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  • Writer's pictureChelsea Penaloza

The Art & Science of Analyzing Pay Data

When it comes to achieving pay transparency, it’s all about execution and, as the age-old saying goes, “what gets measured gets managed”

If you are going to be transparent with job levels and pay ranges and educate employees about their placement within those ranges, it is important to establish a level of analytical rigor and build the right capability in your team to regularly review and monitor pay across your organization. This will help to ensure consistent and defensible application of your philosophy, policies, and guidelines.

So, why is this hard? 

First, many companies struggle with collecting and aggregating their data in accurate and consumable ways. Second, they don’t know how best to interpret the data to provide actionable insights. Analyzing pay data is both an art and a science that requires you to have the right strategy, structure, process, and capabilities to ensure you draw the right conclusions and don’t miss any potential issues.

While many companies hire external firms to conduct a pay equity analysis in a statistically robust way (which is a good thing to do occasionally), it is important to build the internal analytical muscle to understand your current state, identify where to take a deeper dive and build in a cadence of regularly collecting, synthesizing and analyzing your pay data. 

As a start, here are three components of pay analytics to consider:

1. Averages, trends, patterns, and outliers

This is where having the right compensation infrastructure, such as job levels and functions can be critical. Assessing pay levels, promotion rates, merit increases, etc. can highlight patterns across jobs, families, levels, business units, geographies, etc to determine if a deeper understanding of your company practices is warranted. 

For example, you might notice less merit budget being funneled to underrepresented groups than you may have been aware of, or that those same groups take longer to get promoted to the next level than others.

2. Multi-lever views/perspectives

Creating views custom to your unique organizational environment and challenges enables you to better diagnose and determine solutions

For example, don’t just look at employees within the same grade and job, but make sure you look at average pay across groups (i.e., are less represented groups in your lower level jobs and most of your leadership roles are not).

3. Intersectionality

It is important to analyze how factors like gender and race may intersect to create unique challenges. 

This begins to get into the more complex analytics that may require external help, but an example would be looking at BIPOC (Black, Indigenous, and People of Color) Women as a combination instead of just race or just gender independently to identify additional areas of concern.


Pursuing pay transparency requires a sustainable and scalable level of rigor around reviewing and analyzing your pay data. 

Make sure you have the right analytics strategy, structure, process, and capabilities in place to regularly review your data and proactively identify areas where you need to make improvements.

Want to learn more about how Nua can help you build your analytical muscle? Drop us an email at

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