EMPIRICAL INSIGHTS INTO POWER BI APPLICATIONS FOR DYNAMIC FINANCIAL REPORTING AND PREDICTIVE ANALYSIS

Abstract

This study addresses a persistent problem in finance analytics, namely the lack of quantitative, finance specific evidence on how technical quality, user proficiency, and interactive design jointly translate self service BI into better forecasts and decisions. The purpose is to estimate the contribution of Power BI enabled dynamic reporting and embedded predictive analysis to decision quality, and to identify the conditions under which these benefits are strongest. Using a quantitative, cross sectional, case based design, we sampled 208 professionals across 29 cloud and enterprise cases that have used governed Power BI environments for at least six months. Key variables include information quality, system quality, dashboard interactivity, user training and proficiency, organizational support, dynamic reporting effectiveness, predictive performance, and decision quality. The analysis plan specified hierarchical OLS models with HC3 robust errors, moderation by organizational support, and bias corrected bootstrap mediation from reporting to decision quality via predictive performance, with assumption checks and sensitivity tests. Headline findings show that dashboard interactivity, information quality, user proficiency, and system quality uniquely predict dynamic reporting effectiveness, with organizational support adding a direct effect and amplifying returns to proficiency and interactivity. Higher reporting effectiveness is associated with meaningfully better forecast accuracy, approximately a 0.9 percentage point reduction in MAPE per one unit increase in reporting effectiveness, and both reporting effectiveness and predictive performance explain decision quality, with a significant indirect effect through predictive performance. Implications for practice emphasize governed self service: invest early in role targeted training, codify semantic and design standards, and maintain visible sponsorship so finance teams consistently realize faster, more accurate, and more auditable decisions.