Identifying and Visualizing Not-For-Profit Grant Data to Improve Matchmaking of Grants

The social enterprise Our Community aims to build automation and intelligence tools to more effectively find and monitor grants in Australia, New Zealand, UK, USA, Canada and South Africa. 

The problem

Our Community provides information, tools, and advice to thousands of social sector organizations to support their crucial work of building stronger communities.

Traditional methods to find and monitor grants are time-consuming, expensive, and limited. The aim of this project is to help get money flowing between grantmakers (funders) and the not-for-profit sector, providing the necessary capital to enable positive social change.

The results

In this project, the team built a Natural Language Processing (NLP) analysis pipeline for feature extraction, scraping Twitter, Google, and 1200 PDF files through automated APIs.

The team decided on automating the entire process by designing a microservice that used RESTful APIs under the hood.

In addition to processing unstructured data using NLP, the team wrote numerous site-specific scrapers to extract data from the web that was already structured in tables. The overall approach allowed the team to gather data that visualizes several billion dollars of not-for-profit grant data for further NLP analysis across six countries. Finally, the team built an interactive dashboard visualizing the distribution of the grants.

You can read more about the whole process in detail in the article attached below.

A demo of the StreamLit application

Streamlit dashboard – Source: Omdena