When the data exists — but the decisions don't

01 — The Situation

Every month, BirthBridge's 140 community health workers completed paper registers tracking enrollments, antenatal visits, referrals, deliveries, and newborn follow-ups across 73 communities in Northern Nigeria. State coordinators transcribed these into Excel. The M&E officer consolidated them into a master file.

And then, when donor reporting season arrived, three weeks of manual effort produced a Word document that answered exactly one question: What did we do?

Not: did it work? Not: for whom? Not: where are we losing women between referral and delivery? Not: which state needs a different strategy entirely? Just a table of counts, a list of activities, and a set of numbers that no one could fully trust — because no one had a clear view of how they were derived.

That gap, between data collected and insight acted upon is exactly where this engagement began.

03 — What the Data Revealed

Some findings confirmed what leadership suspected. Others reframed how they understood their own program.

Structural inequity — not program design failure

Sokoto's underperformance could not be explained by CHW quality or training gaps alone. The data pointed to structural barriers such as: access, infrastructure, and community decision-making norms. Requiring a state-specific response strategy rather than a uniform program fix.

Early enrollment is the upstream lever

Only 26.4% of women enrolled before their second trimester. This single metric was upstream of almost every other outcome gap — more time in the program meant more ANC visits, better referral rates, and higher skilled birth attendance. Improving first-trimester enrollment would compound across the entire care continuum.

Data quality is a program management issue — not just an M&E problem

Sokoto's 60.8% data completeness rate meant the program was making resource and strategy decisions with an incomplete picture of its lowest-performing geography. Surfacing this in the report reframed data quality from a technical inconvenience to a leadership priority.

04 — What BirthBridge Walked Away With

This engagement produced four distinct outputs, each serving a different use case, and together forming a complete analytical infrastructure BirthBridge did not have before.

05 — What This Project Is Really About

BirthBridge Africa is not an unusual organisation. Across the health NGO and health-tech landscape, there are hundreds of programs just like it: staffed by committed people, collecting more data than they know what to do with, and producing reports that describe activity rather than illuminate impact.

The gap is rarely a shortage of data. It's a shortage of the systems, structure, and analytical thinking needed to turn that data into something an executive director can act on, something a program officer can learn from, and something a donor can fund with confidence.

That is the gap I work in. Not as someone who makes charts — but as a Health Data Systems Strategist who builds the foundation that makes the charts mean something.