A fine-grained perspective on big data knowledge creation: dimensions, insights, and mechanism from a pilot study

The creation of knowledge from Big Data is increasingly drawing the attention of scholars and practitioners in management research. Valuable knowledge first requires identifying the Big Data features connected to knowledge insights creation and the mechanism beyond this creation. This paper examines Big Data dimensions and insights creations at a fine-grained level by adopting the knowledge creation lens. Specifically, what is the mechanism of creating knowledge from Big Data? How to transform raw Big Data into knowledge? We adopted a qualitative case study to explore the large-scale multinational pilot launched in three European cities. The pilot amalgamated a large amount of data feeds from different sensors and open data and created various insights to inform cities’ strategies. By employing an inductive content analysis with abductive procedures and coupling it with participatory observations, we were able to ground findings on the multi-level empirical and theoretical base and build a framework that embraces all discovered complexities and fine-grained features of Big Data dimensions and guides knowledge creation from Big Data. Our research offers a more in-depth understanding of the mechanism of knowledge creation in the BD context. First, we opened up BD's black box by disentangling the knowledge creation mechanism while transforming raw BD into BD insights. Second, our study offered empirical evidence of the growth mechanism working on Volume and Variety dimensions. The uniqueness of this study lies in the fine-grained perspective of BD characteristics and the underlying mechanism of insights creation.