ETHNICITY DATA GATHERING
Given the typically low levels of representation across the sector, most organisations will struggle to present their ethnicity data in a meaningful way when trying to align with the 18 categories recommended by the UK government.
We recommend use of the 5 broader categories for organisations with less than 5,000 employees, as it is unlikely that you will obtain statistically significant data for many of the categories. It is also important to pay attention to those choosing to self-describe. If there are significant numbers of individuals self-describing as a specific ethnic identity, it might be worth adding in further categories in future data collection activities.
At an organisational level, there might be sufficient ethnic diversity to allow the presentation of different ethnicities, however when looking at smaller cohorts (i.e. teams and regions) ethnic groups may need to be aggregated. With this in mind it is helpful to present the majority group (typically White/White British) in contrast to the aggregated minority group (i.e. Black, Asian and minority ethnic). This approach should only be used in situations where the presentation of individual ethnicities might run the risk of undermining employee anonymity.
1. Be clear on your aims - Data collection can be quite sensitive for some individuals. Organisations must be as clear as possible about what the data will be used for and potentially more importantly, what the data will not be used for. There is often a fear that underrepresented groups will be at a disadvantage if they disclose their ethnicity and/or other protected characteristics. GDPR also categorises ethnicity as sensitive personal data, therefore organisations must be clear about the protections that they are putting in place. If staff are expected to disclose their sensitive personal data, they must understand the wider perspective of why their data is needed.
2. Anonymise data to protect respondents - Where ethnicity data is being collected, it is important that organisations ensure that there is no potential for individuals to be identified through the data. This is especially important when personally identifiable information is paired with data about people’s experiences or perceptions. The last thing an organisation should do is expose underrepresented or marginalised groups. A rule of thumb would be to aggregate or hide all responses where there are less than 5 individuals within a particular cohort
3. Demonstrate independence - Where possible, organisations must seek to demonstrate the independence of their approach to collecting data about ethnicities and other sensitive personal data. One of the reasons for poor disclosure of ethnicity data (and other personal data) is the perception that this will somehow be used against the employee. There is also often a fear that if employees disclose anything personal or sensitive, this might be disclosed to others unwittingly. The best way that organisations can overcome this is by partnering with an organisation that is completely independent. Another way to get round this is to ensure that there is full transparency about the controls and balances that are in place to mitigate these concerns. Organisations must ensure that they create a psychologically safe environment where employees feel that they can share their honest views without fear of negative consequences, which may impact an employee’s sense of self, their status and/or career prospects.
4. Make data visible and easy to understand - Any effort to obtain EDI data from employees should result in them having access to the analysis of that data. Organisations often cultivate a lack of trust in their secrecy around data. Another issue that organisations have in dealing with data is that it is often presented in a manner that is inaccessible and difficult to understand. Data should provide a good indication of what is happening within the organisation and any gaps that need to be addressed.
ETHNICITY - KEY STEPS
1. Define the scope of your data request - Make it clear what data you are collecting, what you will do with it and why this is important. The minimum level of data you should collect is quantitative (demographic data focused on protected characteristics – considering intersectionality, where possible) and qualitative (insights about the inclusivity of an organisation). Additional data can be cross correlated with diversity data in relation to remuneration (pay and bonuses), performance management, progression, attrition and learning and development.
2. Communicate regularly and transparently - Employees tend to be suspicious of organisations that only communicate or talk about EDI when it is time for the annual data collection process. Employee trust is earned over time and is dependent on consistent, honest and transparent communication. This communication must be multifaceted, employers should gather opinions and insights from their employees on a regular basis and act upon these to build trust over time. Commit to a regular communications schedule with at least quarterly updates.
3. Commit to actions following data collection - Once you’ve collected data from employees there needs to be a firm and measurable commitment to taking action to address the most significant gaps across the organisation. Actions must be specific and relate to the demographic(s) of concern and target the specific issue(s). The organisation should also commit to reviewing actions and interventions on a regular basis to identify the impact of these actions and any improvements that can be made.
4. Partner with an EDI data expert - Data collection, analysis and visualisation can be a complex endeavour, which might take organisations a significant amount of time to get right. An external organisation can prove the independence needed to reassure employees, whilst also providing expert opinions on how best to overcome any data challenges. External parties can also often provide a greater level of scrutiny and critique.
BEST PRACTICE GUIDE 2021 - INTERACTIVE
ETHNICITY DATA GATHERING
Gather quantitative and qualitative data. Consider starting with new recruits.
Be clear on aims - what will the data be used for and not be used for.
Anonymise data to protect respondents - rule of thumb, aggregate or hide all responses where there are less than 5 individuals within a particular cohort.
Demonstrate independence – consider partnering with an independent EDI organisation or ensure full transparency about controls so employees feel safe to contribute. An external organisation can prove the independence needed to reassure employees, whilst also providing expert opinions on how best to overcome any data challenges. External parties may also provide a greater level of scrutiny and critique.
Make data visible and present it in an easy to understand format.
Commit to actions following data collection - firm and measurable commitment to taking action to address the most significant gaps across the organisation. Actions must be specific and relate to the demographic(s) of concern and target the specific issue(s).
Review actions and interventions on a regular basis to identify the impact of these actions and any improvements that can be made.