What does data innovation mean for traditional social science research?
By Arnaldo Pellini & Andrew Thornley
Data innovation and data analytics offer an unprecedented opportunity for expanding the sources of evidence that can inform policy-making. But is data innovation threatening traditional policy research? Arnaldo Pellini is a Research Fellow at the Overseas Development Institute and the lead for learning at the Knowledge Sector Initiative, and Andrew Thornley is an Asia Foundation alum; both have a wealth of experience across Southeast Asia. They raise some important questions as they look at experiences in Indonesia.
In the last five years, data innovation has changed the research and policy landscape. Policy-makers used to rely on more traditional forms of evidence, such as social science research, to make decisions. But new technologies and alliances for collecting, publishing and analysing data are changing the way policy-makers source and use evidence. We need to understand more about how data is being used in policy and what this means for social science researchers trying to inform policy decisions. Ultimately, social science research must adapt.
What is data innovation?
We are now able to collect much bigger data sets than traditional research. For example, new technologies enable us to collect large amounts of passive data, such as data collected by people using mobile phones or digitalised community radio.
A lot more data is now open and free to be used and reused. We’re seeing more ‘open data labs’, ‘public private partnerships for data innovation’, ‘data innovation challenges’, ‘open data acceleration funds’ and ‘citizen monitoring marathons’.
Data analysis is also more accessible. For example ‘data dives’ — often run by national statistics offices in partnership with the private sector — provide forums for people and organisations to get free support from expert data analysts.
Data visualisation is changing the way we communicate data, making large data sets more useful. Interactive data visualisations, like this, even allow the user to engage or play with data in new ways.
What does this mean for evidence-based policy-making?
The availability and accessibility of new data clearly offers unprecedented opportunity for evidence-based policy-making. The sheer scale of datasets provides credibility that cannot be matched by traditional research methods and approaches. Moreover, data can be more citizen focused (derived from what citizens think and experience, how they move, where they move, and so forth); analysis delivered more quickly; and it’s often far cheaper than traditional research.
But, while big data sets and analytics can identify correlations within data — and therefore help to identify problems or patterns in social behaviour — they struggle with causality and causation. In other words, they can’t tell you why something is happening and therefore are less helpful in finding ways to address the problems. Often the answers to the ‘why’ questions have to come from investing in research.
Social science needs to adapt
The advance of big data and data analytics is unstoppable. With national and local governments embracing data innovation, social scientists will need to get on board. Chris Anderson (former editor-in-chief of WIRED) argues that big data has opened the door to making scientific research methodologies obsolete — no more need to formulate a hypothesis and test it. Kenneth Benoit and Kenneth Cukier argue that the social scientists should not oppose this change, but rather embrace it. Within the next 10 years social science researchers ‘have to know and understand coding’ or else be out of a job, they say.
Indonesia is a good context to explore this further. It has a thriving civil society with a growing appetite for innovation, phenomenal social media use, and the Indonesian government is committed to open data at national and local levels.
Pulse Lab Jakarta, a data innovation lab jointly launched by the United Nations and the Government of Indonesia is working closely with the Knowledge Sector Initiative, a knowledge to policy programme of the Governments of Indonesia and Australia to enhance the role of evidence based policy making. Three studies are being researched to identify the impact of data innovation and data analytics on policy and on traditional policy research in Indonesia. The case studies feature a monitoring system for patients of malaria in remote Indonesia, use of drones for monitoring the condition of agricultural crops and a citizen monitoring system developed by Pulse Lab Jakarta which is being used at national government level. The three case studies will look at how data analytics have been used by policy makers and how it has contributed to policy uptake. The studies will be published later this year — we’ll keep you posted on the findings.