Equality Diversity and Inclusion Data Toolkit


Collecting Equality, Diversity and Inclusion (EDI) data will only be meaningful if the culture of the Fire Rescue Service (Service) supports openness and acceptance of difference. The process of collecting and analysing EDI data will provide evidence of an inclusive culture and help to identify areas that require improvement with a deeper understanding of our staff and their lived experiences. We can use this data to identify any existing biases, gaps or issues and work towards improving them for our staff and the communities we serve.

Our legal duty to analyse data

Equality Act 2010

The Equality Act 2010 replaced the existing anti-discrimination laws with a single Act and included a duty on public sector organisations. More information about the Public Sector Equality Duty can be found here.

The General Equality Duty which is set out in Section 149 of the Act states that: A public authority must, in the exercise of its functions, have due regard to the need to:

  1. eliminate discrimination, harassment, victimisation and any other conduct that is prohibited by or under this Act;
  2. advance equality of opportunity between persons who share a relevant protected characteristic and persons who do not share it;
  3. foster good relations between persons who share a relevant protected characteristic and persons who do not share it.

With the purpose of (Part 2(2b)):

  1. removing or minimising disadvantages suffered by persons who share a relevant protected characteristic that are connected to that characteristic;
  2. taking steps to meet the needs of persons who share a relevant protected characteristic that are different from the needs of persons who do not share it;
  3. encouraging persons who share a relevant protected characteristic to participate in public life or in any other activity in which participation by such persons is disproportionately low.

In having “due regard” to matters set out in The Equality Act 2010, it is essential we consider any impact on people with protected characteristics. This often requires evidence gathering, engagement or consultation with people affected.

Collecting Equality, Diversity and Inclusion Data

In order to set strategies, identify effective practices for diversity, public sector organisations that need good quality equality data on their people and the community they serve.

There is a need for the Sector to consider the equality data in all that they do; across service functions and workforce, to provide a national picture from which trend analysis can be drawn. As a sector, we need to understand the nature and extent of challenges, for example, inequality in career progression, or barriers to participation in work for those with disabilities or caring responsibilities. There is a wide range of data to collect and analyse relating to EDI.

Workforce analytics

Case study research by The Chartered Institute of Personnel and Development (CIPD) provides examples in UK practice of workforce analytics that include:

  • descriptive measures, such as workforce characteristics – for example the distribution of:
    • Age, gender, tenure, disability and sexual orientation or gender identity.
    • How other key points of people data, such as pay, promotions, employee turnover, and
    • Grievance data, varying by diversity criteria.
    • How performance varies according to age and tenure.
    • Measures of efficiency and effectiveness, such as levels of participation in diversity and ethics training, and other assessments of diversity policies.

Community analytics

Data that support:

  • Insights about our communities.
  • Understanding behaviour and lifestyle preferences.
  • Honing in on certain demographics or geographic areas.
  • Targeted, niche campaigns and reaction monitoring to national campaigns.

Harmonisation of data

Harmonisation is about making statistics and data more comparable, consistent and coherent. Harmonisation can include:

  • Using the same words in questionnaires, interviews, and administrative data collection.
  • Producing statistical outputs that use the same categories.
  • Using consistent data formats and storage, and methods.

Why harmonise?

There are two key benefits: getting more from your data, and meeting required standards.

Get more from your data

Harmonisation allows analysts to gain deeper insight and value from their data. This delivers more meaningful statistics that give users a greater level of understanding and better meet user needs. Cost savings can be achieved by avoiding duplication.

Meet the required standards

Harmonisation is required by the Code of Practice for Statistics, which says in its sound methods’ principle:

Statistics, data and metadata should be compiled using recognised standards, classifications and definitions. They should be harmonised to be consistent and coherent with related statistics and data where possible.”

This was underlined by the Women and Equalities Select Committee report on the Race Disparity Audit, which recommended:

“The government, led by the Cabinet Office, should adopt the same categories as are used in the Census as the minimum standard for data collection on ethnicity across government departments”

Commission on Race and Ethnic Disparities The Report highlighted the need for organisations to harmonise their data.

The use of harmonised classifications

This section gives additional details about:

  • The use of harmonised classifications to collect ethnicity and other information
  • Reporting on ethnicity information
  • Ethnicity data collected on Race Disparity Unit’s Ethnicity Facts and Figures
  • Recommendations from the Commission on Race and Ethnic Disparity
  • The Code of Practice for Statistics
  • Feedback on processes

Whilst this section focuses mainly on ethnicity, information on harmonised categories for other characteristics where umbrella terms are used is also recommended.

The Office of National Statistics and the Race Disparity Unit strongly recommend the use of the harmonised standards as the basis for collection, analysis and reporting on ethnicity data (and for other characteristics for which harmonised standards exist). The current Office of National Statistics guidance on the methods they use to produce statistics. This includes classifications, harmonisation, best practice, geography and user guidance for a wide range of data.

The current Government Statistical Service ethnicity harmonised standards are available here. Note that different harmonised standards apply for England and Wales, Scotland and Northern Ireland. They are based on the 2011 Census, and for England and Wales consist of 18 groups (often called the “18+1 categories” (the +1 are people who don’t give their ethnicity).

