A data-driven decision-making system for sludge removal in shrimp ponds in India

The Shrimp Welfare Project (SWP) is running a sludge removal intervention across roughly 100 plus shrimp ponds in India. To know whether the intervention is working, we needed more than field reports. We needed a data system. Pond reports (water parameters) were coming in as images. Cost, area, and coordinate data etc. lived in separate files. The project asked us to build a data-driven decision-making system covering infrastructure, pipeline, capability, and governance.

We approached this as a data infrastructure problem before treating it as an analysis problem. We built a digitization tool that automated data entry from the image-based pond reports, removing a manual bottleneck that had been quietly limiting the team. We then structured the data so pond-level, farmer-level, and intervention-level records connect through stable identifiers like pond ID and farmer ID. We also ran initial analysis on the cleaned data and shared the results back with the team. Alongside the build, we developed a data collection and storage protocol so future records stay consistent with what is already in the system.

The output is the foundation of a reliable data infrastructure for SWP India. The deliverables include a data digitization tool that converts image-based pond reports into structured records, a connected central dataset replacing scattered images and spreadsheets, and an initial round of data science and analysis findings already shared with the team. This will make trustworthy data science and MEL possible going forward, rather than each analysis starting from zero.