The Results Data Initiative Has Ended, But We're Still Learning From It

Vinisha Bhatia-Murdach is Senior Learning Advisor and has been with DG since 2015. During her time she has worked on a number of programs and projects, which have been crucial to the growth and evolution of DG. One of the projects that has shaped our understanding of navigating data ecosystems and designing tools that focus on data use was the Results Data Initiative (RDI).

If an organization with an existing culture of learning and adaptation gets lucky, and an innovative funding opportunity appears, the result can be a perfect storm for changing everything. The Results Data Initiative was that perfect storm for DG. It allowed us to learn in-depth how results data – information about outputs and outcomes from programs and policies – are collected, managed, and used. Specifically, we gained insights on how a diverse set of actors from front line medical workers and national policymakers to international development agencies each play a role in collecting, managing, and using data; giving us a better understanding of how DG and the wider development community can improve the use of data in decision making. In some ways, RDI humbled our team, by confirming that simply building technology and supplying data is not enough to ensure data is actually used.

RDI allowed us to test some of our assumptions and develop new solutions, methodologies, and approaches to more effectively implement our work. In particular, DG’s Custom Assessment and Landscaping Methodology (CALM), our approach to co-designing with users, and our work to promote strategic data use with development agencies and the development community writ-large are all the result of RDI. From the perspective of the rear-view mirror, it is clear just how important this lucky combination of factors was in shaping our work.

What is RDI?

Launched in 2015 in partnership with the Bill & Melinda Gates Foundation, this five year, two-phase program worked with four country governments and two development agencies – Ghana, Malawi, Sri Lanka, Tanzania, Global Affairs Canada and UK’s DFID – to address critical barriers to results data use. In phase 1, we focused on research and exploration, and in phase 2 we focused on applying our lessons learned. 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. 

  "What can we do better?" exercise from an RDI co-design workshop
  Exercise from an RDI co-design workshop

Outcomes of RDI 

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: 

  1. Sponsor technologies that promote data use – not just data reporting. During our RDI interviews, we heard multiple times that nearly all the data related technologies being used at the subnational level were for data reporting. Acting on these lessons, we developed an Open Contracting system for the Government of Makueni County, Kenya. As part of the technical development process, we have worked with local Project Management Committees, which are citizen groups who monitor project implementation. These citizen groups were part of our co-design process, and their monitoring reports are also included in the system. Our goal was to develop a system that reflects the user needs of both government and community stakeholders.
    Example from our current work: Makueni Open Contracting 
     
  2. Generate local-level outcome data. Frontline workers reported that they are rarely asked to collect outcome level data, as highlighted by this quote from Agriculture Extension Officer “If someone modifies livestock farming, builds a good house, and takes children to school after selling crops, [increases] from one meal to three meals… These are among changes that you can notice on the farmer though we rarely reach that level. [However,] the nature of data we access most of the time does not track to outcome level...”
    Example from our current work: Des Chiffres et Des Jeunes support to health clinics
     
  3. Respond to local data demands (this paved the way for development and publication of DG’s Custom Landscaping and Assessment methodology, CALM to facilitate use to systematically identify local demand and design tools fit-for-purpose). We used CALM to conduct data landscaping studies for several UNICEF Country Offices, with the aim of identifying ““smart demand” of data -- collecting information with specific decisions and problems in mind”
    Example from our recent work: UNICEF Country Data Strategy Development

Through our policy and advocacy efforts, we have sought to elevate these findings to the international level, while simultaneously applying the lessons to our own programming through phase 2 of RDI and beyond.

In phase 2 of RDI we expanded and further developed our data landscaping methodology 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 codesign tools that increase results data use within the Governments of Malawi and Tanzania.

RDI and Its Impact on Future Work

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. At the end of phase 2 of RDI, we received additional funding from Gates Foundation to expand the data strategy work already started and to support agencies to better use data to make decisions. We continued working with Global Affairs Canada and UK’s DFID to publish the Decision Making and Data Use Landscaping Study, which again emphasizes the need to consider a user's role in decision making – and to leverage data to support development of sector and country-specific analysis to meet user needs. Our expanded network enabled us to facilitate several knowledge exchanges between these agencies and the Icelandic Development Agency, Swiss Development Cooperation, USAID, MCC, UNICEF, and SIDA. We continue to make these linkages between practitioners and facilitate cross-agency learning, and have recently 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. During the course of RDI, we published the white paper Understanding Data Use and Managing for Feminist Results, which outlines the challenges and opportunities that development agencies may face when adopting new policies, and provides an overview of how other development agencies can integrate a feminist lens into their M&E frameworks. 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. In addition, we led a closed-door consultation around measuring feminist international assistance policies with representatives from Global Affairs Canada, the embassies of the United Kingdom and Australia, the Millennium Challenge Corporation, Oxfam, Publish What You Fund, the International Center for Research on Women, Data2x, Cooper/Smith, the Center for Global Development, and the Council on Foreign Relations. 

Malawi's Data Ecosystem
Mapping Malawi's Data Ecosystem

3. Refined co-design strategies

RDI allowed us to experiment with methods for co-design and co-creating, ultimately to develop tools that meet the end-users’ actual needs. While refining our co-design process, we adapted our approach and learned several valuable lessons on how to link the tools we develop with the end-users’ needs for decision-making. 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 co-design the Conceptual Framework For the National Agriculture Management Information System (NAMIS) with the government of Malawi

We’re Still Learning

Looking back on the last five years of evolution resulting from RDI, it is clear that the program was a perfect fit for DG’s extensive background in aid effectiveness, open data, and our organizational culture of learning. It allowed us to reflect on how we practice technical development and support our partners use data for decision making.  Although the RDI grant has technically come to end, its impact on our work continues, and the culture of learning and adapting at DG is as strong as ever.

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