If properly catalogued, classified and shared, public sector data can be leveraged to increase access to services, make service delivery more efficient and inform policymaking. Real-time data on traffic and urbanisation can inform urban planning efforts including mobility, energy and water consumption, for example. Combining machine learning techniques with access to data can enable solutions for predicting the incidence of malaria, forecasting maize productivity or predicting microgrid electricity consumption, among many other things.  

Cenfri hired a team of consultants to work with the Government of Rwanda (GoR) – specifically the Rwanda Information Society Authority (RISA),  the Ministry of Education (MINEDUC), the Ministry of Agriculture (MINAGRI) and the Ministry of Finance and Economic Planning (MINECOFIN) in three domains:

• Data cataloguing (Module 1)
• Data classification (Module 2)
• Data sharing (Module 3)

While this consultancy assignment responded to the context in Rwanda, the needs of the government institutions with whom we worked, and specific programme objectives, these guidance notes are likely to be of use to any public sector entity that is starting out on its data-for-decision-making journey. The datasets, examples and use cases referenced in these documents are for indicative purposes and should be substituted with whatever is appropriate in the context in which these data cataloguing, classification or sharing principles are being applied.

For more information on this project, contact Marcellin Nyirishyaka.

Rwanda Economy Digitalisation Programme is implemented by Cenfri in partnership with the Rwandan Ministry of ICT & Innovation and financial support from the Mastercard Foundation.