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.This work has been co-funded by the AETHER-UA project (PID2020-112540RB-C43), funded by Spanish Ministry of Science and Innovation; the ENIA Chair of Artificial Intelligence from the University of Alicante (TSI-100927-2023-6) funded by the Recovery, Transformation and Resilience Plan from the European Union Next Generation through the Ministry for Digital Transformation and the Civil Service; the Big Data, Data Space, Artificial Intelligence and Health project (TSI-100121-2024-10) funded by the Spanish Ministry of Digital Transformation; and the CIBEST/2022/122 grant, both funded by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana), Spain. Also, this work has been supported by FCT – Fundação para a Ciência e Tecnologia, Portugal within the R&D Units Project Scope: UIDB/00319/2020