Case study A: The Charity Upskiller

Kate White is a Manager at Superhighways, a VCSE data support provider working in London to support other charities that want to advance their use of data. Superhighways’ largest client group is small, practice-oriented charities that deliver on-the-ground support for disadvantaged people, often including people experiencing poverty. Clients generally have annual incomes of less than £500,000, and more than 40% have less than £100,000. 

Typically, Kate’s client charities are at very early stages of developing digital and technical competencies. Her support therefore consists of things like consultancy on how to collect and manage data, trainings on how to analyse it using tools like Excel or Power BI, and raising awareness of external tools for viewing public data. 

A consistent theme across her clients’ experiences is that, rather than struggling with one specific data gap, they tend to encounter a chain of barriers, some of which are: 

• Knowing what questions to ask. Many frontline organisations begin with a general sense that they need data to support funding applications, demonstrate need, or plan services, but they do not necessarily start with a clearly defined research question.  
• Knowing what data is available and where to get it. The landscape of available data—national statistics, local authority data, health data, census data, and third-sector dashboards—can feel overwhelming, especially for inexperienced users.  
• Understanding the unique value in different dashboards or datasets that they might use, and which one is best for their purposes.  
• Having the skills and confidence to effectively use and interpret data. Even when data is publicly available, users need at least intermediate Excel skills to use it effectively. Tasks such as cleaning data, creating pivot tables, or comparing indicators across years can be significant barriers. 
• Having useful infrastructure (e.g. a Customer Relationship Manager, or CRM) in place to collect their own data to complement or fill gaps in public data. 

Each of these barriers are challenging for Kate’s clients to overcome without structured support because doing so takes time and resources, both of which are usually scarce. 

Kate also pointed out that her clients sometimes struggle with some of the same gaps we mention elsewhere (such as in Case Study B), such as finding data with the desired combinations of demographic and geographic information. The interventions they want to implement normally benefit from data at hyper-local levels wherever possible.  

In practice, Kate finds that her clients rely on tools like dashboards displaying data from the Index of Multiple Deprivation (IMD), for instance. Another helpful resource is the Local Insight platform from Oxford Consultants for Social Inclusion (OCSI), which holds a wide range of local-level information from a variety of sources, with the flexibility to zoom in to lower-level geographies where data is available. 

While those resources are good examples of the kind of data that tends to be helpful for these organisations, there is still plenty of additional support that is necessary to fully include frontline charities in the UK’s data ecosystem. Indeed, Kate’s team has played a key role in delivering that support, and many of the charities that Superhighways supports have successfully improved their capacity for using data to enhance their work. Many now make regular use of public data in combination with their own data to inform their services and make the case to funders that their interventions are working. 

At the same time, there are some systemic shifts that could prove helpful as well, especially at a scale that could shift the way the sector as a whole works. Many of these could be led by government producers of statistics, such as: 

• Better cataloguing of all datasets held by key departments like the ONS, DWP, and others. This should ideally be a unified resource that accessibly presents the datasets, variables they contain, what they are useful for, and how to access them.  
• More frequent and well-advertised trainings on how to use key government datasets as well as more dashboards and data tools meant for people without substantial technical skills. 
• Development and investment into structured, “intermediary” models for improving data access for frontline charities. This could learn from the model already built by Superhighways. 

The key takeaway is that improving the usefulness of poverty-related data for frontline organisations means meeting them where they are to help them overcome the multitude of barriers they face. Scaling that support will mean producing better, more user-friendly data resources, opening up more local-level data, and providing sustained training and consultancy to build data skills and confidence.