DATA DRIVEN KNOWLEDGE

BEYOND THE AESTHETICS OF DATA IN THE PROCESSES OF ACCESSING KNOWLEDGE

Abstract

In the context of the knowledge economy, the proliferation of data and the growing complexity of information systems call for new forms of knowledge production, access, and interpretation. This paper proposes a theoretical and methodological reflection on data visualization as a cognitive and cultural practice, aiming to move beyond the purely aesthetic conception of data to investigate its epistemic, interpretive, and situated dimensions.
Through a critical analysis of computational methods and visual languages — including clustering techniques, network-based models, dimensionality reduction, and interactive interfaces — the paper highlights how the visual form of information is never neutral, but instead embodies design choices, ideological frameworks, and cultural assumptions that shape meaning-making. From this perspective, visualization is understood not simply as a functional representation, but as a semantic space and a knowledge device. Particular attention is given to the potential of Brain–Computer Interfaces (BCIs) in data visualization processes. These technologies, which enable interaction between neural activity and digital systems, introduce a new dimension to the visual experience of data: they allow for real-time monitoring of representational effectiveness and support the design of adaptive systems responsive to users’ cognitive signals. 
This approach opens up advanced research scenarios, where the boundaries between visualization, perception, and interpretation become increasingly fluid.
The aim is to outline a theoretical framework in which information design emerges as an interpretive and relational practice — one capable of transforming data into language, and language into shared knowledge. In this transition — from represented to generated data — new horizons unfold for graphic design and for the visual mediation of complexity.