The Global Partnership for Sustainable Development Data is a global network of governments, NGOs, and businesses working together to strengthen the inclusivity, trust, and innovation in the way that data is used to address the world’s sustainable development efforts.
Eradicating poverty is hampered by unreliable or non-existent data and the lack of skills and incentives to use it. The Global Partnership on Sustainable Development Data (GPSDD) addresses this challenge by improving the effective use of data, filling key data gaps, harmonizing data specifications and architectures, expanding data literacy and capacity, increasing openness and leverage of existing data and mobilizing political will and resources. The GPSDD funded project “Building a Data Collaborative” sets out to do so in the Democratic Republic of Congo (DRC) and Malawi and seeks to enable better targeted action towards reaching the health and WASH SDG targets.
The Netherlands Red Cross is with its humanitarian data initiative 510 one of the front runners in promoting the use of data in the Red Cross movement. NLRC is the overall project manager, implementer in Malawi with the Malawi Red Cross and is responsible for the methodology, technical support and M&E.
CartONG is a French NGO supporting humanitarian and development actors with information management and data analysis, and promoting data sharing. CartONG works together with DRC’s OpenStreetMap community and Médecins Sans Frontières-Switzerland in DRC.
This project will create a Data Collaborative, a semi-formal, long term collaboration, that brings volunteer & technical communities (VTCs) and in particular OpenStreetMap communities, humanitarian and development organizations together with National Statistics Offices, businesses, and other civil society organizations.
The Data Collaborative will:
Simultaneously, the project will pilot a new methodology to monitor a specific set of SDGs indicators, (3.3; 3.8; 6.1; 6.2). The pilots in both DRC and Malawi will combine official with non-official data. This includes field mapping and remote, crowdsourced data collection (through the Missing Maps workflow).
To learn more about this data collaborative project and its lessons learned, check out the detailed news that was published following the end of the project.