Using causal diagrams to understand and deal with hindering patterns in the uptake and embedding of big data technology

In this chapter, we explain the interdependencies between actors and factors that influence the uptake of big data technology and provide more insights into the adoption and spread of big data technologies. The systematic literature review by Günther et al. revealed that to advance our understanding of big data technology, research should move beyond BigMedilytics (BML) study levels and examine how work practices, organizational models, and stakeholder interests interact with big data technology practices. In the BML project, we had a unique opportunity to review 12 study projects using different big data technologies aimed at different goals in several European countries. The studies:
cover three themes with the greatest impact on the sector. Population Health & Chronic Disease Management and Oncology comprise the 78% of deaths [in noncommunicable] diseases. The third theme represents operations and equipment cost, covering the 33% of the expenditure in the sector.