My coffee mug says, “I Heart Big Data”, and I’m going to get one made for small data, too. Here’s why.
At JSI, data for decision-making is a mantra. In our offices, with donors, in stakeholder meetings, we deliver on promoting a culture of data use. But it’s tougher to cultivate enthusiasm for data use where people may not have seen a graph before. It’s also easy for data use to stop right above the community level where formal meetings – that comfy space for soaking up slides of data – don’t occur.
“Small data” is my pet name for data generated by primary level service delivery points: actual numbers that reflect day-to-day work of service providers. In JSI’s work under USAID and the Health Pooled Fund in South Sudan, we saw at the health facility and local government levels that information was being collected, verified and submitted, but not often used for strategic planning, decision making, or program adaptation. As a result, we introduced a data discussion component into our routine facility technical support visits.
When we routinely engaged facility workers and local leaders in discussions based on facility register numbers, staff became more interested in exploring issues. They participated more actively in planning interventions and were more committed to making improvements.
Picture a two-room health facility staffed by a few midwives and a vaccination specialist. The facility serves a couple thousand permanent residents, with spurts of internally-displaced people using the facility for various services. Actual numbers from the registers, hand-rendered in graphs and posted on the facility wall, were a powerful communicator of what was happening in the facility. Sticky as actuals can be, we had to accept that because the denominators weren’t reliable, focusing on real-time data gave us a clearer local picture. Plotting actuals sparked conversation about possible determinants of low coverage — seasons, politics, migration — and solutions. Our project team supported local governance to help facility communities to prioritize issues and to develop two-way action plans, empowering stakeholders to contribute to improvements.
Higher-level data tell us about context, trends, and changes at all health system levels. But on a routine basis at the community level, small data run the show. From month to month, small data analysis means looking at changes in, for example, how many births were attended by skilled attendants.
In South Sudan, as one of many examples, JSI applied a straightforward process based on three components:
- Engage the necessary local actors in identifying a few key focal indicators, and define the process for talking about data. Tying the data use process to discussing services quality drives home the “so what” factor for the numbers. Prep the process, from making sure the right people participate, to distilling key findings, and updating basic data visuals. This process can happen in various fora, such as support visits or monthly stakeholder meetings.
- Wrap up with feedback on the process and collaborative action planning where priority areas are defined with time-bound responsibilities assigned.
- Follow-up: Little happens without it. Previous action plans can be the basis for future supervision visits or meetings.
Here’s a small data victory snapshot from South Sudan: During a support visit, our project supported staff and local governance through this process to discuss and plan implementing 24 -hour maternity care access. Skilled deliveries were soon on the rise.
Small data use is neither new nor complicated – it just doesn’t happen as much as it could in community-based public health projects. With awareness, commitment and planning, a routine and collaborative process focused on small data can have big impact.