A key fix for aid is to unify fragmented data

The humanitarian and development sectors are facing an unprecedented challenge: A crisis of data fragmentation. In an age where digital technology allows for instant communication and analysis, the data that drives aid, policy, and recovery efforts remains largely siloed, outdated, and difficult to verify. This information gap leads to delayed responses, misallocated resources, and a fundamental disconnect between decision-makers and the communities they aim to serve. To move beyond this paradigm, the global development community must embrace a new data architecture — one that is API-first, human-in-the-loop, and built on a foundation of collaboration. The problem isn’t a lack of data; it’s a failure to manage it effectively. In post-conflict or disaster-affected areas, a multitude of groups — from large NGOs to small local organizations — collect critical information on everything from infrastructure damage to population displacement. Yet, this data often resides in disparate spreadsheets, local servers, or proprietary systems. Without a unified way to access and verify this information, a humanitarian coordinator in Geneva may have to spend days or weeks sifting through a patchwork of unreliable reports. This information lag is not just an inefficiency; it is a direct obstacle to achieving impact. To fix this, we must shift our focus from the “why” of data sharing to the “how.” The solution isn’t another database; it is a fundamental change in how we think about data access. By adopting an API-first approach, we can move from a model of reactive file-sharing to a system of proactive, programmatic data exchange. An API — application programming interface — acts as a universal translator, allowing different systems to speak to each other seamlessly. This enables a local NGO to input on-the-ground data, which can then be instantly and securely accessed by a remote policy team. This eliminates data silos by design, making information a fluid, real-time asset rather than a static report. Crucially, an API-first approach also facilitates the integration of human-in-the-loop verification. In the volatile environments of crisis and conflict, data from digital sensors or social media streams can be unreliable. It needs to be validated by local experts who possess vital contextual knowledge. A technology platform that integrates human verification into the data pipeline ensures that every piece of information is not only fresh but also credible. This process builds trust and provides a layer of integrity that is essential for high-stakes decision-making. For instance, in a project mapping critical infrastructure in Lebanon, a platform such as CivAPI could ingest data from various sources but would require a local expert to verify the accuracy of a building’s status before that data is disseminated to the wider community. This blend of cutting-edge technology and on-the-ground human intelligence is the key to creating truly useful humanitarian data. The most successful data-driven solutions are not built by a single entity, but by collaborative ecosystems. The traditional model, where a central organization dictates data standards, is often slow and ill-suited to the needs of local groups. Instead, we need to create platforms that actively foster community-led data creation. The work of Humanitarian OpenStreetMap Team, or HOT, and its collaboration with organizations such as the Lebanon Reforestation Initiative, or LRI, are powerful examples of this principle in action. By engaging local communities in “mapathons” and training them in geospatial data collection, we not only gather high-quality data but also build capacity and promote local agency. This data is then fed into platforms and made accessible via APIs, creating a self-sustaining cycle of innovation and impact. For global development practitioners, the path forward is clear. It requires a commitment to three key actions: 1. Adopt an API-first mindset: Challenge your teams and partners to move beyond traditional data exchange methods. Ask how your data can be made available programmatically and in real time, not just as a downloadable file. 2. Insist on data verification: Make data quality a nonnegotiable part of your strategy. Prioritize solutions that integrate human-in-the-loop verification, especially in projects where accuracy directly impacts lives. 3. Invest in collaborative ecosystems: Instead of funding top-down data projects, seek out and support platforms that empower local players. The most valuable data often comes from those closest to the problem. The future of humanitarian and development work hinges on our ability to transform a fragmented data landscape into a unified, reliable, and collaborative ecosystem. By embracing this approach, we can move from simply reacting to crises to proactively building resilient, data-driven societies.