I have been taking an intersectional approach to data diversity analysis for some time and sharing these working with those who work on diversity data.<\/p>\n\n\n\n
For people conducting gender equity audits, compliance reporting, or gender pay gap analysis, you may receive data from payroll, your data analyst, code the template and upload, and say bye bye. And then wait for the published summary report. Getting under the hood and checking raw data is worth the effort, as you understand how the data is put together for reporting. It can be complicated but worth the investment and you can quickly build you confidence.<\/p>\n\n\n\n
Organisations can get fixated with singular figures and gaps between women and men, forgetting people with intersecting identities, age, cultural diversity, LGBTIQ, TGDNB, disability, Aboriginal and Torres Strait Islanders, and the combination of these. This then results in setting crude targets. You need to take the guess work out, and it\u2019s tricky if you data literacy is not your strong suit.<\/p>\n\n\n\n
This is an example I created below (not real figures) that shows the average total remuneration pay gap and representation of employees – gender, disability and age.<\/p>\n\n\n\n
You can dive into the data in a number of ways. The aim is not to have scatter gun approach or go down rabbit holes. Once you know the organisations better, or understand your context, you can be strategic in your approach by adjusting one variable at a time.<\/p>\n\n\n\n
I am continually thinking of the human experience behind the figures, and what kind of disruption I can create in my actions. Not broad brush, but a few key targeted actions that will drive change.\u00a0Over time the data will show transition of people into leadership (eg. manager and non-manager).\u00a0The average current remuneration may give you a hint at what level employees may occupy (senior or junior position).<\/p>\n\n\n\n
Making the complex simple takes practice. I like to construct a worksheet and practice calculations. You can also connect your data with other gender equality indicators or qualitative and quantitative data sets, looking for causes of the gap. Aggregate data often masks what is happening below the surface and result in crude targets.\u00a0You can develop your own internal data sets and targets.<\/p>\n\n\n\n
These are trends, but important information (even with incomplete data sets) that can help you identify equity issues and inform actions in your Gender Equality Action Plans. You don’t need to have complete data sets, but as you build confidence in your employees sharing personal information, they see why it benefits in evening the playing field.<\/p>\n\n\n\n
If you are truly valued by your organisation, then being paid fairly is one of the ways that your value is recognized.<\/p>\n\n\n\n
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