Harmonisation is key in maximising the value of data collected and the resulting statistics – it ensures commonality in the use of definitions, survey questions, administrative data, and the presentation of outputs. It gives users a greater level of understanding and better meets their needs. Cost savings can be achieved by avoiding duplication.

This is about harmonisation, not standardisation. Data for ethnic groups other than the 18+ can certainly be collected, but they should fit within the harmonised standards, and allow users to unambiguously get back to the 18+1 classification.

Pseudonymising data

Pseudonymisation is a technique that replaces or removes information in a data set that identifies an individual.

The UK General Data Protection Regulation defines pseudonymisation as:

…the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organisational measures to ensure that the personal data are not attributed to an identified or identifiable natural person.

Pseudonymisation may involve replacing names or other identifiers which are easily attributed to individuals with, for example, a reference number. Whilst you can tie that reference number back to the individual if you have access to the relevant information, you put technical and organisational measures in place to ensure that this additional information is held separately.

Pseudonymising personal data can reduce the risks to the data subjects and help you meet your data protection obligations.

However, pseudonymisation is effectively only a security measure. It does not change the status of the data as personal data. Recital 26 makes it clear that pseudonymised personal data remains personal data and within the scope of the UK General Data Protection Regulation:

… Personal data which has undergone pseudonymisation, which could be attributed to a natural person by the use of additional information, should be considered to be information on an identifiable natural person…

Anonymising data

It is important to remember that any information held by public bodies may be subject to the Freedom of Information Act 2000 and The Environmental Information Regulations 2004 as well as the Data Protection Act. As such, statistical data disclosed in response to a request for information, or being made publicly available should be depersonalised in a way that no living individual can be identified from those data and any other data that in the possession of, or likely to come into the possession of, the data controller. Any statistical data that includes personal data should therefore be appropriately classified in accordance to the Government Security Classifications.

The Data Protection Act 2018 and the Information Commissioners Office Guide to the UK General Data Protection Regulation

The Data Protection Act makes provision for the regulation of the processing of information relating to individuals, including the obtaining, holding, use or disclosure of such information (personal data).

Personal data means any information relating to an identified or identifiable person (data subject). An identifiable person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person.

Processing means any operation or set of operations which is performed on personal data or on sets of personal data, whether or not by automated means, and includes: collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction.

The Information Commissioner is the UK regulator for information rights laws and also issues guidance on UK information rights laws. Find out about your obligations and how to comply, including protecting personal information, and providing access to official information here.

All staff are responsible for ensuring that the data controller comply with data protection law and that personal data is processed lawfully, fairly, transparently and securely. The Information Commissioner’s Office has issued guidance: Anonymisation: managing data protection risk code of practice. This code explains the issues surrounding the anonymisation of personal data, and the disclosure of data once it has been anonymised. It explains the relevant legal concepts and tests in the Data Protection Act. The code provides good practice advice that will be relevant to all organisations that need to convert personal data into a form in which individuals are no longer identifiable.

The Information Commissioner’s Office Guide to the UK General Data Protection Regulation is part of Guide to Data Protection. It is for Data Protection Officers and others who have day-to-day responsibility for data protection. It explains the general data protection regime that applies to most UK businesses and organisations. Furthermore, it covers the UK General Data Protection Regulation, tailored by the Data Protection Act.

It explains each of the data protection principles, rights and obligations. It summarises the key points you need to know, answers frequently asked questions, and contains practical checklists to help you comply.

Where relevant, this guide also links to more detailed guidance and other resources, including Information Commissioner’s Office guidance and statutory codes of practice. Links to relevant guidance published by the European Data Protection Board (EDPB) are also included for reference purposes.

You may also find other sections of the Guide to Data Protection useful:

Analysing EDI Data

Equality Impact Assessment is a useful tool to help you move towards improving or promoting equality. Carrying out an Impact Assessment will help you meet your legal duties.

The NFCC Equality Impact Assessment template provides a way of systematically assessing the effects that a policy, project or decision is likely to have on different people within your Service, and the communities it serves. It is designed to assist you to identify discrimination by analysing your policies, projects, practices, processes, procedures, services, and decisions, to make sure they do not discriminate or disadvantage people.

There is no one method for undertaking equality analysis; however, it is expected that the analysis must be undertaken before making policy decisions that may affect people with protected characteristics. Proper written records contribute to the transparency of the policy/decision and demonstrate compliance.

The NFCC Equality Impact Assessment template

To support services, NFCC has produced an equality impact assessment toolkit and assessment template for services to adopt. Carrying out Equality Impact Assessments will help you meet your legal duties as well as bringing a number of benefits. It will:

  • Ensure that your decisions impact in a fair way: where there is evidence that groups will be negatively affected by a decision, action should be taken to address this.
  • Make your decisions based on evidence: Equality Impact Assessment provides a clear and structured way to collect, assess and put forward relevant evidence.
  • Make decision-making more transparent: a process which involves those affected by the policy, based on evidence, is much more open and transparent. This is more likely to engender trust in decision-makers and in your decisions.
  • Provide a platform for partnership working: Equality Impact Assessment offers an opportunity for partnership working, supporting collaborative and co-ordinate financial decisions considering the impact on members of shared communities.

