Power BI and Oracle Integration for Enterprise-Level Business Intelligence

Sandeep Parshuram Patil

Abstract: This paper examines the integration of Microsoft Power BI with Oracle Database systems to enhance enterprise-level business intelligence (BI) performance and decision-making. In modern organizations, where data volumes are vast and decision cycles are increasingly short, seamless integration between analytical tools and enterprise databases is critical. Power BI, a leading self-service analytics platform, and Oracle, one of the most robust relational database management systems, together offer a powerful combination for managing, visualizing, and interpreting enterprise data. The study explores architectural frameworks, data connectivity options, and optimization strategies that enable efficient interoperability between the two platforms. Emphasis is placed on integration techniques such as DirectQuery, ODBC, and on-premises data gateways, as well as on methods for ensuring high performance, security, and compliance with governance standards. Empirical analysis and case-based evidence highlight improvements in data accessibility, report refresh efficiency, and decision accuracy following successful implementation. The findings indicate that Power BI?Oracle integration significantly enhances data-driven culture, enabling organizations to unify disparate data sources into coherent, actionable intelligence. The paper concludes by identifying best practices and future opportunities such as cloud-based scaling, AI-driven analytics, and automated pipelines that can further strengthen enterprise BI ecosystems and support long-term strategic growth.

Keywords: Power BI, Business Intelligence (BI), Data Integration, Enterprise Analytics, Cloud Integration, Data Security

How to Cite?: Sandeep Parshuram Patil, "Power BI and Oracle Integration for Enterprise-Level Business Intelligence", Volume 12 Issue 9, September 2023, International Journal of Science and Research (IJSR), Pages: 2249-2253, https://www.ijsr.net/getabstract.php?paperid=SR23922094534, DOI: https://dx.doi.org/10.21275/SR23922094534