Applying intersectionality to workplace gender auditing and analysis


Taking an intersectional approach is to workplace gender auditing is necessary to understand whether your organisation’s systems, structures, policies, and practices are working to promote workplace gender equality for all.

"Intersectional data" (in this guidance) is data that is separated by gender as well as attributes other than gender, such as Aboriginality; age; disability; ethnicity; gender identity; race; religion; or sexual orientation.

Breaking down or “disaggregating” the data in an intersectional way is important. It helps us understand how someone’s experience of gender inequality might be compounded by the discrimination or disadvantage they may experience based on other attributes.

If available, intersectional data must be used for your workplace gender audit (s11(3)(b)). If this data is not yet available, you should consider how you can collect this in the future and include this as an action in your Gender Equality Action Plan (GEAP).

In collecting, analysing and reporting this data, organisations need to be sensitive to employee safety and privacy considerations and allow employees the discretion to self-identify.

Considerations for future data collection

If intersectional data is not yet available, you should consider how you can collect this in the future and include this as a planned action in your Gender Equality Action Plan (GEAP).

You might find that your entity does not yet have the systems capabilities to collect and store some types of data. You might not yet have the culture or level of trust required to support the collection of data deemed sensitive and personal.

The best way to deal with data gaps in the immediate term is to document them in a deliberate and systematic way. Over the next reporting period, you should think about how you can improve your data collection and consultation processes to better understand intersectional gender inequality in the workplace.


The collection, analysis and reporting of data throughout the audit must be kept confidential to protect the privacy of employees.

This is particularly important when taking an intersectional approach, as the more intersectional attributes per employee are collected, the easier it becomes to identify individuals and risk their exposure.

When collecting personal information at the onboarding stage or as part of HR records, we recommend ensuring that the intention behind the collection of data is clearly articulated through a collection statement, and that questions are non-mandatory. Staff must always have the option not to disclose.

Data analysis

Analysing the data, disaggregated by intersectional attributes, will identify which groups of people should be the focus of your strategies. Furthermore, the analysis will provide insight into the systemic barriers to access, inclusion and equity for people who experience intersectional gender inequality. The results will also indicate what past actions have had the most positive or negative impact on groups with similar intersectional attributes.

Aggregated quantitative data often misses the nuances and specific concerns of people with intersectional attributes, who may become “lost” in the overall gender data. However, small sample sizes or false assumptions can easily lead to flawed data analysis, misinterpretation, and incorrect conclusions. Therefore, intersectional data should be supplemented by qualitative data analysis, to validate conclusions and ensure that less representative minority experiences are not overlooked.

As best practice, it is worth identifying any gaps in data for groups of people with intersectional attributes and if so, determine if these gaps need to be addressed immediately or in a future data collection exercise. Also consider what other metrics or measures can be substituted for the gaps in data and what improvements can be made to future data collection activities.

Quantitative analysis

Gender disaggregated data provides one option for analysing data, but without breaking this down to other demographic attributes such as age or cultural background, the specific issues and concerns of people with intersectional attributes may not be visible.

To assist with intersectional quantitative data analysis, organisations should:

  • Conduct data comparisons – comparing the data of people with intersectional attributes against the data of men and women who represent the majority. Comparisons will help you identify unusual patterns and trends in the data.
  • Consider the sample size – when data is disaggregated by intersectional attributes, the sample size will decrease. Calculating the “margin of error” of the sample size, for each group being reviewed, will help avoid drawing incorrect conclusions and exaggerating effects based on a small (statistically insignificant) sample size. For example, if 1 of 2 Indian women with a disability in a department of 100 people have experienced discrimination. To present the findings as: “50% of Indian women with a disability have experienced discrimination”, would misrepresent the actual situation.
  • Conversely, do not assume that there are no underlying issues if the results appear to be insignificant or within the margin of error, as the sample size may be too small.

Qualitative analysis

As far as possible, use diverse sample groups that represent the many people with intersectional attributes, to ensure that different perspectives are considered. As required for the GEAP, engage in meaningful consultation with communities, peak bodies, and other external stakeholders of representative groups to gain their unique perspective and represent the voices of those with lived experiences.

Always focus on “what was said”, not “who said what” when analysing commentary to determine major themes and correlations.

Ensure psychological support is available

It is important that individuals feel psychologically and culturally safe in sharing data that reveals their intersectional attributes. (Psychologically and culturally safe means being able to represent your true self without fear of negative consequences for your mental and emotional health or career.)

In some cases, your data analysis will identify ‘good news stories’. In other cases, your analysis will identify current and/or longstanding gender and intersectional inequalities. Regardless of your findings, it is important to remember that work on gender equality can raise issues for anyone in your organisation, at any time.

Whenever you discuss issues of gender in the workplace, remind employees of your Employee Assistance Program and other local support services available to them. Having appropriately skilled staff who can respond to disclosures and refer people to services as part of your consultation process is also important. Access to psychological support for those who engage in the analysis process is also an important consideration.



Amanda, an Aboriginal queer woman, discusses the challenges of asking Aboriginal and Torres Strait Islander people to identify when collecting data.

Case study

Read the case study to understand how an organisation took steps to improve their intersectional data collection and mobilised internal communications to communicate with staff about data.