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.
References
[1] Sweller, J. (2024). Cognitive load theory and individual differences. Learning and Individual Differences, 110, 102423. https://doi.org/10.1016/j.lindif.2024.102423 DOI: https://doi.org/10.1016/j.lindif.2024.102423
[2] Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97. https://doi.org/10.1037/h0043158 DOI: https://doi.org/10.1037/h0043158
[3] Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87-114. https://doi.org/10.1017/S0140525X01003922 DOI: https://doi.org/10.1017/S0140525X01003922
[4] Cleveland, W. S. and McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531-554. https://doi.org/10.2307/2288400 DOI: https://doi.org/10.1080/01621459.1984.10478080
[5] Healey, C. G. and Enns, J. T. (2012). Attention and visual memory in visualization and computer graphics. IEEE Transactions on Visualization and Computer Graphics, 18(7), 1170-1188. https://doi.org/10.1109/TVCG.2011.127 DOI: https://doi.org/10.1109/TVCG.2011.127
[6] Wickens, C. D. (2024). The multiple resource theory and model: Some misconceptions in data interpretations. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 68, 713-717. https://doi.org/10.1177/10711813241260740 DOI: https://doi.org/10.1177/10711813241260740
[7] Hjelle, S., Mikalef, P., Altwaijry, N., and Parida, V. (2024). Organizational decision making and analytics: An experimental study on dashboard visualizations. Information and Management, 61(6), 104011. https://doi.org/10.1016/j.im.2024.104011 DOI: https://doi.org/10.1016/j.im.2024.104011
[8] Stahmann, P., Rodda, A., Nebel, M., van der Staay, A., Janiesch, C., and Teuteberg, F. (2025). Advanced analytics in real-time operational dashboards in smart manufacturing: Effects on users' cognitive load and task load. Journal of Decision Systems. https://doi.org/10.1080/12460125.2025.2593245 DOI: https://doi.org/10.1080/12460125.2025.2593245
[9] Alhamadi, M., Alsayahani, H., Clinch, S., and Vigo, M. (2025). Behavioural indicators of usability in visual analytics dashboards. ACM Transactions on Interactive Intelligent Systems, 15(2). https://doi.org/10.1145/3715710 DOI: https://doi.org/10.1145/3715710
[10] Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Graphics Press.
[11] Few, S. (2013). Information Dashboard Design: Displaying Data for At-a-Glance Monitoring (2nd ed.). Analytics Press.
[12] Nielsen, J. (2006). Progressive disclosure. Nielsen Norman Group. https://www.nngroup.com/articles/progressive-disclosure/
[13] Almasi, S., Bahaadinbeigy, K., Ahmadi, H., Sohrabei, S., and Rabiei, R. (2023). Usability evaluation of dashboards: A systematic literature review of tools. BioMed Research International, 2023. https://doi.org/10.1155/2023/9990933 DOI: https://doi.org/10.1155/2023/9990933
[14] Ouwehand, K., Lespiau, F., Tricot, A., and Paas, F. (2025). Cognitive load theory: Emerging trends and innovations. Education Sciences, 15(4), 458. MDPI. https://doi.org/10.3390/educsci15040458 DOI: https://doi.org/10.3390/educsci15040458
[15] Sweller, J. (2025). An integrated human cognitive architecture. Educational Psychology Review, 37, 108. Springer. https://doi.org/10.1007/s10648-025-10089-1 DOI: https://doi.org/10.1007/s10648-025-10089-1
[16] Huang, W., Eades, P., and Hong, S. H. (2009). Measuring effectiveness of graph visualizations: A cognitive load perspective. Information Visualization, 8(3), 139-152. https://doi.org/10.1057/ivs.2009.10 DOI: https://doi.org/10.1057/ivs.2009.10
[17] Conati, C., Carenini, G., Hoque, E., Steichen, B., and Toker, D. (2014). Evaluating the impact of user characteristics and different layouts on an interactive visualization for decision making. Computer Graphics Forum, 33(3), 371-380. https://doi.org/10.1111/cgf.12393 DOI: https://doi.org/10.1111/cgf.12393
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
