The Role of Data Visualization Tools in Financial Decision-Making: A Comparative Analysis of Tableau, Power BI, and SSRS - Nayak, Saugat (Eastasouth Institute, 2025)
This paper discusses the effect of data visualization instruments on the management of money or funds, with special reference to Tableau, Power BI, and SQL Server Reporting Services (SSRS). As big data volumes define today's financial sector, these tools provide just the right set of features to analyze massive amounts of information. Tableau is thereby brought out for its enhanced data visualization, where financial analysts can easily analyze data and get real-time trends. Power BI is also inexpensive and fits into the Microsoft ecosystem: AI is used to provide personalized recommendations and to detect anomalies automatically. SSRS, on the other hand, is more popular for its strong reporting purposes. It can handle more formatted reports, which are needed for such tips as regulation with colossal organizations. In the comparative analysis, each tool's effectiveness in the financial scenarios and its drawbacks are discussed, and how each of the tools can be applied in risk management, resource allocation, and market trend identification are displayed. Tableau is best used for interactive dashboards, Power BI has customized visuals better for customer behaviors, and SSRS is best for structured tabular reports with large volume data. It also demonstrates the interaction between Tableau and SSRS, which, when combined, make real-time data analysis and structured reports increase the rate of decision making. Anticipated development inversions like AI integration into services, real-time analytics, and self-service business intelligence are others that are seen to redesign the financial sector's manner of handling data. It will be wise to adopt this research's implication that the identification of the right tool based on an organization's organizational needs can substantially enhance financial operations' efficiency and offer a competitive advantage in data-intensive contexts.