Last summer the U.N. High-Level Panel of Eminent Persons on the Post-2015 Development Agenda (HLP) released a report that does a fine job of thinking through what international development goals should succeed the Millennium Development Goals, which expire in 2015. Perhaps to the surprise even of its authors, one idea in the report got lots of people talking: the call for a “data revolution”:
The revolution in information technology over the last decade provides an opportunity to strengthen data and statistics for accountability and decision-making purposes. There have been innovative initiatives to use mobile technology and other advances to enable real-time monitoring of development results. But this movement remains largely disconnected from the traditional statistics community at both global and national levels. The post-2015 process needs to bring them together and start now to improve development data.
Data must also enable us to reach the neediest, and find out whether they are receiving essential services. This means that data gathered will need to be disaggregated by gender, geography, income, disability, and other categories, to make sure that no group is being left behind.
Better data and statistics will help governments track progress and make sure their decisions are evidence-based; they can also strengthen accountability.
Post2015.org has just posted the first of a two-part series from me embodying my initial attempt to make sense of the “data revolution.” I think the idea is problematic because it is vague. And yet, national statistics are an underfunded public good almost everywhere, so this excitement could accelerate progress against a real problem.
I think the key to thinking it through is to inventory the different kinds of data—censuses, sampling surveys, systems for registering births and deaths as they happen, etc.—and ask, hard-nosed, which ideas for improvement could realistically make the most difference in people’s lives. Different types of data need different types of institutions for collection. Weak data reflect weakness in those institutions. And institutions are not easy for outsiders to revolutionize for the better.
As far as I’m aware, the most intelligent, pragmatic thinking about the best ways forward is taking place within CGD’s Data for African Development working group. (OK, I’m biased.)
I discovered that the term “data revolution” predates the HLP report. It seems that my then-colleague Nandini Oomman coined it in 2010. This led to the following pair of unwittingly matched blog posts, separated by more than 2 years: