Re-imagining MEAL Under Localisation: A Systems and Governance Perspective
A post by Peter Mureithi Kariuki
Localisation has become one of the most significant shifts in contemporary humanitarian and development practice. At its core, localisation refers to the intentional transfer of power, resources, and leadership to actors within the communities and countries affected by crises. It emerged from long-standing critiques that the international humanitarian system, despite its objectives, has often operated in ways that marginalise local capacities, overlook contextual knowledge, and concentrate decision-making among external organisations. Over the past decade, global commitments such as the Grand Bargain and the Charter for Change have pushed the sector to recognise that sustainable, ethical, and effective responses must be led by those closest to the challenges. Localisation, therefore, is not simply about working “with” local actors; it is about rebalancing governance structures, funding flows, and knowledge systems so that local institutions and communities shape priorities, define solutions, and maintain ownership of outcomes.
This shift carries profound implications for Monitoring, Evaluation, Accountability, and Learning (MEAL). Traditionally, MEAL functions have often been designed and controlled by international NGOs and donors, serving upward reporting requirements more than local decision-making needs. As localisation advances, MEAL must undergo a fundamental redesign, a transformation not only of tools and processes but of governance, authority, and epistemology. A localised MEAL ecosystem does not merely relocate tasks; it restructures who defines evidence, who owns data, and who interprets findings. It reimagines MEAL as a locally governed knowledge infrastructure that supports community agency, strengthens institutional legitimacy, and aligns humanitarian action with contextual realities.
In this paradigm, MEAL systems must be conceived and led by local actors. National NGOs, community structures, and government agencies, not international partners should shape theories of change, select indicators, and determine methodologies that reflect social, cultural, and political realities. Donors may still articulate strategic objectives and provide financing, but they no longer dictate metrics or enforce externally defined measurement frameworks. Expatriate technical experts transition from directive roles to facilitative ones, supporting interoperability, system design, and quality assurance rather than controlling data generation or analysis. Technologically, this calls for modular architectures in which platforms such as Kobo, DHIS2, and CommCare feed into locally hosted data systems through secure APIs, ensuring both integration and data sovereignty.
Localisation also requires a new approach to data governance. Instead of international agencies retaining default ownership of data, local institutions become custodians of both raw and processed datasets. External access is granted through formal agreements rather than assumed entitlements. Ideally, data infrastructure is hosted within national jurisdictions and complies with local data protection laws. External specialists support capacity building, but local analysts lead data engineering, dashboard development, and interpretation. Such distributed governance embeds accountability at the source while preserving analytical rigour.
Monitoring becomes decentralised and participatory when rooted in localisation principles. Continuous data flows are generated by local enumerators, civil society organisations, and community groups, often using mobile-based tools. Indicators are not static but evolve through regular community reflection sessions that validate insights and refine measurement priorities. Data quality assurance draws on both quantitative outputs and participatory verification methods such as community scorecards and sense-making workshops. Donors and international actors play a supportive role by funding digital infrastructure and maintaining quality standards, while stepping back from operational monitoring responsibilities.
Evaluation also undergoes a structural transformation. Local research institutions and evaluation firms design and lead evaluation processes, supported by inclusive governance boards that bring together donors, authorities, and community representatives. These bodies collectively define evaluation questions and approve terms of reference. Methodologically, evaluation models blend rigorous external approaches with participatory and utilisation-focused methods, ensuring contextual relevance and ownership. Expatriate evaluators shift into roles centred on methodological peer review, ethical guidance, and capacity strengthening rather than principal investigation.
Accountability systems follow the same trajectory, moving away from compliance-driven, upward reporting toward community-centred mechanisms. Feedback channels, ranging from public forums to digital tools such as WhatsApp and IVR are integrated into locally owned case-management systems that support timely and transparent responses. Issues are resolved at the closest appropriate level, with communities informed of the outcomes. Public-facing transparency dashboards reinforce trust by sharing aggregated accountability trends. Donors recognise these mechanisms not as optional add-ons but as essential governance infrastructure deserving of sustained investment.
Learning becomes a collective and iterative process in this model. Instead of producing reports for external stakeholders, learning activities are embedded in local ecosystems. Networks of community actors, civil society, and government agencies convene regularly to synthesise insights and adapt programming. Evidence is translated into formats tailored to local decision-making processes, and digital knowledge hubs hosted within national systems preserve lessons, evaluation findings, and geospatial data for cross-project learning. Donors and international partners play an enabling role, supporting interoperability and facilitating horizontal learning exchanges without appropriating local knowledge.
The success of localisation hinges on long-term investment in local MEAL capacity. Donors must fund core systems rather than project-specific MEAL budgets, ensuring stable infrastructure and institutional resilience. Tiered capacity models allow expatriate experts to mentor and guide while local MEAL professionals lead operational analysis, reporting, and learning. National professional networks strengthen ethical and technical standards, creating pathways for sustained professional growth and reducing dependency on external expertise.
Ultimately, localisation reshapes the power dynamics that have long defined humanitarian and development evidence systems. Communities are no longer passive sources of data but active agents defining what matters, interpreting meaning, and shaping solutions. Local organisations own and govern the evidence that influences their futures. Donors evolve into system stewards and enablers rather than compliance enforcers. Expatriates provide scaffolding rather than control, contributing to systems that are technically robust, ethically grounded, and structurally devolved. Localised MEAL is therefore not merely MEAL conducted at the local level, it is a reimagined governance model that places authority where it rightfully belongs: with the people and institutions closest to the context.
Peter Mureithi Kariuki is a Monitoring, Evaluation, Accountability, and Learning (MEAL) expert dedicated to strengthening evidence-driven decision-making in development and humanitarian programs. He brings experience designing MEAL systems, leading evaluations, and building partner capacity across Africa and Asia. Peter is passionate about localisation and advancing data governance that empowers communities. He is an Anthralytic collaborator and occasional contributor, providing thought leadership through writing, research, and practical tools that improve program quality and impact.
Anthralytic builds MEAL systems where the people closest to the work own the data, define what matters, and use what they learn.
