How To Use Data: Examples from the charity sector


Data can be a powerful asset for a social change organisation. Good data analysis means organisations can better understand their clients, their services and their impact. More advanced techniques can help to predict changes in demand or to spot particularly at-risk or high-need beneficiaries.


Yet most social change organisations aren’t using data to its full effect - and many aren’t making use of data much at all. In this post we highlight five different organisations who are at the forefront of using data to guide their work.


Finding insight from text data to better understand the experiences of vulnerable families


Buttle UK gives out around 10,000 small grants a year to some of the most vulnerable children, young people, and families in the UK. These grants fund basic goods that most of us take for granted, such as beds, cookers, and fridges.


Buttle UK holds a huge amount of data on the grant applications they receive, including short written reports by support workers describing the families' circumstances. DataKind assessed common sequences of requests for support, with a particular eye for which requests tend to lead to others. For example, families who receive grants on the grounds of domestic violence often also apply for grant support with child health and development problems later on.


This analysis of text data - called Natural Language Processing or NLP - has traditionally been off limits for many social change organisations, due to the technical expertise required. As the tools and techniques for NLP improve, and the social sector increases its access to data expertise, NLP is likely to become more commonplace. For more examples of social sector uses of NLP, see here.


Computer vision to find villages in need


DataKind worked with non-profit GiveDirectly, which provides financial aid to people around the world. They identify some of the poorest households in rural Kenya and Uganda and send money via mobile phone transfer - a simple, radical idea that has been proven to work.


But it is both difficult and labour-intensive to identify which villages are most in need. From experience, GiveDirectly knew that the material each family uses for their roof is a proxy for their level of poverty. If families have the money to do so, they invest in a metal roof. Those who can’t afford metal make thatch roofs.


DataKind worked with GiveDirectly to use computer vision to solve the problem. Computer vision uses machine learning techniques - essentially finding patterns in the data - with satellite imagery to identify, on a village-by-village level, the proportion of thatch and metal roofed homes. The result was a powerful proof of concept, showing the potential of these techniques in development and humanitarian work.


Using predictive models to ensure the most in-need to get support first at a food bank


The Welcome Centre is a food bank based in Huddersfield, UK. They provide support to people in crisis, offering practical help in the form of food, toiletry, and household support packs. For those who need it, a support worker can provide advice on underlying problems, and help them avoid becoming dependent on the food bank. Identifying those most in need of support, who are therefore most likely to become dependent, is challenging, and currently done manually by a support worker.


DataKind UK and The Welcome Centre partnered to build a system that could identify a client’s likelihood of needing additional or longer term support. The aim was to create a probability score that would aid the support worker to decide, in conjunction with other information, whether a client is likely to need extra support. This is another type of machine learning - or pattern recognition - where we find patterns in historical data and see if they are applicable to future cases.


Using this information, The Welcome Centre improved the accuracy and efficiency of the targeted work that the support worker undertakes, enabling them to make earlier interventions before a crisis escalates.


Segmentation to understand outcomes among young homeless and vulnerable people


Computer vision, predictive modelling, and text analysis are considered exciting techniques by many data scientists. But it’s often the more foundational techniques that can be the most useful - and lead to the most impact - for social change organisations. 


Welsh charity Llamau supports young homeless people and vulnerable women. They wanted to better understand who benefits from their services. Among the techniques used on this project was segmentation - when beneficiaries are divided into groups of ‘segments’ and compared to see if each segment experiences the same or different outcomes. 


In Llamau’s case, the analysis showed the considerable variation between outcomes for different segments of Llamau’s beneficiaries, with less positive outcomes for people who were male, ex-youth offenders, or previously in the care system. When an individual fitted into two or more of these categories at the same time, the likelihood of a successful outcome dropped even further. These insights helped Llamau to improve the services they provide to individuals.


Using network analysis to uncover corporate corruption


Global Witness is an NGO that campaigns to end the environmental and human rights abuse that is driven by corruption and the exploitation of natural resources. DataKind UK partnered with Global Witness to better understand networks of corporate ownership by analysing a newly opened register of UK companies. This information has the potential to lift the lid on chains of corporate ownership and uncover webs of corruption that were previously far more difficult to investigate.


Together, the project team created a network graph. This is a way of connecting entities in the data - making a web instead of a table. It allows the data to be analysed based on how each entity (person, business, place) is related to other entities.


This network analysis can be used to discover ‘red flag’ activities that might indicate nefarious behaviour - like the thousands of UK companies that are owned by other companies in tax havens, potentially unlawfully. Some of these tax-haven-owned companies are also in receipt of government contracts. 


Get involved


DataKind UK is a charity that supports social change organisations to use data and data science, through a large network of pro-bono data scientists. If you’d like to get involved with DataKind UK, learn more about our (free) programmes and apply for support. 


Datawise London is here to help small charities and community organisations in the capital unlock the potential of data to shape activities and services to meet local need. We support small organisations as they start their data journeys.


You can take part in free training or get one-to-one advice to help you better collect information and stories that count, manage your data and analyse the results. All with free and affordable technology.