyze the impact that big data can have on economic policy-making and rationalization of resource exploitation.

However, big data projects are not without challenges such as privacy issues, lack of human capacity, expanding IT infrastructure to deal with the demands of large data sets, as well as risks of growing inequality and bias. There are gaps between those who own data and those who do not.

But big data can deliver on its promises to achieve sustainable development in the coming years if more efforts are made to improve affordable access to ICTs and knowledge for all people, promote opportunities for open learning and lifelong learning, build human capacity to exploit big data, ensure that specialized expertise is kept up with technological developments and promote North-South, South-South cooperation for technology transfer and skills.

19.1 INTRODUCTION

Increased technological development has allowed the analysis of the vast amounts of available information on the Internet or the so-called “big data,” which led to the development of new technologies that can be used in many areas such as business, medicine, education, and security.

Three factors contributed to their emergence; first, Internet users exchanged an astronomic amount of data related to cross-cutting topics; second, the growing capacity of data centers to store increasing volumes; third, effective cloud computing can process trillions of digital processes just in few seconds.

Simultaneously, the scrutiny of “big data” has led to obtain valuable professional information and discover future business patterns and trends and multiple options and trends for customers. Big data has thus become the lifeblood of decision-making, the raw material of the liability process, and turned into knowledge that can be exploited in all areas of life.

The rapid growth in the production of data in terms of volume, source, velocity, and variety, faced great challenges in how to deal with these data and maximize the use of it so that, in the meantime, big data has become a recent event by most national, regional, and international bodies and institutions, and a wide range of research in database management, methodologies, and procedures that can be adopted to harness big data to serve the sustainable development goals.

What is big data, and how can it be used to achieve the SDGs?

To deal with this problem, we will address the following points:

  • • What is the meaning of “big data” and what are its characteristics?
  • • What is sustainable development, and what are its objectives?
  • • How can the opportunities presented by big data be used to serve the SDGs?
  • • What are the main challenges associated with the exploitation of big data?

To answer these questions, we divided our study into the following sections:

  • Section 19.2: The concept of big data and its characteristics.
  • Section 19.3: The concept of sustainable development and its objectives.
  • Section 19.4: Opportunities provided by big data for sustainable development.
  • Section 19.5: Big data exploitation challenges.
  • 19.2 BIG DATA CONCEPT AND CHARACTERISTICS

Not so long ago, data was confined in a categoiy of organized databases in folders, files, tables, and others. This now constitutes no more than 10% of the world’s total infonnation. However, access to new, accelerated, and unstructured infonnation sources such as e-mails, videos, Facebook posts, tweets, WhatsApp chat messages, and other sources have set large databases.

19.2.1 BIG DATA CONCEPT

The definition of big data may not be clear because the volume of data is not defined by place or time. With the current acceleration in the development of infonnation and communication technology (ICT), the huge data at present may not be huge in the future. In addition, a large amount of data for a particular person or institution may not be large for another person or institution. Below we will tiy to provide some definitions for big data.

In 2011, the Mackenzie World Institute launched a definition of big data: a data set larger than the ability of traditional databases to capture, store, manage, and analyze those data. In this context, the term “big data” in the field of infonnation technology has been given to a set of veiy large and complex data packets that are difficult to deal with by traditional database management systems.

Also, the United Nations describes big data as “sources of data of large volumes, high velocity and data variety, which require new tools and methods for their capture, preservation, management, and effective processing" [1].

Big data is a set of large and complex data that has unique characteristics (such as volume, velocity, variety, data validity), which cannot be efficiently processed using existing and traditional technology to take advantage of it.

The challenges associated with this type of data are its provision, processing, storage, analysis, research, sharing, transmission, imaging, and updating, as well as the preservation of the specificities that accompany it [2].

