Public sector - understanding equality data collection: main report

This research describes and explores the range of equality and socio-economic disadvantage data collected by public sector organisations. Findings offer insights into what works best in terms of collecting, utilising and safeguarding robust data, highlighting major barriers to its collection or use.

6 How are equality data collected and processed?

6.1 This section briefly describes how equality data are collected and processed.

6.2 It is important to note that organisations often emphasised that data were collected and processed in line with the Data Protection Act 2018.

How are equality data collected?

6.3 The methods used to collect the equality data examined in this research included online, telephone, face-to-face, and paper-based methods - sometimes used in combination; there was also variation in whether the information was provided directly to the organisation by the customer, or gathered and recorded by a 'third party' (i.e. a partner or external organisation, or individual(s) contracted to provide services of some type).

6.4 Research participants also described the use of equality monitoring forms (EMFs) [38] in relation to data collection. This took three main forms: firstly, EMFs formed the basis of the data collection; secondly, EMFs were used separately, as an additional form to accompany the 'main' data collection (note that in some cases these were returned separately); and thirdly, the EMF was developed separately and then integrated with the 'main' data collection.

6.5 The extent to which the collection of equality data is 'required / mandatory' or 'voluntary' is complex and can depend on the rationale for the data collection. For example:

  • Where data collections seek to ascertain information in order to award grants, or benefits, or other financial or practical support, interviewees were more likely to say (at least some of) the responses to equality questions were 'required' or 'mandatory'.
  • Where equality information was collected to examine differential experiences or impacts of services for different equality groups, the interviewees were more likely to say responses to equality questions were 'voluntary'.
  • In many cases, a 'prefer not to say' response option (or similar) was offered as a response to equality questions (see also paragraph 4.17 above). This has implications for understanding what was meant when the collection of equality data was described as 'required' or 'voluntary'. Arguably, answering the question may be 'required' but providing information about one's equality characteristics is not.

6.6 These issues are returned to in Section 9.

How are equality data processed?

6.7 The processing of equality data (which covers data input, quality control, data cleaning, data revisions and storage) is highly dependent on the infrastructure and resources which are available to support the particular data collection. Compliance with the DPA 2018 is the basic legal requirement, and the organisations in the sample were very conscious of this. For example:


The organisation is 'very alive to data protection issues' and robust processes are embedded in our systems. We have a raft of data protection policies, and treat the issue very seriously. The information we hold on people is very sensitive, and we are very aware as data controllers of only collecting and holding information that is required to progress a case and carry out our work.

Making sure that data is held securely, following all the legal processes, it's of utmost importance for all our roles.

6.8 The research heard about very varied processes. On the one hand, a number of data collections were identified where the information was gathered on paper (and in most, but not all cases, subsequently input to an electronic system). In other cases, there were very detailed arrangements for quality control and feedback (involving several iterations between respondents and the bodies collecting and reviewing the information), and collections were integrated into extensive IT systems and platforms, with complex and carefully developed data linkage arrangements in place.

6.9 Specific issues referred to in relation to the processing of equality data included:

  • Ensuring that personal details (e.g. name and other identifying information) are stored separately from other information in an individual's record
  • The requirement to get consent for data holding and sharing, the different ways this was done, and the challenges of doing this in some situations
  • Recognising the importance of having a facility to update equality data on a regular / continuous basis in relation to ongoing administrative data collections
  • Restricting staff access to data on a 'need to know' basis
  • Implementing quality control procedures (e.g. the automated identification of records for deletion based on date information) to ensure that data was not retained longer than was necessary, and
  • Recognition that self-reported data cannot be verified.

6.10 Such issues are returned to in Sections 7 and 8.



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