humanitarian.info

There’s inevitably a data drought when you’re working in an emergency in a developing country – either the coverage isn’t good, the historical record is patchy or the accuracy is questionable. In many cases, the data simply isn’t there – nobody has collected it in the past or is collecting it at present. Where they are collecting it, the collection process often isn’t systematic and the results are in formats that aren’t easily shared – where people are willing to share their data, which they often aren’t.

In my last blog post, you might have noticed that we were trying to identify water scarce unions. There’s no actual data on water scarcity, though – it’s not the sort of thing that anybody has ever measured in itself. So how do we work out which locations are potentially water scarce?

What we did was use a proxy – a set of data that we do know that can stand in for what we want to know. In this case, we had a list of unions where tube wells aren’t feasible – derived from a couple of phone calls and some photocopied sheets. Tube wells are the primary means through which the government delivers water to communities, due to the nature of the ground and the groundwater (particularly when you’re close to the sea, salinity is too much of a problem).

Where tube wells aren’t possible, we assumed (and it was an assumption) that there would be chronic problems with water supply – problems that would have been exacerbated by Cyclone Sidr. These are the locations where the humanitarian community needs to make sure that alternatives are available – for example, water trucking to ensure a supply line, even if it isn’t sustainable – and to allocate resources for rehabilitation, particularly rehabilitating the ponds that local communities rely on where they don’t have tube wells.

Now I freely admit that there aren’t many people who are as fixated on data quality and quantity as me – most people are busy actually implementing programmes rather than crunching numbers. Yet it’s something we should be concerned about, because if we don’t accurately know the numbers and locations of people in need, then how can we possibly target assistance properly? If we don’t have good baseline data, then how can we know if our work has had any impact (especially where high poverty levels make it difficult to work out which problems are specifically caused by a disaster and how many were pre-existing)?