Kenya’s aid information management: The data landscape

There is a substantial demand for external finance data in Kenya and in many other African countries. This is driven by concerns about the country’s debt sustainability, overall economic landscape, investment choices and the need for monitoring both public and private sector performance.

Kenya acknowledges that the effective management of aid data is essential for driving development and ensuring accountability, as highlighted in the National Treasury's "Effective Development Co-operation Strategic Plan (2018-2022)." ^[1] The plan underscores the importance of focusing on results rather than inputs, emphasising that reliable and accessible data is crucial for measuring progress and fostering transparent, accountable partnerships. In the face of complex global challenges, ^[2] such as the economic aftereffects of Covid-19 pandemic and geopolitical shifts, the demand for accurate, accessible and well-governed aid data is more crucial than ever. These crises have disrupted global supply chains, heightened economic instability, and deepened existing inequalities, particularly among vulnerable populations. Consequently, the ability to make informed decisions and allocate resources effectively in the aid sector hinges on the availability of reliable and up-to-date data. Various stakeholders, including government entities, businesses, investors, media and researchers, rely on aid data to inform their decisions and ensure that assistance reaches those most in need.

This paper is part of a pioneering series of data landscaping reports from Development Initiatives (DI), covering Kenya and Uganda . The methodology for the study used DI’s established data landscaping approach, qualitative research methods and key informant interviews.

• Section one provides an overview of Kenya’s national digital administrative data management: the collection, availability and accessibility of aid-related data.
• Section two considers the legal and institutional framework surrounding the management of aid data in Kenya, including national plans, policies and coordination mechanisms.
• Section three looks at the data systems used for aid management and identifies the key stakeholders involved in the management of aid data: how government ministries, departments and agencies track and manage aid-funded projects and programmes in Kenya.
• Section four considers how well foreign aid systems integrate with domestic financial management platforms.
• Section five identifies challenges of deploying, maintaining and using aid information management systems in Kenya.

The findings of the aid data landscape study in Kenya highlight several key aspects and challenges related to the management, coordination, and accessibility of aid-related data. The main findings are:

1. Fragmented Data Systems: Kenya's aid data landscape is characterised by a fragmentation of data systems across different government ministries, departments, and agencies (MDAs). This fragmentation makes it challenging to obtain a unified and comprehensive view of aid flows and projects. Different systems are not well-integrated, leading to inconsistencies and inefficiencies in data management.
2. Lack of Interoperability and Standardization: The study found a lack of standardisation and interoperability between existing data management systems, such as the Integrated Financial Management Information System (IFMIS), Aid Information Management Systems (AIMS), and other sector-specific databases. This lack of integration hampers data sharing, analysis, and coordination among stakeholders.
3. Limited Accessibility and Usability of Data: While several data management platforms exist, access to data is often limited to certain government officials or departments. Public access to aid data is also restricted, with few data sets being openly available. This limits the ability of non-state actors, such as civil society organizations (CSOs), development partners, and the public, to engage in evidence-based decision-making, accountability, and monitoring.
4. Inconsistent Data Quality and Timeliness: The quality and timeliness of aid data vary significantly across different sources. Data often lacks completeness, accuracy, and timeliness, making it less useful for planning, decision-making, and monitoring purposes. Challenges in data collection and reporting processes, including insufficient resources and capacity, contribute to this inconsistency.
5. Limited Use of Existing Aid Data Management Systems: The study found that existing aid data management systems, such as AIMS, are underutilized by MDAs and development partners. There is a lack of awareness and capacity to use these systems effectively, leading to a reliance on manual processes and ad hoc data management practices.
6. Weak Coordination Mechanisms: The coordination of aid data management between state and non-state actors is weak. There is limited collaboration among government entities, development partners, and other stakeholders in terms of data sharing and alignment of data management practices. This lack of coordination leads to duplication of efforts, inefficiencies, and missed opportunities for leveraging data for development outcomes.
7. Capacity Gaps in Data Management: There is a need for capacity building in data management skills across various government agencies and departments. Many government officials lack the necessary technical skills to effectively manage, analyse, and use aid data. Additionally, there is a need for more resources and support to strengthen data management infrastructure.
8. Opportunities for Improvement: The study identified several opportunities for improving the aid data landscape in Kenya, including the development of a unified aid data repository, greater standardisation of data collection and reporting processes, and increased use of digital platforms to enhance data accessibility and transparency. The study also emphasised the importance of fostering collaboration among stakeholders to promote data-sharing and integration efforts.

Based on the findings of this study, we offer the following policy recommendations:

1. Integrate finance systems: Integration of domestic and aid management systems and processes is vital. This integration will streamline financial reporting and aid tracking, minimising discrepancies and ensuring that all financial resources, both state and non-state, are accounted for comprehensively.
2. Establish comprehensive development finance tracking and aid data management: A unified system for tracking all development finance, encompassing both state and non-state actors, is crucial. This comprehensive approach will prevent duplication of efforts, improve resource allocation, and facilitate better targeting of development projects. Additionally, creating a comprehensive aid data repository that includes information on disbursements, projects and donors is essential. This repository should be accessible to the public, development partners and government agencies to enhance transparency and accountability.
3. Improve coordination: Strengthening coordination mechanisms is crucial for effective aid management. The National Treasury, in collaboration with relevant ministries and counties, should establish clear lines of communication, streamline inter-agency committees and foster cooperation among stakeholders. Improved coordination will ensure that aid aligns with national development priorities and is efficiently utilised to achieve desired outcomes.
4. Build capacity: Government staff should be equipped with the necessary data skills and tools through capacity building programmes. This upskilling will empower them to effectively manage and utilise aid-related data, thereby enhancing decision-making processes and accountability.