Shifting Perspectives

Abstract

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.

How Data Workers Cope with Uncertainty

“…Ignoring Uncertainty: Although seven participants declared having ignored a known uncertainty in their data at some point during the analysis, this is not a common strategy (P2, 4,5,6,7,11,12). Our participants ignored known uncertainties when the category of uncertainty itself was not relevant to the analysis or when dealing with it was outside the scope of their role or expertise.…”

“…These models suggest that analysts maintain a mental representation of what data should be like, allowing them to filter uncertain data from their workflows. The use of uncertainty in the decision making process is not new and impacts various domains, types of data, and kinds of analysis [11]. Focusing on the human analyst, Grolemund and Wickham [9] propose a cognitive model and interpretation of the data analysis process that describes the effects of cognitive bias inherited from sense-making processes, with a goal of improving current data-analysis.…”

“…M: filter imprecise data; M: enrich with better quality data^A: improve acquisition^R: human expertise; M: aggregate data; R: prevent error propagation (P1, 3,7,9,11,12) Noise (4 participants) f P: plot data with uncertainty (P9) R: discuss with experts to identify noise^M: remove noise; C: compute threshold level^M: remove noise (P4,6,9,11) Table 3. Data uncertainty coping strategies by high level goals: Ignore, Understand and Minimise (Exploit is not shown as it only applies to missing data & imprecision).…”

“…Visualization tools were used by nine participants to support different tasks, e.g. P2 used Jigsaw [24] do nothing (P7) C: annotate to prevent data usage of erroneous records (P7) M: filter uncertain data; M: delete model that generates errors; A: improve acquisition source and tools; A: acquire data from multiple sources; R: group discussions^M: manual correction; C: set task constraints (P1, 3,5,6,7,8,11,12) Imprecision (8 participants) do nothing (P5) C: model uncertainty ; C: annotate data quality, confidence or possible range; R: compare to literature^C: set quality threshold (P1,2,9,12)…”