19.2.2 CHARACTERISTICS

Characteristics of big data are as follows [3]:

  • 1. Volume: Amount of data extracted from a source, which determines the value and volume of data to be classified as big data. By 2020, cyberspace will contain approximately 40,000 MB of data ready for analysis and debriefing.
  • 2. Variety: It means the variety of the data extracted, which helps users, whether researchers or analysts, to choose the appropriate data for theh field of research and includes structured and unstructured data in databases such as images, clips, and recordings of audio and video, SMS, and call logs and maps data (GPS) and requires time and effort to be configured in a suitable format for processing and analysis.
  • 3. Velocity: The velocity of production and extraction of data to cover the demand for is the velocity of a critical element in the decisionmaking based on these data, which is the time between the moment of the arrival of this data and the moment of decision.
  • 4. Reliability and Argument: It refers to the reliability of data source, accuracy, correctness, and timeliness of data, as one in three executives do not trust the data presented to them for decision. For example, there are studies that estimate that the damage to good data on the US economy is estimated annually at S3.1 trillion.
  • 19.3 SUSTAINABLE DEVELOPMENT MEANING AND DIMENSIONS

The sustainable development idiom was first mentioned in the 1987 report of the World Commission on Environment and Development (WCED), which is defined in this report as: “that development which meets the needs of the present without compromising the ability of future generations to meet their needs.”

In 1989, Barbier defined sustainable development more broadly as encompassing the creation of a social and economic system that would ensure support for the following objectives: an increase in real income, an improvement in education level, and an improvement in population health

[4].

The United Nations has set seventeen development goals as a plan for a better future for all. These goals face current global challenges [5]:

  • 1. Elimination of poverty;
  • 2. Elimination of hunger;
  • 3. Good health and well-being;
  • 4. Good education;
  • 5. Gender equality;
  • 6. Clean water and hygiene;
  • 7. Clean and affordable energy;
  • 8. Decent work and economic growth;
  • 9. Industry, innovation, and infrastructure;
  • 10. Reduce inequalities;
  • 11. Lasting cities and communities;
  • 12. Responsible consumption and production;
  • 13. Climate action;
  • 14. Life underwater;
  • 15. Wildlife;
  • 16. Peace, justice, and strong institutions;
  • 17. Partnerships to achieve goals.
  • 19.4 OPPORTUNITIES PROVIDED BY BIG DATA FOR SUSTAINABLE DEVELOPMENT

Big data will contribute to the achievement of sustainable development goals, especially in light of the large spread of the Internet. This new technology will contribute to the management of difficult issues at the local and global levels and help address them.

Big data technology can help promote enterprise development. It allows the development of tailored and accurate analysis, to businesses, for existing and potential customers, improves user experience, addresses manufacturing inefficiencies, and associated processes [6].

In the health sector, big data technology may allow improved health care to diagnose treatment, collect patient data beyond what it exchanges with the doctor on sporadic visits; detect early disease progression and proactively treat it at the individual and community level, and arrive at more effective treatments for a range of diseases. In particular, data maps can help support response to disease outbreaks.

In agriculture, big data technology opens up new opportunities in agriculture, including food security.

Big data technologies can balance energy supply and demand by installing smart grids that enhance the role that renewable energy sources play hr the distribution and the production of energy by supplying households with solar panels or wind turbines to feed the power grid with surplus energy.

Real-time information provided by smart grids helps electrical supply companies improve their responses to changes hr demand, power supply costs, and emissions, as well as to avoid power outages.

Efficient water production and distribution, especially in urban areas, is an ever-present challenge for governments. In this context, Internet-related tools, such as sensors, meters, and mobile phones, can be provided with functions that allow monitoring and study of water quality for smarter water management, as hr the case of a wireless sensor network.

The collection and measurement of development indicators will be central to monitoring progress hr the achievement of development goals. In this context, stakeholders, including international organizations, academics, and corporations, are seeking to explore ways in which big data can contribute to the objectives of monitoring and economic policy-making activities.

