DIGITAL GOVERNANCE
Results Data Initiative
Increasing the Impact of Results Data.
Launched in partnership with the Gates Foundation, the Results Data Initiative (RDI) set out to address critical barriers to results data use. The program worked with four country governments and two development agencies – Ghana, Malawi, Sri Lanka, Tanzania, Global Affairs Canada and the UK’s DFID. Working with governments, the initiative revealed that data systems were often designed for reporting rather than for use. RDI’s findings shaped DG’s approach to building data systems that are demand-driven, decision-focused, and grounded in the realities of those who rely on them.
While the first goal of RDI was to elevate the results focus of a few governments and agencies, our broader program goal was to provide real examples for how the “Data Revolution” can improve development policy and practice. DG created a combination of tools, datasets, resources, and approaches to help dynamic officials in each partner institution enable data-driven decisions.
During phase 1of RDI, we focused on how data are collected, managed, and used in the health and agriculture sectors in Tanzania, Ghana, and Sri Lanka. We published a policy brief “How Should The Development Community Invest In Results?”, which highlighted three main recommendations for the broader development and ICT communities: sponsor technologies that promote data use – not just data reporting; generate local-level outcome data, and respond to local data demands.
In the second phase we expanded and further developed our data landscaping methodology known as CALM to inform strategies for increasing the use of results data within development agencies such as DFID and Global Affairs Canada. Before RDI, our assessments were largely focused on identifying the technical and data requirements. With CALM, we are able to probe deeper into where the tool will live in the broader ecosystem, who will be using it, which decisions they will be making, and what information they will need to make that decision. CALM was developed as a user-centered design methodology, which puts the need to make decisions at the center of the tool(s), policies, and data governance models being designed. Keeping in mind the decision spaces of our target users, we aimed to co-design tools that increase results data use within the Governments of Malawi and Tanzania.
impact
Through RDI, Development Gateway worked to better understand how local-level development actors actually collect, share, and use results data to inform development programs. After speaking with over 450 government officials, donor representatives, and project implementing staff in four countries (Ghana, Malawi, Sri Lanka, Tanzania), we were able to put what we learned into action.
RDI has been influential in three key focus areas for DG.
1. Expanded data strategy network: Through RDI, we were able to develop a rich network of results-oriented development practitioners with development agencies, which has been instrumental in our data strategy thematic area. We continue to make these linkages between practitioners and facilitate cross-agency learning, and have supported multiple agencies and INGOs in developing internal data strategies.
2. Expanded policy work: The lessons we learned through RDI built on our prior fifteen years of project work, and enabled us to test and refine feedback from DG programming into policy papers, guidance, and strategy and to disseminate them with the development community. Our policy engagement also included working with the OECD Development Cooperation Directorate on research into the use of the Sustainable Development Goals (SDGs) as a shared results framework between development cooperation providers and partner countries.
3. Refined co-design strategies: RDI allowed us to experiment with methods for co-design and co-creation, ultimately to develop tools that meet the end-users’ actual needs. In Malawi, for example, we developed a data ecosystem map using CALM, which provides a comprehensive picture of the Malawi agriculture sector, highlighting which data are currently gathered and used by which actors. Once we had a full picture of the ecosystem, we used it to co-design the Conceptual Framework For the National Agriculture Management Information System (NAMIS) with the government of Malawi.
These learnings were an important stepping stone for how DG understands barriers to data use, and have fed into our approach to other programs as well.