Quality data makes our work smarter

Targeted, effective programming: Sara Posada, from the Nike Foundation, on what data can do for development.

Quality data helps us target effective, economically sound projects to lift adolescent girls out of poverty - it's that simple. Sara Posada, from the Nike Foundation, on what data can do for development.


One of the major challenges development programmes face is finding the people in the greatest need. With limited funds, our approach needs to be targeted, so not a single penny is wasted. That's where data comes in. Accurate statistics shine a light on the specific groups we need to be working with and show us exactly where they are. 


Girls need unique, targeted programmes, centred around them. But if we haven't got the right kind of data to prove this, those programmes don't get funding - and therefore they don't happen. Take the example of maternal mortality: we know childbirth is the leading cause of death for adolescent girls, but maternal mortality statistics aren't split by age, so it's hard to make the argument to policymakers and other programmers that prevention strategies should be targeted at girls. With disaggregated data we would win that argument hands down.


We took the first step on the journey towards better data for girls with the Girls Discovered project. Driven by a desire to tell the world what we do and don't know about adolescent girls, members of the Coalition for Adolescent Girls and a team of advisers from the United Nations Foundation and the Nike Foundation joined forces with data mapping specialists Maplecroft. We brought existing global data onto one platform, made it visible and shared it. 

Girls Discovered is the go-to resource on global data for the Nike Foundation. In some cases we have shifted our geographic focus for an investment because of data we found on Girls Discovered. But it's a first step. For programmers and many policymakers, we need to see data at a sub-national level.


If the data gaps were filled, we could have more targeted and, as a result, more impactful programming. For example; if we understood where the HIV hotspots are, we could make a difference for young girls who are infected with HIV through sexual contact and for girls who are at risk for dying in childbirth.


Data that is split by both sex and age, from age 10 - that's the goal. Without data on girls in different age groups, the lines get blurred and we don't get the true picture of what adolescence for girls is really like. And we need that picture to be geographically specific. We need to focus in on the state, the county and even the village.
With extra investment we can find the girls who need us most, and work with them to end poverty - for good.