Cognitive Load Optimization Models for Enterprise Analytics Dashboards
DOI:
https://doi.org/10.22399/ijcesen.5261Keywords:
Cognitive Load, Optimization Models, Enterprise Analytics DashboardsAbstract
Enterprise analytics dashboards have become central instruments of organizational decision-making, yet their effectiveness is frequently undermined by cognitive overload. This paper presents a three-layer model for optimizing cognitive load in dashboards, breaking the problem down across three dimensions: user context, data semantics, and interaction design. Drawing on cognitive load theory, graphical perception research, working memory constraints, and recent empirical studies of dashboard usability, the model maps validated reduction strategies to three types of cognitive load (intrinsic, extraneous, and germane) and differentiates their application across four enterprise dashboard archetypes: operational monitoring, executive reporting, investigative analytics, and compliance and audit. The paper further examines measurement methodologies for evaluating cognitive load in dashboard contexts, including physiological, behavioral, and subjective approaches. The framework that comes out of this work equips dashboard architects and design system engineers with a structured approach for making cognitive load a deliberate part of the architecture, rather than leaving it as an afterthought in usability reviews.
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