Bonn: German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE)
Dt. Ausg. u.d.T.:
Daten für Entwicklung: eine Agenda für die deutsche Entwicklungszusammenarbeit
(Analysen und Stellungnahmen 4/2018)
Data is a central but underestimated prerequisite for the realisation of the 2030 Agenda. Although technical innovations such as smartphones or the internet of things have led to a data explosion in recent years, there are still considerable gaps in the availability and use of data in developing countries and development cooperation (DC) in particular. So far it is not possible to report regularly on the majority of the 230 indicators of the Sustainable Development Goals (SDGs).
Already in 2014 an independent panel of experts, called for nothing less than a data revolution to support the implementation of the SDGs in their 2014 report to the UN Secretary-General, A World that Counts. Data is one of the key requirements for planning, managing and evaluating development projects and strategies. The aim of the data revolution for sustainable development is 1) to close data gaps with the aid of new technologies and additional resources, 2) to strengthen global data literacy, promote data use and enable equality of access, 3) to create a “data ecosystem” that follows global standards in order to improve data quality, enable data aggregation and prevent abuse.
The data revolution for sustainable development is a challenge for all countries. There is a lot of room for improvement in both partner countries and all areas of German policy making. This paper focuses on German DC.
Overall, the subject of data has to date received little attention in the organisations of German DC and their projects. The demand for evidence- and data-based work is often limited to evaluation.
A results framework to support portfolio management in German DC does not exist. Monitoring at project level is often not sufficient, as data quality is frequently poor and capacity is lacking. In the partner countries the implementing organisations (IOs) often introduce parallel structures for monitoring and evaluation (M&E) in order to keep track of the measures implemented, instead of using and strengthening national statistical systems as much as possible. Collected data and project progress reports are usually not published.
The following recommendations can be derived from the analysis:
German DC should agree on common data standards and principles for data use, such as Open Data by Default. At the same time, personal rights should also be ensured.