Big-Thick Blending: A method for mixing analytical insights from big and thick data sources

Recent works have suggested an analytical complementarity in mixing big and thick data sources. These works have, however, remained as programmatic suggestions, leaving us with limited methodological inputs on how to archive such complementary integration. This article responds to this limitation by proposing a method for blending' big and thick analytical insights. The paper first develops a methodological framework based on the cognitivist linguistics terminology of blending'. Two cases are then explored in which blended spaces are crafted from engaging big and thick analytical insights with each other. Through these examples, we learn how blending processes should be conducted as a rapid, iterative and collaborative effort with respect for individual expertise. Further, we demonstrate how the unique, but often overlooked, granularity of big data plays a key role in affording the blending with thick data. We conclude by suggesting four commonly appearing blending strategies that can be applied when relying upon big and thick data sources.