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The interaction dashboarding has been identified as a method to reduce the cognitive load of the Business Intelligence (BI) tools and is discussed in this paper. Due to the heavy usage of the BI platform to accommodate and render the large-scale dataset to the user in terms of cognitive strain in the process of information overload, and data rendering complexity, the user is commonly exposed to a certain cognitive load. Based on the cognitive load theory, the paper explores the ability of progressive disclosure, adaptive filtering, dynamic visual hierarchies, contextual drill-through, and responsive feedback to work in harmony to increase the usability of dashboards. These techniques increase the decision-making task by decreasing extraneous cognitive load and supporting germane load, hence, facilitating the process, especially in a self-service analytics system that supports users with different levels of knowledge. The paper achieves this by examining the theoretical underpinnings and practical examples of BI design principles that help in increasing the understanding and the speed of decision-making processes, as well as the widespread application of BI tools. The results highlight the need to strike the right balance to maintain dashboard interactivity and cognitive ergonomics in the development of analytics environments that would not dazzle and paralyze their users but allow them to take charge.
Keywords:
Interactive Dashboards, Cognitive Load, Business Intelligence, Data Visualization, User-Centric Design
Cite Article:
"Interactive Dashboarding Techniques to Minimize Cognitive Load in BI Tools", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.10, Issue 10, page no.a813-a818, October-2025, Available :http://www.ijrti.org/papers/IJRTI2510085.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator