Gobernanza de datos y visualización crítica en ecosistemas de innovación dinámicos

Abstract

The relationship between innovation ecosystems, data governance, and critical visualization is analyzed to explain why business innovation in dynamic contexts often fails less because of a lack of technology than because of weaknesses in coordination, shared interpretation, and trust among actors. The analysis begins from a specific gap: although the literature on ecosystems has advanced in the study of interdependencies, roles, platforms, and orchestration mechanisms, it often treats data as a relatively stable technical input, without sufficiently problematizing how its quality, traceability, selection, and visual representation influence resource allocation, risk interpretation, and collective learning. Based on an argumentative review of contributions on ecosystems, complexity, organizational learning, data governance, ethics, and algorithmic risk management, the notion of “reliable sensemaking” is proposed as an ecosystem capability. This capability involves building explicit agreements on definitions, lineage, quality criteria, analytical assumptions, and ways of visualizing uncertainty. It is argued that, in regulated regions and supply chains, competitiveness does not depend only on incorporating more digital tools, but on producing shared, contestable, and auditable evidence among organizations with different interests. Consequently, data governance is presented as a coordination architecture rather than merely as documentary compliance. It is also argued that dashboards, models, and visualizations are not neutral means of communication, since they organize priorities, legitimize decisions, and may amplify biases when they hide limits or assumptions. It is concluded that data ethics, interorganizational governance, and visual literacy should be understood as strategic infrastructure for coordinating multi-firm innovation.