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The role of analytics in reducing the gender pay gap

Mind the gap.

Published by Orgvue 

The Prime Minister has announced for the first time that companies with over 250 employees will be forced to publish details of salary differences, in a move to reduce gender inequality. The gender pay gap currently stands at 19.1% for full- and part-time workers in the UK, meaning a woman on average earns around 80p for every £1 earned by a man, as reported in The Guardian.

It is clear that a man and a woman doing the same job in the same location, with the same experience, skills and performance ranking, should be paid the same amount. On the journey to achieving fair and equal pay, businesses will need to get more from organizational data analytics and ensure the right conclusions are drawn and appropriate action is taken. Herein lies the problem: businesses are currently struggling to bring together and comprehensively analyze and report on the gender pay gap across factors including experience, skills and performance ranking.

The first issue that companies need to address is data, writes Ann Francke, CEO of CMI, in The Telegraph. “Businesses need to develop consistent metrics and have the right tools to collect the data – no small task.”

“Large businesses can be complex,” she adds. “The essential reporting tool is pay audits; without this, there is no meaningful way to understand if a business is closing its gender pay gap. There will be a real benefit to employers in cleaning up and simplifying how they pay everyone, not just women.”

Whilst companies will have access to the relevant data, the challenge lies in bringing the data together, analysing it and providing HR analytics reports to show pay gaps and explain their occurrence. organizational data is often messy, outdated and lying in multiple systems and Excel islands. The first task is for businesses is to compare, merge and clean data from across the organization. For example, by matching finance data, which tends to sit in ERP systems, with HR data in systems such as HRIS and payroll.

Even when good data is available, companies will then have to analyze and interpret them in a meaningful way, so that action is taken in the workplace.

With David Cameron’s measures for equal pay to be introduced in the next 12 months, businesses will need to start investigating how pay by gender is split across the organization in greater detail, and fast. This is not simply an average; it requires analyzes across numerous metrics, including department, grade, performance, depth, tenure and role.

At Orgvue, we’ve been helping organizations apply analytics to help address the gender gap using Orgvue, our HR analytics tool. Orgvue provides a rapid way to perform analytics to visualise imbalances in companies across organizational metrics including gender, ethnicity, age and disability. An illustration of how a company might do this is below, analysing a 1500 employee organization. The overview in Fig 1 shows a much higher proportion of men to women in the company whilst the chart in Fig 2 shows the split of average salaries by department.

Figure 1

Figure 1


Figure 2

While there may be acceptable reasons for imbalances in gender distributions or average salaries in particular industries, organizations now need to start investigating and demonstrating why this is. Fig 3 and 4 explore the company’s gender salary distribution and show cause for concern in some grades and locations:


Figure 3


Figure 4 

Digging down further using an analysis chart of split by gender and depth Fig 5 shows that more of the company’s most senior roles, which are awarded the highest salaries, are filled by men. This helps explain some of the inequalities and indicate where the organization may need to address differences in overall average pay.


Figure 5

Analysing salary by grade or tenure shows there seems to be an issue for long serving females. This may need further investigation and helps explain the gender gap, in Fig 6 and 7.


Figure 6


Figure 7

To comprehensively understand where the differences are and if there are unfair imbalances in pay, organizations will need to report at the micro as well as the macro level. For example, organizations will need to look across specific role types against a range of metrics as in Fig 8 and 9.


Figure 8


Figure 9

organizations need to investigate differences based on experience, tenure, working hours or performance of men and women in the company before jumping to conclusions. Detailed analysis using org charts and organizational analytics (as in the gender metrics dashboard in OrgVue above) are essential when it comes to illustrating, understanding, explaining and tackling gender pay inequality. It is an area organizations are going to have to upskill fast if they want to keep up with government’s latest requirements.

On the one hand, these types of data visualisations and analytics demonstrate the types of reports organizations need to be producing, but more importantly the basis for action. The government’s policy should not be regarded as a reporting exercise but an initiative for change in the workplace.