A methodology for the systematic design of storytelling dashboards applied to Industry 4.0

Abstract

Dashboards are popular tools for presenting key insights to decision-makers by translating large volumes of data into clear information. However, while individual visualizations may effectively answer specific questions, they often fail to connect in a way that conveys the overall narrative, leaving decision-makers without a cohesive understanding of the area under analysis.

This paper presents a novel methodology for the systematic design of holistic dashboards, moving from analytical requirements to storytelling dashboards. Our approach ensures that all visualizations are aligned with the analytical goals of decision-makers. It includes several key steps: capturing analytical requirements through the i* framework; structuring and refining these requirements into a tree model to reflect the decision-maker’s mental analysis; identifying and preparing relevant data; capturing the key concepts and relationships for the composition of the cohesive storytelling dashboard through a novel storytelling conceptual model; finally, implementing and integrating the visualizations into the dashboard, ensuring coherence and alignment with the decision-maker’s needs. Our methodology has been applied in real-world industrial environments. We evaluated its impact through a controlled experiment. The findings show that storytelling dashboards significantly improve data interpretation, reduce misinterpretations, and enhance the overall user experience compared to traditional dashboards.