Authors : Dr. Pavithra M. R.; Bogala Uma Naga Mahesh Reddy

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/2mysj8pt

Scribd : https://tinyurl.com/5nyyubkp

DOI : https://doi.org/10.38124/ijisrt/26mar323

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In the contemporary business environment, organizations increasingly rely on data-driven insights to support strategic decision-making. The exponential growth of business data has intensified the need for effective data visualization techniques that can transform complex datasets into meaningful and actionable insights. Data visualization serves as a critical interface between analytical outputs and managerial cognition, thereby influencing decision accuracy, speed, and confidence. Despite the widespread adoption of business intelligence tools such as Power BI and Tableau, many organizations struggle to design dashboards that effectively support strategic decision- making. Ineffective visualization design may lead to information overload, misinterpretation of insights, delayed decisions, and suboptimal strategic outcomes. Existing literature largely emphasizes usability, adoption, and technical aspects of visualization tools, while empirical research linking visualization design elements directly to decision quality and decision-making speed stays limited. The proposed research aims to examine the impact of data visualization design on strategic business decisions, with a specific focus on Power BI and Tableau. The study investigates how visualization design elements—such as chart selection, color schemes, layout structure, interactivity, and dashboard complexity—affect decision quality, decision-making speed, and managerial confidence. A mixed-method research approach will be adopted using both primary and secondary data. Primary data will be collected from middle and senior-level managers through structured questionnaires and controlled decision-making experiments. Secondary data will include organizational datasets, dashboards, and published industry reports. Statistical tools such as correlation analysis, multiple regression analysis, and analysis of variance will be used to analyze the relationship between visualization design and decision outcomes. The expected outcome of this research is the development of an empirically validated visualization design framework aimed at improving strategic decision quality and speed. The findings are expected to contribute to academic literature in management and business analytics and offer practical recommendations for organizations using Power BI and Tableau for strategic decision support.

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