The latest technological advancements allow users to generate a large volume of data related to their experiences and needs. However, the absence of an advanced methodology that links the big data and the creative process prevents the effective use of the data and extracting all its potential and knowledge in this context, which is crucial in offering user-centred solutions. Incorporating data creatively and critically as design material can help us learn and understand user needs better. Therefore, design can bring deeper meaning to data, just as data can enhance design practice. Accordingly, this work raises a reflection on whether designers could appropriate the workflow of data science in order to integrate it into the research process in the creative process within a framework of user experience analysis. The proposed model: data-driven design model, enhances the exploratory design of problem space and assists in the creation of ideas during the conceptual design phase. In this way, this work offers an integrated vision, enhancing creativity in industrial design as an instrument for the achievement of the proper and necessary balance between intuition and reason, design, and science.

La economía creativa refleja estilos de vida contemporáneos cada vez más asociados con las redes sociales, la innovación, la conectividad, el estatus, las marcas, las experiencias culturales y las co-creaciones. La creatividad implica un proceso generador de ideas y un acto selectivo para determinar cuáles son las mejores. El presente trabajo muestra cómo la combinación de fuentes del ámbito creativo, en conjunto con técnicas de análisis de grandes conjuntos de datos (big data), pueden contribuir a superar ciertas deficiencias metodológicas para la generación de productos, servicios o sistemas novedosos y de alta calidad.

Food design increasingly operates in phygital back-of-house professional kitchens where embodied culinary work is mediated by connected cooking appliances, embedded interfaces and cloud data infrastructures. Primary users are professional kitchen staff (chefs, line cooks, kitchen assistants) who operate appliances per cooking cycle within coordinated team workflows. This article examines cloud-hosted interaction event logs as design material for trace-based inference of operational interaction behaviour, captured as sequences, transitions, deviations, interruptions and recovery actions, without researcher presence, task scripting or post hoc self-report. A trace-based case study draws on eighteen months of telemetry from a fleet of 300 connected professional ovens. Analysis combines sequence-oriented metrics with a state-transition representation to characterize recurrent interaction paths, estimate conformance vs. deviation outcomes and localize breakdown-prone transitions across programme-entry flows. Results are presented as traceable evidence (corpus descriptives, device-level conformance distributions and programme-family patterns) and are interpreted as redesign hypotheses targeting programme legibility, alert handling and interruption recovery under time-critical service conditions. Sustainability relevance is framed operationally as reduced rework and fewer aborted cycles, while environmental quantification remains outside the evidential scope of interaction logs alone. Experiential constructs (satisfaction, preference, perceived usability) remain outside trace-only inference and require complementary qualitative inquiry; accordingly, only trace-derived behavioural evidence is presented within everyday kitchen production.