Big data and big ideas in Asian Studies

This text is based on introductory remarks to a double panel on big data at the Association for Asian Studies annual convention held in Philadelphia in March 2014. The panel was organised by researchers from the eHumanities project Elite Network Shifts. Four papers from this panel, and a longer version of the present text, have been submitted to the Asian Journal of Social Science for a special issue on big data.

Social scientists are discovering a bonanza of new material as Asian media and social lives move online, and as historical repositories are digitized. Cross-disciplinary collaboration with computer scientists and statisticians offers immense scope for cutting-edge research. Big data offers us new ways of resolving long-standing controversies, substantiating hunches, or visualizing proven relations within the study of Asian societies. It may even lead to entirely new questions. At the same time, there are plenty who doubt the added value of these discoveries, since big data does not by itself produce insight about things like causality. The question to be addressed in this special issue is: Will big data open up a new era of big ideas in Asian Studies, or will the big ideas drown in big data and leave only little ideas in its wake?


Big data is traditionally defined as data sets too large and complex to be managed with conventional data processing applications. But as the concept has made the move from the natural to the social sciences it has acquired dimensions beyond mere size. Among them are its diversity and hence potential for relations with other types of data such as Wikipedia, its incompleteness, and the fact that it is often collected for purposes other than research. Big data tantalizes social scientists with its vast possibilities (shading at times into the sinister), yet at the same time presents such daunting methodological challenges that the rewards threaten to slip just beyond our grasp.

The papers to be presented in this special issue of the Asian Journal of Social Science have their origin in a back-to-back panel at the March 2014 Association for Asian Studies conference in Philadelphia, Pennsylvania (see preview by Jacky Hicks of the panel in our first emagazine). It brought together scholars who identified primarily as political scientists interested in Asia with others who were in the first place statisticians or mathematicians. As the gulf between ‘qual’ and ‘quant’ scholars widens throughout the social sciences, a genuine meeting of minds such as the one we experienced there was refreshing. We trust the selection of the papers at that panel included here will convey something of this sense of excitement.


The meeting was stimulating not least because experiments with big data are now producing real results. They are rapidly expanding our horizons of the possible. For example, the opening paper by Ben Nyblade and colleagues offers a new method of observing and analyzing the political engagement of ordinary people. Scaling up to big data yields results that have a much greater breadth than those delivered by the traditional survey or the ethnographic interview. Masses of data from online social media allow researchers to hear the political engagement of millions of people, in their own voices and not filtered through survey questions. The sheer bulk of this data coupled with its microscopic detail and continuous time scale are simply unprecedented, especially for a developing country like Thailand. Others are doing similar work in Indonesia, Malaysia, Japan, and even in China.

While big data project proposals commonly promise ‘revolutionary’ results, in reality they rarely break radically with proven methods. They build on them. For the moment at least, existing disciplines still provide the depth of insight that guides the most productive research strategies. This conceptual continuity should help calm fears in some quarters (arising from long-standing qual vs quant battles) that big data research plays fast and loose with our existing knowledge, or that the extra numbers big data gives us are not matched with extra qualitative insight. (That said, the question of theory and big data will be one of the big challenges of the future. It will pit those who say the data is now so big it doesn’t need theory any more, against others who say that, on the contrary, it needs better theory than ever before.)


The big ideas promised by the big data revolution will probably only emerge if these difficult cross-disciplinary conversations are pursued tenaciously. Such a conversation should avoid the easy formulae of inter-disciplinarity, which too often lack serious content. Rather each partner should hold on to the integrity of their chosen discipline while at the same time learning enough of the language of the other to engage in intelligent discussion. This requires effort. We do suspect that big ideas are there within the big data that is now all around us. But those big ideas are not there for the picking up. They will only take shape in the minds of those prepared to engage in a creative and open dialogue with collaborators from beyond the borders of their own discipline.

In short, we expect social scientists working on Asia increasingly to discover the potential for new insight into the questions that interest them by exploiting the large amounts of big data now becoming available for their area of research. They should expect no panacea. Results will come incrementally rather than in a revolutionary fashion; they will often appear ‘big picture’ rather than subtle; and to get there the Asian studies research community must invest time, money, and a fresh appreciation for the drudgery of data work. But the results presented in this edition suggest insights are possible that simply cannot be obtained any other way.

Gerry van Klinken is senior researcher at the Royal Netherlands Institute of Southeast Asian and Caribbean Studies (KITLV) in Leiden (email:; website:, and coordinator of Elite Network Shifts. I gratefully acknowledge inspiring discussions with Wolfgang Kaltenbrunner, PhD candidate in Science and Technology Studies at Leiden University. His doctoral thesis on emerging forms of knowledge production in digital scholarship is forthcoming in 2015.