Jamiil Touré Ali
Data for Sustainable Development Unit
j.toure@cepei.org
Introduction: Big data and the 2030 Agenda
We live in a data century and information floods from every corner of the world, yet we live in a world where some people’s data are left behind as opposed to the 2030 Agenda principle: Leaving no one behind.
Digitization and rapid advances in Information and Communication Technologies (ICT) have propelled us to this torrent of information i.e big data. Nevertheless, humankind needs such information to be stored and processed in an efficient way to solve the challenges we are facing worldwide. And big data proposes data storage, mining, analytics, visualization tools that could help us find insights for decision making.
The 2030 Agenda adopted by the world community in 2015, is a result of extensive negotiations among the Member States, which embodies 5 core principles: Universality, Leaving no one behind, Interconnectedness and Indivisibility, Inclusiveness, Multi-Stakeholder Partnerships. At the heart of the 2030 Agenda are the Sustainable Development Goals (SDGs) which help translate the core values and principles underlying the agenda into concrete and measurable results. To monitor progress and achievements underpinned in the agenda, communities rely on tracking traditional data sources (survey, census, etc). However, big data and the 2030 Agenda is the new synergy that can help us better track goals and targets to achieve sustainable development.
How could the use of big data be promoted to advance the 2030 Agenda?
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What is big data for?
Big data is the study of a very large amount of data that cannot be operated with standard data processing software due to its size. This presents some complexities in finding not misleading correlated insights as well as challenges for capturing, storing, analyzing, and modeling them. Big data is often characterized by the 3 V’s dimension Volume, Velocity, and Variety.
- Volume: The quantity of data we receive in a single day, hours, minutes, and seconds
- Velocity: The speed at which data is now available
- Variety: The different sources and format of data
While common scientists tend to agree on those characteristics, big data characteristics are sometimes also expanded to 5V or 6V characteristics or more. The 2 or 3 extra V’s usually refer to Veracity, Value, and Variability.
- Veracity: To measure the trustworthiness of such a big amount of data that determines the analysis we produce.
- Value: To represent the key information brought by such big data processing
- Variability: To capture the disparity of data sources and types of information contained in our big data that could be unstructured and/or structured
Then V’s characteristics of big data are often measures or evaluation tools to assess a large amount of data hence a guide for effective analysis to extract value from the data.
Valuable information can be drawn from big data if organized and processed adequately. And this could be applied to various domains such as education, government, healthcare, marketing, finance, sports, among others. Some of these usages consist of predictive, descriptive, prescriptive, and diagnosis analytics, which help for better decision-making. For instance, the government can use big data to optimize traffic flow in cities by establishing public transportation with an accurate routing system and transportation fare adjusted to the passenger standard of living.
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What is big data for sustainable development?
Big data has the power to assist altogether strides in the quality of life for all. The United Nations (UN), Governments, non-profits organizations, and other groups are using big data to assist progress towards the SDGs, hence resolving social, environmental, and economic challenges, focusing on inclusive, participatory development that leaves no one behind.
Big data for sustainable development means the use of non-traditional data sources to measure the progress of the SDGs through indicators and targets. Non-traditional data sources, as opposed to the traditional, are those data sources that don’t require the physical presence of agents on a field to record or track data, and use ICT. Some non-traditional data sources are mobile phone data, satellite imagery data, geodata, web data, Twitter data, financial transaction data, scanner data, Facebook data, sensor data, smart meter data, etc. Non-traditional data sources can be used to achieve the SDGs and the 2030 Agenda.
For illustration, UN initiative Global Pulse proposes one example for each of the 17 SDGs showing how big data could be used to support their implementation:
Moreover, Big data SDGs and COVID-19 shows a case-by-case study on how big data is used to advance on SDGs 1, 2, 3, 4, 8 amidst the COVID-19 pandemic.
Considering the plethora of non-traditional data sources one could use for SDGs big data-related projects, a survey by the UN task team in 2015 found that the most frequently investigated goals and data sources are respectively SDG1 and mobile phone data. A word of caution that data source types and investigation on SDGs should be diversified so we leave no one behind.
Big data helps achieve the 2030 Agenda by providing data storage, mining, analytics, visualization tools. And while the Vs helps propose a framework to safely provide some insights for decision making, one should beware that data privacy is still one of the biggest challenges when designing a big data analytics solution.
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How does Cepei promote the use of big data to advance the 2030 Agenda?
In light of the different ways big data could be used to promote and advance sustainable development goals, Cepei as one of the leading organizations following up on the 2030 Agenda in the LAC region, believes that big data offers an immense opportunity to better track the progress and achieve sustainable development by 2030, and stir interest in multi-stakeholder partnership engagement. Below are some of our contributions using big data for sustainable development:
- Data story about Mocoa: An analysis of the impact of internal and external mobility caused by the torrential avenue in the city of Mocoa, between March 31 and April 1, 2017, through the registry of mobile calls and data traffic. It also presents a chronological overview of the actions that have been taken by government authorities to rebuild the city and support its population.
- Artículo Crisis migratoria en Colombia y Costa Rica: una visión desde el análisis de sentimientos (SPA): A sentiment analysis research using big data to understand the migration crisis in Colombia and Costa Rica.
- Workshop on sentiment analysis toolbox: A 3-day workshop to strengthen technical capacities on the analytic potential of non-traditional sources of big data. As a case study, the Workshop employed Twitter sentiment analysis to understand public reaction to Government COVID-19 restrictions – the case of Alorica.
- Workshop on big data and data visualization: Two workshops on big data and data visualization for local representatives of the private sector, academy, statistical offices, and experts in data and statistics which respectively addressed the following subjects: The importance, challenges, and opportunities of the use and applications of big data in the private sector, as well as the benefits of contributing with big data to sustainable development in Jamaica and to strengthen the technical and methodological skills for the application of big data to measure, implement and monitor SDG processes.
- Big data smart metering grids: An explanation on how we could use big data through smart metering grids to achieve SDG 7.
- Big data in education literacy: An explanation of how big data application is used in education to contribute to literacy hence working towards SDG 4.
- Big Data: a tool to achieve sustainable development: A brief document prepared by Cepei that explains what big data for development is, what are the advances and challenges of big data for development in Latin America, and how Colombia is approaching the use and production of big data.
Conclusion
Big data and the 2030 Agenda mean achieving the Sustainable Development Goals using big data storage, mining, analytics, and visualization tools. In such a process, monitoring of progress on the Agenda could be tracked by relying on non-traditional data sources, which can support decision-making on a specific target or SDG indicator, considering data privacy. Cepei believes that if we acknowledge big data privacy issues, we can better: track and achieve the 2030 Agenda by seizing the opportunity big data presents and also by creating multi-stakeholder partnerships. If we are all convinced of the importance of big data for sustainable development, it is then our responsibility to promote big data use for access to analytical and storage tools to be open for all.