Development of a Dashboarding and Visualization Framework For Pharmaceutical Brand Performance Analytics Using Tableau and Python

Abstract

This study has examined the development of a dashboarding and visualization framework for pharmaceutical brand performance analytics using Tableau and Python in selected pharmaceutical cloud and enterprise analytics cases. The main problem has been the dependence of many pharmaceutical organizations on fragmented reports, manual spreadsheet processing, static charts, delayed updates, and limited statistical validation, which can weaken brand monitoring and managerial decision-making. The purpose of the study has been to evaluate whether Tableau-based dashboarding capability, Python-based analytical processing, dashboard usability, data visualization quality, and analytics automation significantly improve pharmaceutical brand performance analytics effectiveness. A quantitative, cross-sectional, case-based research design has been used, and data have been collected from pharmaceutical professionals involved in brand management, sales reporting, marketing analytics, business intelligence, data analysis, and decision support. Out of 250 distributed questionnaires, 214 valid responses have been retained, producing an 85.6% valid response rate. The analysis plan has included descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation analysis, multiple regression modeling, hypothesis testing, dashboard prototype evaluation, and Python-driven analytical workflow validation. The findings have shown strong agreement across the constructs, with data visualization quality recording the highest mean score of 4.18, followed by dashboard usability at 4.13, Tableau-based dashboarding capability at 4.09, pharmaceutical brand performance analytics effectiveness at 4.05, analytics automation at 3.98, and Python-based analytical processing at 3.92. Reliability has been confirmed through Cronbach’s alpha values ranging from .81 to .89, with overall reliability of .91. Correlation results have shown significant positive relationships between analytics effectiveness and data visualization quality (r = .68), dashboard usability (r = .65), Tableau capability (r = .62), Python processing (r = .57), and automation (r = .55), all at p < .001. The regression model has been significant, F (5, 208) = 48.72, p < .001, explaining 53.9% of variance. The study implies that integrating Tableau visualization with Python analytics can improve brand monitoring, KPI interpretation, reporting accuracy, and evidence-based pharmaceutical decisions.