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Research Paper | Information Technology | United States of America | Volume 9 Issue 3, March 2020 | Rating: 3.2 / 10
A Modular Python-Based Framework for Real-Time Enterprise KPI Visualization Using Pandas and Interactive Dashboards
Dheerendra Yaganti
Abstract: This paper presents the design and implementation of a modular, extensible data analysis framework in Python aimed at enabling real-time Key Performance Indicator (KPI) visualization for enterprise business intelligence dashboards. The framework integrates core components such as Pandas for efficient data manipulation, Matplotlib for static reporting, and Plotly for interactive visualization. The system architecture supports plug-and-play modules for seamless integration with diverse data sources, including CSV, SQL databases, and RESTful APIs. By leveraging event-driven data pipelines and lightweight computation logic, the proposed solution enables continuous monitoring of business metrics without introducing significant processing overhead. The framework emphasizes scalability, maintainability, and customization, ensuring it can be adapted to suit various business domains and data environments. Interactive dashboards are generated dynamically based on user-defined parameters, offering stakeholders intuitive insights into operational trends, performance anomalies, and strategic decision points. The study includes a performance evaluation conducted across multiple enterprise datasets, demonstrating the framework?s effectiveness in providing timely, actionable visual analytics. The proposed approach offers a practical foundation for organizations seeking real-time, extensible business intelligence systems without relying on complex or proprietary solutions.
Keywords: Real-Time Data Visualization, Python Framework, Business Intelligence (BI), KPI Dashboards, Pandas, Matplotlib, Plotly, Data Analytics, Modular Architecture, Enterprise Reporting, Interactive Dashboards, Data Pipeline, Data-Driven Decision Making, Data Integration, Open-Source Visualization Tools
Edition: Volume 9 Issue 3, March 2020,
Pages: 1735 - 1738