Traditional and non-traditional data sources for sustainable development
Research and monitoring of the 2030 Agenda require data sources to support hypotheses, highlight findings and generate knowledge for better decision-making. In the fields of statistics and information gathering, traditional and non-traditional data sources are essential.
Due to the excess of data, new opportunities have been provided to connect with the dynamics of sustainable development. This implies having a definition of the types of data that can be found and their contribution to closing information gaps.
Types of data sources
Traditional data are those that come from sources such as surveys, censuses and administrative records with statistical potential. This is connected with the work of government organizations that generate and use statistical information and are within the framework of the National Statistical Systems coordinated by Statistical Offices.
On the other hand, non-traditional information sources come from earth observation (satellite images); Mobile telecommunications (call records); social networks (sentiment analysis), and citizen-generated data (civil society data). Many of these are considered big data, large volumes of unstructured information that require new capacities for their analysis.
In recent years there has been a need for data from these two types of sources. Discussions about what official statistics are currently measuring and the types of data being used for these measurements, have been taking place at the Statistical Institutes of different countries.
Non-traditional data sources for monitoring the Sustainable Development Goals
The 2030 Agenda and the Sustainable Development Goals (SDGs), opened the debate about using non-traditional data sources to measure this ambitious agenda, to speed up reporting and the possibility of measuring variables not considered by official statistics. In the document A World that Counts (2014), the following is stated: “The integration of these new data with traditional data to produce more detailed high-quality information, timely and relevant for multiple purposes and users, and especially to promote and monitor development sustainable". In other words, non-traditional data sources become an opportunity to complement official monitoring carried out by the statistical community
An example on the use of traditional and non-traditional data sources is the work carried out by the National Administrative Department of Statistics (DANE) of Colombia: From the use of satellite images, data is obtained by performing geoprocessing, as well as administrative records and other sources for the calculation of Indicator 11.3.1, Ratio of land consumption rate to population growth rate. This demonstrates that new sources of information are useful to close data gaps.
Our contribution through DataRepública
At DataRepública we present all kinds of data sources, and provide evidence of the opportunities we have to close information gaps in the measurement and monitoring of the 2030 Agenda and the SDGs. We do this through:
Mapping data sources from different stakeholders, among which are the public sector with traditional sources; and civil society, multilateral organizations, the private sector and academia with non-traditional data sources.
Strengthening capacities and encouraging data science for the research, collection, processing, analysis and dissemination of both traditional and non-traditional data sources.
Creating data stories on SDG themes that use both types of data and show the complementarity that exists between them.
Analyzing different variables of the SDG indicators with governments, multilaterals and other non-traditional data sources, to present alternative data sources for measuring the 2030 Agenda.
An example of the above is the data story Reconstruyendo Mocoa a través de los datos, a collaboration between Cepei, Telefónica Movistar and LUCA, which shows the migratory flows (internal and external mobilization processes) that occurred in Mocoa, Colombia, during and after the torrential avenue of 2017 through the analysis of mobile data. This story highlights the challenges involved in risk management and natural disasters response, whose impact affects a large number of SDGs, as well as highlights the importance of building multi-stakeholder partnerships, in line with SDG 17, for successful decision-making.
DataRepública seeks to bring users closer to data to monitor sustainable development.
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