A couple of weeks ago I sat with Robert Kirkpatrick, the head of the UN Global Pulse Initiative who was over from the UN headquarter in New York and Diastika Rahwidiati a consultant at PulseLab Jakarta. We had an interesting chat about things that are important to us: data, big data, evidence, demand of evidence, and public policy – and particularly some of the misconceptions out there.
Jakarta and Kampala are the two ‘field‘ offices of the Global Pulse initiative. PulseLab Jakarta opened in 2012 and works in close collaboration with the Indonesian government to facilitate the adoption of new approaches for applying new, digital data sources and real-time analysis techniques to social development.
The main points I took from our conversation are:
1) There is a strong common denominator in the work on big data, data innovation and the strengthening of demand and use of evidence in government institutions. This common denominator is the belief that more and better data and evidence can help governments to make better decisions about the ways they want to design and implement policies, the way they want to organize service delivery, or the way they want to empower citizens to have a say in how policies influence their lives.
2) Big data and data innovation is perceived as a threat by researchers rather than an additional source of evidence that can help inform government decisions. It is true that big data analysis requires a new set of skills which may not necessary be in the luggage of academic and policy researchers, but it does not automatically mean that big data analysis will put researchers out of work. There is some misinformation or misperception out there.
3) The main difference between these two ‘worlds’ is that big data are exposing new ways for doing research. For example, no need for representative samples. There may not even be the need for a hypothesis to be tested through research but conclusions result from constant analysis and crawling through large sets of digital data. This makes big data and data innovation analytical approach very different from the positivist approach that has guided research for centuries. It can be destabilizing, but it does need to be.
4) In spite of these differences, traditional research is still needed. Policy research that looks at the past and tries to provide options for the future will continue to be valuable to inform development planning and policy design. Data innovation and the analysis of big data are a new type of evidence that complements traditional forms of evidence. The added value of data innovation is that it allows a much a quicker analysis of policy strategy and directions, it can develop quick feedback loops that allow, almost in real time, to make changes to policy implementation without the need to wait for the results of the next census.
We, who work on demand and use of evidence in policymaking should embrace the opportunities that data innovation provides. There is no need to be scared of big data. It can help us as well as the partners with whom we work to make policy more evidence based.