Big data can provide a unique service to educational institutions if they can use, process, store, and manage it. It provides a better understanding of the beneficiaries and their requirements and helps to make appropriate and rational decisions within these institutions in a more efficient and effective manner using the information extracted from the beneficiary databases and thus reduce costs for students and faculty [7].

Big data and Internet sensors for research and development enable researchers to analyze and discover patterns of scientific data that were not available until recently. There are many areas in which such capabilities are of great benefits, such as meteorological forecasts and the human mind’s exploration.

Many big data technologies and artificial intelligence algorithms are based on open-source technology. This makes them freely available for use, exchange, change, and adaptation by creating opportunities for local and pro-poor innovation adapted to regional needs and markets.

Using open licenses by colleges and universities can develop versions of big data technologies and machine learning algorithms that address local challenges. However, working with these technologies and innovating through it requires appropriate skills (such as the ability to analyze and preview big data, requiring mathematical and computational skills), and thus highlights the importance of capacity building to take advantage of these new technologies [6].

19.5 ISSUES RELATED TO BIG DATA

Governments and institutions face a number of greetings and risks in their implementation of large data projects. Concerns include legal issues related to the privacy and the access to data, lack of human capacity, and the expansion of IT infrastructure to address the demands of large data sets, including challenges we find: [1]

Another challenge is detennining what to do when data analysis indicates that a person is about to commit a crime. Prosecutors can ask a judge to place someone under house arrest or imprisonment if there is sufficient physical evidence, but arrest a person based on big data analysis can be more difficult to convince a judge.

Perhaps data and software do not always show the foil picture, although big data programs and their associated technologies provide data and information in advance, contribute to law enforcement and crime prevention, but before we can do that, programs need to be improved and answer important questions, such as effects on personal privacy.

2. Data Coverage Challenges: Often, there are categories of the target community for which no data are available, or data may be available, but incomplete, as the data may not cover certain variables under consideration.

This requires the availability of expertise, technical, and statistical capabilities in addition to tools and software for data processing and compensation for missing values and testing the quality of statistical data as a final output.

  • 3. Big Data Volume: Dealing with the large volume of data is a major challenge for all institutions in most countries. This challenge concerns the ability of these institutions to store a large amount of data, as well as procedures for processing data and minimizing the impact of expected procedural errors on results [1].
  • 4. Data Usage Laws and Regulations: Analysis of the huge data is based on data that can be discovered, accessed, and used, and data must be collected, stored, used, and managed in conformity with the relevant laws and regulations.

To realize the foil potential of big data, departments and government agencies need to focus their attention on making as much data as possible open and accessible. States are currently publishing an open data policy to facilitate the use of government data within government agencies, as well as by the public [9].

Data stored in one country may be of utmost importance to institutions in another country, so the fact that different countries have adopted different laws and regulations on data storage, how they are used, and what data is accessible is a big obstacle for users of big data.

5. Data Access: Although the majority of new technological transformations are seen as a powerful tool that may, in different ways, help the political, economic, and social empowerment of the average user, big data sensors are often in the hands of intermediate institutions of government agencies and large corporations and do not have ordinary people causing disparities of power. Between those who have the data and those who don’t [10].

Big data is also expected to open a new front of disparity of forces among nations of the world, dividing them between those who know and those who do not. The coming wars will be essentially information wars, so who will have access to and analyze big data will be able to provide new opportunities to enhance his intelligence and take the appropriate decision for his national security.

6. Market Access: Increased dissemination and the exploitation of big data technologies can create highly diverse jobs. For instance, in the United States, there are estimated to be about 500,000 jobs in the big data sector in 2014.

Countries’ ability to participate competitively and actively in global markets depends on, inter alia, their ability to create a trained workforce capable of understanding the unprecedented flow of data generated by these innovations and harnessing them to produce real value.

