Using data science to understand and respond to your users.
Case Study: DataKind UK
DataKind UK is a small charity that supports other social change organisations to use data and data science to transform their impact. The Charity Digital Code of Practice is clear about the value of digital work being user-led. DataKind UK have taken this advice and built in time to engage more with the charities they support and with data scientists who volunteer with them.
“Like any organisation, charities can learn a lot about what they do, who they serve and the impact they have, by drawing on their data. Crucially, they can also use data and data science to improve their services, much the same way that the private sector does.”
Giselle Cory
Executive Director, DataKind UK and Code Champion
The Challenge
Charities know they work best when they are led by their users, but how can we really know what our users want? Without data to go off, any decision is just a best guess.
Charities gather data all the time: about our service users, our donors, our social media followers. Some of us view data as a liability - the mere mention of GDPR is enough to make many panic. But DataKind UK urges us to see it as an opportunity. Insights from data can and should inform our strategies.
The Solution
Giselle Cory of DataKind UK describes digital and data as inextricably linked - when you use the former, you get the latter. Any digital service generates data on those using it, and this can be harnessed for good.
“Like any organisation, charities can learn a lot about what they do, who they serve and the impact they have, by drawing on their data. Crucially, they can also use data and data science to improve their services, much the same way that the private sector does.”
As charities, we can use digital tools to analyse data, to get better at understanding who our users really are and allocate our resources more effectively.
The Results
DataKind UK have found the Code useful in measuring their own performance against good practice, as well as in supporting the charities they work with.
For example, they’ve worked with food banks to build a machine-learning tool that helps identify which people might need more long-term support, allowing the charity to focus their resources in the right places.
