The perils of using data as proof

I had a look through a new DFID publication called “Assessing the Strength of Evidence.” It’s a guide for DFID staff to help them better understand and better use data gathered from research. It brings to light an important point often forgotten in the race to use ever more data to support advocacy/communications/humanitarian intervention/campaigns: just having data does not prove your case.

I’m not saying data is a bad thing. Data is vital to develop a position on an issue and recommendations to resolve a problem. As the DFID document states, “Robust research and evaluation generates the evidence required to form judgements, deliberate options and make intelligent decisions about how to spend scarce financial resources on behalf of taxpayers.” Data isn’t only important for making funding decisions, but that’s obviously DFID’s particular area of interest.

Yet the value of data and other types of evidence can be overstated. The non-profit world, in its continuing love affair with the private sector (admiration for some aspects of the private sector isn’t without merit, but there are good reasons that non-profits and for-profits have different models), seems to sometimes take the view that more data means more legitimacy and more “rightness”.

Lots of data -- but what is its relevance? image courtesy Marc Smith, Flickr

Lots of data — but what is its relevance? image courtesy Marc Smith, Flickr

Yet as the DFID publication attempts to teach, more data isn’t necessarily better data. It’s the quality of that data and its relevance to the issue(s) at hand that matters. Anybody who knows anything about research methods knows this, but unfortunately the drive for quantity of data over quality and data — and crucially data analysis — sometimes seems to have the non-profit sector in a chokehold.

As ever more communications technologies spring up, as well as public-private partnerships on use of communications technologies, we have more and more data at our fingertips. We do some amazing things with that data, from better tailoring humanitarian assistance to meet needs to raising the awareness on wealth inequality of over five million people with captivating graphics on the web. This is an interesting and burgeoning field and I say more power to it. But merely having a lot of data at your fingertips does not mean you’re doing a great job (unless you job’s entire objective is to gather data!). The analysis of the data, and its connection to relevant issues, is what counts.

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