Goal-Oriented Data Storytelling from User Requirements: An LLM-Assisted Method for Industrial Analytics

The Digital Transformation of manufacturing, driven by Industry 4.0 and the green transition, has led to a surge in sensor deployment and real-time data collection. This shift gives rise to contexts in which large volumes of complex multivariate time series data present valuable opportunities for advanced analytics, while simultaneously posing significant challenges for effective interpretation. Analytical dashboards are widely used to support decision-making, yet their impact is often limited when design choices do not align with users’ analytical goals or cognitive workflows. A promising response to this challenge is data storytelling, which combines data visualization with narrative structures to enhance comprehension, especially in high-pressure, multi-stakeholder environments. However, in complex industrial contexts, the task of identifying and preparing relevant data for analysis presents considerable challenges due to the massive data volume constantly generated. Recent advances in Artificial Intelligence, particularly Large Language Models (LLMs), present new opportunities to automate and enhance the development of such goal-oriented dashboards. It is therefore necessary to investigate how they can be incorporated into a method that applies them for data storytelling in data-intensive contexts. In light of this, this paper proposes a method for designing analytical dashboards that integrate multivariate sensor data with goal-based storytelling techniques, supported by LLMs to accelerate and guide the development process. The proposed method is instantiated in a real-world industrial case, within the PRODUTECH R3 “Industry-UP” project, in the CEI use case for anomaly detection and operational optimization in sensorized stone-cutting machines. The results show that the method reduces manual intervention, identifies data gaps in earlier stages, and delivers dashboards directly traceable to strategic goals, improving both development efficiency and decision-support quality.

This is a repository for the several documents associated with the publication of the paper "Goal-Oriented Data Storytelling from User Requirements: An LLM-Assisted Method for Industrial Analytics" in ER2025: Companion Proceedings of the 44th International Conference on Conceptual Modeling: Industrial Track, ER Forum, 8th
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