What does data innovation mean for traditional social science research?

Pulse Lab Jakarta
Sep 22, 2016 · 4 min read
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Image by Kris Krüg, licensed under Creative Commons.

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.

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.

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.

Pulse Lab Jakarta

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