The Global Fund Data Explorer
Global Health · Data Visualisation · Lead Design
Visualising the global fight against AIDS, tuberculosis, and malaria, making $65 billion in investments publicly explorable for governments, civil society, journalists, and the public.
The Global Fund Data Explorer is live.
00 / Metadata
2024 – Present
•
Built four dataset pages from the ground up. Ongoing design across the wider platform, first launched 2019.
01 / The Problem
Billions in global health funding. No single place to understand them.
Prior to the Data Explorer, The Global Fund's data existed across technical files, downloadable datasets, and a document-heavy Grant Portfolio this was presenting things in a way that prioritised internal reporting over public access. If you were a journalist trying to trace funding to a specific country, or a civil society group checking your country's eligibility status, you'd need to download spreadsheets and cross-reference them manually.
The data wasn't hidden. It just wasn't designed for anyone outside the organisation to actually use.
I lead design on the platform that changed that. The dataset pages (where each raw dataset becomes something you can actually move through) I built from scratch. The rest of the platform I've designed and maintained alongside them. An interactive, public-facing explorer that lets people move between pledges, grants, results, eligibility, and geographic breakdowns without needing to understand the data model underneath.
02 / The platform
Six data dimensions. One coherent experience.
The Data Explorer covers resource mobilisation (where funding comes from), access to funding (who's eligible and how allocations work), financial insights (how grants are disbursed), annual results (what the funding achieves), grants (individual grant performance and documents), and geographic location (country-level entry point into everything else).
Each of these has its own data logic, its own update schedule, and its own primary audience. Resource mobilisation data is relevant to donor governments. Eligibility data matters to recipient countries. Grant performance matters to implementing organisations. The interface needed to hold all of them in one navigation without making any single user type wade through the others to find what they need.
Resource Mobilisation.
Access to Funding.
Financial Insights.
Annual Results.
Geographic Location.
Grants.
03 / Audience Archetypes
The same data, read four different ways.
There's no single user of this platform. A policy analyst at a health ministry arrives wanting cycle-over-cycle funding trends. A journalist wants to verify whether money is reaching the highest-burden regions. A civil society org wants to know if their country qualifies for the next allocation round. A member of the public wants to understand what The Global Fund actually does.
They're all looking at the same dataset. They need completely different things from it.
health ministry
Cycle-over-cycle funding trends
Resource
Mobilization / Financial Insights
Show comparable figures across cycles without implying continuity the methodology breaks
Journalist
investigative / data desk
To verify money reaches the highest-burden regions
Annual Results / Geographic Location
Make the path from a country to its grants traceable, and quotable without distortion
Civil society org
recipient country
To know if their country qualifies next round
Access to Funding
Surface eligibility and allocation logic without assuming knowledge of the funding model
no prior context
To understand what the Global Fund does
Homepage
Offer a low floor. Orientation before depth, no jargon to clear first
04 / How it fits together
A connected part of a larger whole.
The Data Explorer doesn't exist in a vacuum. It was designed to sit on top of the Global Fund Data Service API, while sitting alongside the Global Fund website, the Results Report, and the Data Service downloads. Knowing what already existed (and where the actual gaps were) shaped what the Explorer needed to be.
It also meant designing for upstream constraints. The data comes through a middleware layer that aggregates from the Data Service. Quality varies across publishers. Update cadences differ between datasets. The interface absorbs all of that without pretending the data is cleaner than it is.
Global Fund Ecosystem Map.
05 / The hard part
When the data changes shape, the chart has to change too.
The hardest design problem on this platform wasn't layout or navigation. It was representation.
Some of the datasets we work with don't behave the way people expect data to behave. The way something gets measured, categorised, or reported can shift between cycles (not because anyone made an error, but because standards evolve). When that happens, a number from three years ago and a number from today might look comparable but aren't.
If the interface doesn't account for that, it tells a false story. And on a platform used for public accountability, a false story isn't a minor UX issue.
A significant part of my work on this project has been figuring out which visual forms are honest enough to carry the data they're given, and which ones imply more continuity, more comparability, or more certainty than actually exists.
A chart implies a story whether the designer intends it to or not. On a platform where most readers never open the methodology notes, the visual form does the interpretive work. Sometimes the most honest design move is to change the chart, not add a disclaimer.
06 / Key Design Decisions
Two choices that shaped the product.
Chosen
Cycle-discrete visual forms for metrics whose methodology evolves
For metrics whose underlying definitions shift across reporting cycles, we avoided any chart form that implies temporal continuity. Each cycle appears as its own unit — the visual grammar itself carries the caveat that footnotes often fail to.
Alternative Considered
Continuous line charts with inline annotations at each methodology boundary. Technically more information-dense, but created anxiety for non-expert users before they'd engaged with the underlying data, and tested poorly in early user sessions.
Chosen
Switchable chart types via dataset-level controls
Users select their visualisation mode — line, bar, Sankey — at the dataset page level. This puts the analytical choice with the user rather than the designer, supporting multiple valid interpretations of the same data.
Alternative Considered
Fixed chart type per dataset — simpler to implement, but would have required a separate page per analytical mode, fragmenting the experience and multiplying the maintenance surface.
07 / Outcomes
This platform doesn't track its users, but there are the traces it left anyway.
The Global Fund Data Explorer does not track its users. It is an open data tool. And for this kind of tools impact isn't sesion or page views. We can measure it's impact by looking into who trusts the data enough to cite it, build on it, and route the public through it. Here I listed all the impact my work has done on Global Fund Data Explorer.
Adopted
Cited as a primary data source by KFF (The Kaiser Family Foundation), and referenced in the Global Fund's own results reporting.
Benchmarked
The cycle the expanded financial data launched, the Global Fund reached the top of the "Good" tier in the Aid Transparency Index, up more than 10 points since 2022.
Canonical
Became the public route into Global Fund grant data, retiring the document-heavy Grant Portfolio it replaced.
Redistributed
Picked up and redistributed by humanitarian information services, including ReliefWeb.
The four dataset pages behind these outcomes (Resource Mobilization, Access to Funding, Financial Insights, Annual Results) I built from scratch.
08 / The Relationship
Still building, still learning the data.
Most case studies have a clear end date. This one doesn't.
I've been designing within this data model for over three years now. That means I understand things a brief can't communicate (why a category was renamed, what a metric actually measures versus what it looks like it measures, where the platform needs to go next). The Global Fund team trusts the design process because they've seen it compound over time: patterns designed once, refined through real use, not specced in isolation.
09 / Reflections
What this project taught me.
01
Data integrity is a design problem, not a data-engineering one.
The misinformation risk on this project wasn't hypothetical. A misleading chart form could inform a policy decision based on a false picture. That changed how I evaluate every visualisation I design.
02
Change the chart, not the footnote.
When annotations can't reliably carry the caveats a dataset needs, the response has to happen at the level of form. Pick a visual grammar that refuses to tell the wrong story.
03
Letting users switch chart types isn't a feature, it's a position.
There's no single correct view of a complex dataset. The designer's job is to enable good questions, not present fixed answers.
04
Long-term client relationships produce better design.
Knowing the full history of a data model and why something changed, what a metric actually tracks leads to better decisions than any brief could deliver on its own.
The Global Fund Data Explorer is live.