Many organizations continue to rely on legacy software that stores valuable operational data but provides little or no capability for exporting, analyzing, or visualizing it. This project was developed to overcome those limitations by creating a complete data analytics pipeline for employee performance evaluation.

The system automatically extracts information from a legacy web application, where the original software lacks proper reporting and data access capabilities. Since much of the extracted content is stored in poorly formatted text, a custom decoding engine was developed in Python using regular expressions and advanced string-processing techniques to reconstruct structured information from raw data.

After processing, the cleaned data is stored in a carefully designed SQL database that supports efficient querying and long-term data management. The platform then performs more than twenty analytical procedures to evaluate employee performance across different time periods, departments, and operational metrics.

To make the system practical for daily use, two user-facing applications were developed. A Windows desktop application automates the data extraction process from the legacy system, while a web application imports new data into the database, executes all analytical workflows, and presents the results through configurable dashboards and interactive visualizations.

The project demonstrates expertise across the complete data lifecycle, including data acquisition, data cleaning, database design, analytical modeling, software development, and visualization, resulting in a scalable platform that converts previously inaccessible data into meaningful business intelligence.