Find out more about who is protected from discrimination, the types of discrimination under the law, and what action you can take if you feel you’ve been unfairly discriminated against.

The responsibility for undertaking, reviewing and filing the equality analysis should be with those responsible for the given initiative with the help from an EDI professional, if available. It is recommended that the services’ equality manager, or other role responsible for the EDI will be the central point for enquiries regarding analyses undertaken.

The key is the quality of the analysis and how it is used in decision-making to improve outcomes, rather than the process itself. Any analysis must be proportionate to the impact of the initiative in question.

The Government Equalities Office and Equality and Human Rights Commission have produced specific guidance: Information and guidance on the Equality Act 2010, including age discrimination and public sector Equality Duty.

Reporting Equality, Diversity and Inclusion Data

Collecting EDI data is an important step in support of our diversity strategies, but the real value of the data is how we use it. Once we have collected the data, the next step is to evaluate it and draw lessons from it. By involving all sections of the Service, we will maximise the opportunities for feedback to address under-representation.

Reporting on ethnicity information

When reporting on ethnicity data, the following principles generally apply:

  • Report in as much detail as possible – ideally the 18+1 categories (plus more detail if it’s been collected as described earlier in this toolkit. There can be big differences in outcomes between different ethnic groups, and you want to show as much of that as possible. A longer discussion of these issues can be found here.)
  • Try not to aggregate data in a non-harmonised way, as this reduces users’ ability to compare with other datasets. As an example, you might conclude that for a certain set of data, the White Irish group is too small and risks disclosing individuals. It might be better to suppress that group, rather than add it in a bespoke way to the White Other group, for example. Doing the latter means that White Other (including White Irish) group is then not comparable with the White Other group in other datasets.
  • Even if you can’t show the full 18+1 classification for whatever reason, report as many ethnic groups as you can – each disaggregation adds value
  • Avoid using a binary White/Other than White classification, as it has no analytical value.
  • The Gov.uk website provides advice on how their writing principles ensure the content is clear, meaningful and trustworthy – Our writing principles – GOV.UK

Ethnicity data collected on Race Disparity Unit’s Ethnicity Facts and Figures

Ethnicity data for different public sector workforces are available on Ethnicity Facts and Figures here.

Recommendations from the Commission on Race and Ethnic Disparities

The Commission on Race and Ethnic Disparities final report recommended (page 76):

Yet, we acknowledge that to fully understand the challenges and realities of workforce representation, more needs to be done to improve data collection, monitoring and quality of analyses.

The report made recommendations to the Department of Education that could be applied to other public sector workforces, such as:

  • Collating the most robust data sets to allow trends to be identified and comparisons to be made taking account of age, demographics, professional background and geography.
  • The production of guidance on data collection, monitoring and analysis to better support understanding and drive policy interventions in this area, engaging and collaborating with local authorities across the UK because of the importance of local context and local data.
  • Setting clear expectations for governing boards on how to collect and publish data on board diversity, as well as how to regularly review their membership and structure.

Code of Practice for Statistics

The Code of Practice for Statistics is available here. The framework for the Code of Practice is based on three pillars – Trustworthiness, Quality and Value. Each pillar contains a number of principles and detailed practices that producers should commit to when producing and releasing official statistics. The Office for Statistics Regulation reviews compliance with the Code.

Whether or not data collected by Services is classed as Official, the Services can voluntarily apply the recommended code. This demonstrates that Services are following the code to help them produce analytical outputs that are high quality, useful for supporting decisions, and well respected.

A commitment to the Code pillars of Trustworthiness, Quality and Value offers the opportunity for an organisation to:

  • Compare its processes, methods and outputs against the recognised standards that the Code requires of official statistics.
  • Demonstrate to the public its commitment to trustworthiness, quality and public value.

The Aqua Book: guidance on producing quality analysis for government (publishing.service.gov.uk) sets out standards for analytical modelling and assurance, incorporating guidance on producing quality analysis for government.

Case Study

A Service had a need to collect information on people of Sri Lankan ethnicity in response to an initiative targeting that community, and they are worried that the numbers might be small.  They collect the data using a Sri Lankan category which sits under the Asian/Asian British group, and in terms of the 18+1 could be presented on its own and as part of the Asian Other group.

Many departments use extended lists like this, see for example Department for Education’s list here. (This is actually harmonised to the (older) 2001 Census harmonised definition, but the principle remains the same).

Office for National Statistics and Race Disparity Unit would encourage collection of data additionally for religion and national identity. Research by the Office of National Statistics has shown that asking these two questions allows respondents to express a fuller picture of their cultural identity. As ethnicity is multifaceted and can include elements of religion and national identity by providing the opportunity to express these elsewhere, it means respondents are better able to align themselves to the harmonised categories in the ethnicity question.