Distributed Representational Analysis in Support of Multi-perspective Decision-Making

This paper describes research that explores principles of designing visual representations of data in sup-port of strategic decisions from the perspective of distributed decision makers. It uses the concept of distributed cognition (DC) to understand requirements of different stakeholders for data and information needed for making decision, and the concept of boundary objects to understand the shared utility of the visual representations in cross-functional decision-making. The study examines a product development setting in which decision-making is based on data resulting from digital modeling. Visualizations are widely used to explore data and make sense. However, research lacks studies connecting data visualization to decision-making. This research aims to propose visualization principles that are based on investigating information and visualization requirements of distributed decision makers. This empirical study uses the human-centered distributed information design (HCDID) methodology to analyze stake-holder requirements in a case study company and contributes to the research of decision-making with big data and analytics, particularly to corporate multi-perspective decision-making.