We in the EU Platform of Diversity Charters engage with signatories to provide support and encouragement. In recent times, as we all strive to show what we are doing to promote diversity and inclusion in the workplace, questions about the most effective and useful data collection and measurement approaches are becoming more frequent. This is the first in a series of blogs that explores the issues around data collection and measurement.

As signatories, you continue to develop and implement diversity and inclusion strategies that are yielding results for your organisation and the people who work there, but there are challenges when it comes to measurement. 

The Question of Data

Data is critical because it provides evidence, for potential investors, potential employees, potential business partners and potential customers or service users, to demonstrate the impact of diversity and inclusion activity in our organisations.

External actors look at data, not just investors but potential employees and customers. They check available data to make their assessment of brand reputation. Internally, staff look at the available data to assess it against their experience and perspective of working in the organisation.

With the right data, it is possible to develop indicators that provide the information needed to assess progress in achieving d&i objectives. Data, when collected and collated into indicators can show change aligned to business objectives, for example, achieving recruitment targets, increasing diversity in workforce composition, improved staff performance scores, higher scores for a sense of belonging, progress on sales and procurement targets, enhanced success in innovation generation, increased participation in learning and development opportunities across the organisation.

Organisations collect a range of data on a regular basis, the key here is to be clear about what data is important to measure progress over time, to show the contribution d&i activities make to achieving organisational aims and objectives.

Human resource data is collected to report on staff performance, employee retention, talent attraction and training delivery. The data used here is generally collected in organisational administrative systems, and the indicators are generally numerical, changes in figures related to, for example, the number of people who apply for jobs and trends across different diversity dimensions, the number of people across different dimensions that succeed in interviews, the number of people across different dimensions who participate in training against their length of time working in the organisation, changes in staff performance ratings, changes in scores related to sense of belonging.

Organisations also collect data to produce corporate annual reports on profits, sales, market share, return on investment, return on equity figures, gender balance on boards, promotion of corporate social responsibility activities etc. The data used here is sometimes collected for regulatory purposes, some is administrative data and some data is qualitative, examples of activities in the organisation.

More frequently, organisations are conducting polls which record the opinion or vote of those asked question/s at a specific time, pulse surveys which are a quick/short survey, to check-in with employees or sample surveys with a statistically representative number of participants answering to indicate/show what the whole is like.


In addition, organisations undertake full surveys, often annually and conduct focus groups to collect information that generates additional data. All data sources can be used to create indicators that collectively make-up the metrics, the sets of measurements that help to pinpoint the actions that produce positive change that has, or is, taking place. Data, irrespective of why or how it is collected, can be used to develop three types of indicators, output, outcome and impact indicators, once the proper procedures and guidelines are applied to ensure data protection.

We need to take time to decide what we want to show and learn and then decide how that is best illustrated to others.  It is not always advisable to measure everything straightaway. In fact, by selecting some actions first and taking the time to generate learning we can avoid making mistakes.  Then we need to do an audit to reveal what data is currently collected, administrative data, survey data and other sources, and is that data useful in generating an understanding of what has and is changing.  Check how frequently that data is collected and does that frequency allow you to decide on an indicator that can show real or meaningful change. 

These are the first steps. In the framework laid out below (in downloads) you will find a guide to stimulate the discussion and help to start you on your measurement journey.