Some professions related to big data include mathematics, computing, and engineering [6]. Moreover, than a trained workforce, big data’s effective application requires a wide range of supporting infrastructure and enabling policy frameworks, such as cloud computing resources and interoperability standards.

7. Data Management, Use, and Analysis: Large businesses are looking for the right ways to store, manage, use, and analyze data to make the most of it. A New Vantage Partners survey found that only about (37.1%) of businesses believe that they are successful in trying to use big data, while (71.7%) of institutions believe that they didn’t formulate a culture of data dependence yet, and the rate of (53.1 %) of institutions stated that they didn’t yet deal with data as a commercial asset.

These institutions often fail to know the basics about what big data is, what its benefits are, what infrastructure to adopt, and so on. Without a deep understanding of all these fundamentals, the big data

Adoption Project will fail, and organizations may waste a lot of time and resources on things that employees do not know how to use [11].

If employees are unaware of the benefits of big data or do not wish to change the methodology of existing processes for adoption, they will resist it, thereby impeding the institution’s progress.

8. Data Quality: The integration of the data is a problem that organizations face because the data they need to use comes from a variety of sources. For example, e-commerce companies used to analyze data from website records, call centers, and competitors’ websites, and there is obviously data inconsistency, so hard to match.

A bigger challenge is unreliable data, since big data is not accurate, not only because it can contain wrong information, but because it can be repetitive, as well as may contain discrepancies. Poor quality data are unlikely to provide any useful information or important opportunities, and inaccurate information may increase the risk of making wrong decisions.

  • 9. Big Money Expenditure: Big data accreditation projects involve a lot of expenses, taking into account the costs of new hardware, hiring staff such as system administrators, developers, etc. Although the necessary systems are open source, there is always a need to pay for new software development, preparation, and maintenance. If one decides to rely on a large cloud-based data solution, organizations will still need to hire staff, pay for cloud sendees, develop big data solutions, and set up and maintain frameworks.
  • 10. Timing: The accessibility and timeliness of finding items in a limited time in a large database is another new challenge in data processing, and the ability, for new types of criteria, to respond to data requests with limited tunes is an additional challenge.
  • 11. Financial Loss and Reputation: This is a result of big data penetration.
  • 19.6 CONCLUSION

Through the above study, it becomes clear that big data projects can play a vital role in economic growth and sustainable development by their exploitation to improve health sendees, open learning, increase access to education, stimulate innovation and agriculture and business, and open the door to increased transparency, justice, and efficiency in service delivery and access to energy sources.

However, the barriers to harnessing big data as a useful tool for development are many, but not insurmountable, as big data bring the expecting results. In the coming years, if conditions are right to exploit them, by (I) making greater efforts to improve affordable access to ICTs and data whether in urban and rural communities, (ii) promoting the continuous development of network security and privacy, (iii) opening learning and lifelong learning opportunities for all members of society, and (iv) building human capacity in the exploitation of big data, etc.

KEYWORDS

  • • big data
  • • privacy
  • • sustainable development

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CHAPTER 20

  • [1] Privacy: Tracking personal issues and monitoring to analyze (web)page or social network visits, phone calls, and e-mail, and track andmonitor religious, political, or terrorist tendencies is a risk of bigdata, for example, former US President Obama and former BritishPrime Minister Cameron (such as Google and Facebook) in collaboration with intelligence in tracking terrorists on social networks andthe Internet. Furthermore, this action provoked protests from human rightsorganizations, which represented a violation of personal privacy.Big data includes collecting and analyzing personal data about individuals, population information, business, government, and militaryactivities, water consumption, energy, national reports for variouspurposes, online IP abuse, social media, e-mail, and free subscriptions to websites. Big data technologies and services tax the protection of individuals’privacy and sensitive data during the processing cycle, while keepingthat data stored, and scalability is a real threat to security and privacy. The biggest obstacle to the manipulation of big data in criminalactivity prediction is that programmers and law enforcement are notcooperating [8].
 
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