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

TY - JOUR

T1 - Big–Thick Blending

T2 - A method for mixing analytical insights from big and thick data sources

AU - Jørgensen, Tobias Bornakke

AU - Due, Brian Lystgaard

PY - 2018/6

Y1 - 2018/6

N2 - 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.

AB - 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.

M3 - Journal article

SN - 2053-9517

JO - Big Data & Society

JF - Big Data & Society

ER -