The Problem
An NGO with 42 branches and over 2,100 microfinance groups needed branch-level data aggregated into regional and zonal reports — a process complicated by complex Unicode text matching across multilingual datasets.
The Solution
Built Python-based data processing pipelines using openpyxl to extract, clean, and aggregate branch, employee, and field officer data into structured Excel and JSON outputs — solving Unicode text matching challenges across multilingual data along the way.
The Result
Delivered accurate, structured regional and zonal reports, reducing what was previously a manual, error-prone process to an automated pipeline.