Data Poverty Lab: My first four months

Digital exclusion means social exclusion

I’ve been involved in the digital sector and studied social and health inequalities for many years, but this is my first time explicitly looking at data poverty. If anything, it’s made me somewhat ashamed to realise how data poverty gets skirted over. I remember in past trials looking at online healthcare services, if someone couldn’t afford a broadband or mobile connection to get online, they might simply be excluded from the project. This reflects how it can be much easier to minimise digital exclusion as niche or as someone else’s problem. This risks perpetuating the issue - that people experiencing data poverty are neglected from essential products and services. 

As someone coming into this specific world with fresh eyes, I wanted to share some headlines that I’ve learned in my first four months in the Data Poverty Lab. 

The impact of data poverty

Data poverty is not one profile – it affects a wide range of different people. It also impacts some of the most vulnerable and underserved people in our society. Those in extreme need, such as unhoused people, refugees, and people escaping domestic violence, are reported as frequent users of services such as the National Databank.

While it is hard to say if the number of people experiencing data poverty is increasing or staying the same, the extent of the inequality those people experience is undoubtedly growing. This is due to so much of our lives now being played out digitally. Our social and working lives, our education, our healthcare, our local councils, our banking, are all predominantly online.

We need long-term solutions

Inspiring collaborations have emerged to address data poverty (see examples in Kat Dixon’s report on Scaling Solutions; or the Greater Manchester Digital Inclusion Social Housing pilot). But sustainability is a big concern. Some collaborations emerged in the pandemic as a crisis response, and aren’t built for the long term. There is a need to consider how data poverty solutions should be funded, including how the staff or volunteers delivering those solutions are supported.

There is no single solution. This makes sense given the diversity of people impacted, and also the variety of settings where solutions are offered. But there are concerns about gaps in support and the risk of a mismatch between where there is need and what help is on offer. These worries about inconsistency and fragmentation mean that a strategic approach is needed, to turn the diversity of solutions into a strength rather than a potential risk.

Poverty is hard work, and data poverty makes this even harder. One participant in my research referred to the “three dimensional chess” of trying to juggle a budget that doesn’t go far enough, which is made even harder when experiencing digital poverty, as so many budgeting tools, benefits calculators, and deal comparisons are online.

Looking through the lens of 'implementation science'

While my previous work in healthcare inequalities didn’t shine a light on data poverty in the way I now think it should, I see several parallels between implementing healthcare treatments and the challenges facing solutions for data poverty. I evaluated healthcare through the lens of ‘implementation science’ which focuses on understanding the practical realities of delivering support, understanding how different contexts and settings can mean things work differently in different places, and recognising people themselves – both the people accessing help and those delivering it – as crucial.

There are several lessons from implementation science that chime with the emerging findings from my Data Poverty Lab research so far. One is the huge importance of access, both in terms of people knowing a solution exists, and understanding the barriers that can get in the way of them using it. An excellent service or a great product, without active effort to reach people and make them aware of it, can end up unused. We’re seeing this in several ways in the Data Poverty Lab, from information about switching to broadband social tariffs being too hard to find - through to organisations having a surplus of free SIMs despite knowing they’re in places where people need them. Thinking about where people encounter problems in their own lives can be one way to approach this, making sure information is where people are, and engaging different services to be active in signposting them.

Implementation science includes using behavioural models, such as COM-B (Capacity; Opportunity; Motivation) to think through the challenges a person can experience and how we can simplify that ‘three-dimensional chess’ match.

1. Do they have the Capacity to decide, for example can they access supporting information accessible to them?
2. Do they have the Opportunity, for example, is it quick to find the solution or does it require time they don’t have?
3. And lastly do they have the Motivation, recognising that motivations can be complex and conflicting – for example, do they like the idea of free data but feel embarrassed asking about it? Do they like the sound of a lower tariff but feel anxious about being stuck in a long term contract? 

The challenges of sharing successful approaches so that good support can be more universally available is also a common implementation concern. Whether or not something can ‘scale’ can be seen as ‘can we replicate exactly this thing, somewhere else?’ 

Implementation science would advise us to think carefully about how a place, an organisation, and a solution all interact to make something work. Particular networks, ways of working, local champions and advocates, can be key ingredients in making something effective. This means successful scale can actually be about flexibility, how a solution can fit into a different place and make use of the particular strengths there, rather than just thinking about copying a solution from one place to another. This requires us to understand and respect local variation and work with people who best understand their area and their communities. 

The importance of lived experiences and collaboration

Crucially, this recognition of the value of different expertise needs to include the expertise of people experiencing a problem first hand. In the Data Poverty Lab, we’re using the CHESS framework, which was co-produced with people with lived experience, to consider different solutions from the perspective of people who need to use them. 

This spirit of collaboration, and the commitment to work and learn with others is at the core of the Data Poverty Lab approach. While I’m excited to bring in learning from my past work, I’m even more excited to continue to hear from the wide range of people working to address data poverty. We’ll continue to share what we learn, as we continue to delve into what solutions can help us tackle data poverty, and what can be done to help people experiencing the impacts of data poverty in their lives.