I Am Me: The challenges of collecting equalities data
by Christine Goodall, Network Coordinator of the HEAR Equality and Human Rights Network. This has been cross-posted from the Data4Good Festival website as part of a series of posts written by speakers at the 2021 Data4Good Festival.
Increasingly, charities and social enterprises need to collect equalities data about their service users, beneficiaries, staff, trustees, and volunteers. Data is often collected because it is required by funders, but it is important to use this data to improve inclusivity, service delivery, and recruitment practice.
Earlier this year, HEAR Equality and Human Rights Network, with the Social Investment Consultancy, delivered a session at the Data4Good Festival on these subjects. The aim was to challenge participants to think about why and how we collect equalities data, especially to ensure that it isn’t just a tick-box exercise.
Why did HEAR and the Social Investment Consultancy work together on this?
The HEAR Network has been working for some time on a variety of issues related to the collection of equalities data. We support member organisations to think about how and why they collect this data, as well as learning from members who are experts in their own specialisms, and trying out different ways to improve our practices. We have worked with partners like London Plus and Race on the Agenda, and with colleagues from the Office of National Statistics. We have revised our own monitoring forms and thought about the Census, what equalities data is collected, how, and why.
At the same time, we have been doing a lot of thinking and learning about intersectionality, and developing our understanding — and that of our members’ — around our complex identities and how best to reflect and respect these in the data we collect.
We were pleased to be able to work with Bonnie Chiu and the Social Investment Consultancy by contributing to the development of the DEI Data Group population framework, where we discovered a common interest in equalities data, and developed a session for the Impact Allowed event. When we had the chance to develop this session further for the Data4Good Festival, it was a great opportunity.
Why ‘I am Me’?
The title for our session came from someone who anonymously completed one of HEAR’s monitoring forms with this phrase when invited to self-identify. This sums up exactly what we mean when we talk about inclusive equalities monitoring: nobody should be forced to reduce themselves to a set of narrow tick boxes if we can possibly avoid it. Of course, we often encounter constraints in how and what we can collect, including from funders.
Making assumptions
We started the session with some ground rules. Discussing this topic can sometimes be difficult, so we need to be confident in a safe space where there are no ‘wrong answers’ or ‘stupid questions’. Saying that, the participants were then challenged to deal with some of these difficult issues in our opening ‘assumptions game’.
First, they were invited to contribute something about their own unique identity. Some shared characteristics like sexual orientation or gender identity, others shared things like hobbies or jobs. This gave them the opportunity to start thinking about facets of self and self-identification. Participants were then put into small groups in breakout rooms and asked to form assumptions about their fellow group members, and share these assumptions with that group. They were then asked to share how they felt about this process.
Many people found the exercise very uncomfortable — they were not used to verbalising assumptions, and were worried about saying ‘the wrong thing’. However, it created the opportunity to reflect on how and why we sometimes make assumptions; for example, based on a person’s name, or physical characteristics. Participants could also think about what it was like to have assumptions made without the opportunity to self-identify, for example by providing gender pronouns.
This sort of discomfort is what we may inadvertently subject our stakeholders to when we seek to collect equalities data from them. How can we overcome this challenge?
Collecting data with transparency
First and foremost, we need to critically assess the purpose of collecting equalities data, and be transparent about the reasons to those from whom we collect it. Collecting equalities data is an important way for the voluntary sector to achieve its mission: to increase impact on the most marginalised; to ensure everyone is reached; and to identify gaps or needs in service provision. But often we collect data without asking why. Or if we do know why, we do not clearly communicate it to the people we collect the data from.
Secondly, there is always a balance to strike between the need for collecting harmonised and structured data and allowing for self-identification. That balance constantly needs calibrating. Organisations can look to follow frameworks such as the DEI Data Standard, which is currently being adopted by funders.
They can also try seeking input from the relevant communities of interest, while actively including space for self-identification and completing forms ‘in your own words’. At our session, we discussed the challenge of balancing these concerns, and analysing data when more than fixed categories are being used.
Finally, all categorisation frameworks can be contested, or have the potential to offend or exclude. To mitigate these risks, stakeholders should always be involved in the design of equalities data collection processes. This includes reviewing or designing the questions, deciding how questionnaires should be administered, and deciding how the data can be shared.
Collecting equalities data should never be an extractive exercise and should be in line with the ethos of equity and inclusion — the very reason why we want to collect equalities data in the first place.
Find out more about HEAR Equality and Human Rights Network on their website, or on Twitter.
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