This paper proposes that, in the context of generating actionable knowledge, uncertainties pertaining to big data streams should be recognized, categorized and accounted for at the appropriate level of knowledge management process models. Arguing that sensemaking from big data sources is a complex series of processes extending beyond just the application of sophisticated analytics, this paper proposes a big data reengineering (BDR) framework to guide requisite categorization, contextualization and remediation processes. The authors discuss the characteristics that uncertainty presents to organizations using big data streams as potential knowledge sources – surfacing relationships between the underlying knowledge flows and uncertainty and presenting typologies that categorize the effects of several common sources of uncertainty. These typologies also serve to provide guidance to transformation agent(s) regarding appropriate actions ultimately aimed at the generation of actionable knowledge.
Editor's notes on Special Issue on “Statistical Process Control for Big Data Streams”