Applicants

David Kane is a Data Scientist working for Cast and Beehive Giving, who has produced an analysis of applications to the Tech for Good programme, as well as a searchable directory of applications. Please see his introduction to this analysis below.

As part of applying to the Tech for Good funding programme, applicants were informed that their application would be made public even if they were unsuccessful.

This meant that when Comic Relief asked us to analyse the applications to the fund we could expand the scope of analysis beyond just the ten successful applications. Looking at all applications allows us to look at wider trends – such as who is asking for tech for good funding, what do they want to do with it, and what stage of digital development are they at.

The analysis looks at five main questions:

– What types of technology are being developed?
– What approaches are adopted?
– What’s the focus of the application?
– Who are the target audience?
– What stage of development are the projects?

Surprisingly, the first of these was the most difficult to answer. Reflecting the criteria of the fund, the projects were generally at the concept or pilot stage. This meant that applicants had often not yet come to decisions about the technology they would be using – and described the project outputs in more general terms as an “app” or “website”. I believe this should be seen as a positive – a sign that applicants were focusing on the problems they wanted to solve, rather than letting the technology drive the project goals.

You can read more answers to the questions above in the application analysis. The analysis showed that the typical application was from a larger registered charity based in London, but that there was variation beyond that typical picture. The largest focus of applications was on health and wellbeing, and generally applicants were aiming to provide services directly to beneficiaries.

We’ve also created a directory of applications, where you can explore the projects, filtering by their individual characteristics. We’ve made the code for the site available on Github.