How do organisations analyse this data?

Section 5

How do organisations analyse this data?

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Understanding and improving data quality

The more robust your data set, the more insight you will gain. While organisations will ideally aim for a response rate of 80% of their population, you can still gain insight with less (60% is suggested as a minimum). If response rates are low, you may wish to begin your analysis on areas with higher disclosure, and supplement this with focus groups to gain initial insights.

Having gathered and reviewed diversity data, organisations could then look to communicate findings with their employees and, potentially, externally. Using the data, you can identify areas of the business with lower rates of disclosure and use targeted communications to positively engage employees in low response areas on how the data is being used, why data is needed and how responses will drive actions. Many organisations have found this transparency after the data has been first collected has significantly improved response rates on an ongoing basis.

Response rates can also be improved by making data collection part of ‘business as usual’ activity by embedding it within standard processes such as annual year end, training or compliance exercises. It is also beneficial to review other employee communications where nudges on data collection may be included, such as whenever new diversity initiatives are launched.

Calculating and disclosing pay gaps and other metrics

Once you have gathered your data, you will need to decide what information you wish to report on and share with leaders, employees or externally, with a clear focus on the messaging and narrative that supports any disclosures.

In the UK, this is most commonly seen through pay gap reporting given the existing regulation around gender pay gap reporting. When calculating pay gaps beyond gender, you first need to agree your methodology for calculating them and on what basis you may wish to disclose (e.g. for ethnicity this could be Ethnic Minority vs Ethnic Majority or broken down to individual ethnicity categories).

If you have previously calculated your gender pay gap, you may wish to replicate your calculations with other diversity dimensions to maintain consistency in your approach. Consider consulting your key stakeholders, including employee networks, to understand their views and expectations.

Beyond pay gaps, you may wish to track and disclose other metrics to build accountability and encourage positive change. Organisations often disclose representation across grades, seniority levels or divisions but each organisation may choose to focus on additional metrics that they would like to improve on e.g. recruitment proportion or differences in average tenure.

Understanding requirements from investors and other organisations can help you decide on additional metrics you will want to track, for example, you may want to have the ability to provide prospective clients an insight into the diversity composition of your investment teams. You may also wish to understand how diverse the organisations you are working with are, but as you begin to request and metrics

from other organisations, it is important to be able to demonstrate that you have calculated and analysed this data yourself.

When communicating insights, it is important to acknowledge the quality of the data, you may wish to share the level of self identification and the size of the cohorts being compared to provide an understanding of how robust, reliable and statistically valid your analysis is.

Detailed analysis and integrating insights

In order to fully understand your data and uncover insights, detailed analysis, looking across the entire employee lifecycle, is often critical to understanding where there may be issues or differences. This can help you investigate variances in how different groups enter, grow, and leave your organisation, and understand where action may have the biggest impact.

Taking entry level recruitment as an example; you may wish to look at the diversity of the applicants at each stage of the recruitment cycle, e.g. looking at the proportion of applicants from a lower socioeconomic background and how this proportion changes through CV sifts, assessment centres, interviews and ultimately, offers. This can give you an understanding of whether there is evidence for biases in the recruitment process.

Similar analysis can be undertaken to help influence talent management and succession planning with a diversity lens, identifying the next generation of leaders and using diversity data to ensure that people from all backgrounds are able to succeed will help you proactively improve diversity in leadership.

When analysing the data it is valuable to break the data down into categories and sub categories in order to fully understand the differences between them; for example breaking down ethnicity into subgroups may show interesting trends or disparities between people of different ethnicities. Once you have uncovered these insights, or found areas for further investigation, you may wish to test what may be causing these differences and conduct further analysis, in order to identify possible interventions that may help mitigate any biases. It is also important to look at how intersectionality affects your people. Looking at data holistically through multiple lenses can give you a deeper understanding of the lived experiences of your employees.