Introduction
This article walks through the seven steps that actually determine whether a pay equity analysis tool gives you something you can trust, or just a number that falls apart the moment someone asks how it was calculated. A lot of HR teams treat this like uploading a spreadsheet and reading whatever pops out. That’s usually how you end up with results nobody can defend.
The teams that get this right follow a specific sequence: protect the process legally, build honest comparison groups, control for the factors that legitimately affect pay, then run the analysis and act on what it shows. Skip a step, or do them out of order, and even the best pay equity analysis tool on the market will hand you numbers that look clean but won’t hold up under real scrutiny.
Table of Contents
Step 1 : Protect the Process With Legal Privilege
Before you open any pay equity analysis tool, loop in legal counsel and set up the analysis under attorney-client privilege. This single step is easy to skip and expensive to regret, since an unprotected analysis can become discoverable evidence in a lawsuit even if the findings were preliminary or later corrected, as this guide to conducting a pay equity analysis explains in its first step.
This isn’t about hiding anything. It’s about giving your team room to investigate honestly, flag uncertain results, and revise conclusions without every draft becoming a permanent, subpoena-ready document.
Step 2 : Define Scope and Pull a Clean Employee Census
Decide upfront which employees, locations, and compensation elements the analysis will cover, base salary at minimum, plus bonuses and other cash compensation if you’re including them. Then pull a full employee census from your HRIS or payroll system: job title, department, hire date, tenure, performance ratings, education, location, and demographic data like gender, race, and age.
Getting this data genuinely clean matters more than people expect. Outliers, inconsistent pay periods, or mismatched currencies will quietly distort every result that follows, and a pay equity analysis tool can only work with what you feed it. If your workforce data still lives across disconnected spreadsheets at this stage, pairing this process with a proper HRIS and HRMS platform makes pulling a clean, complete employee census far easier from the start.
Step 3 : Build Comparator Groups That Actually Make Sense
Group employees doing substantially similar work, or work of equal value, based on job family, level, and location, not just job title, since two people with the same title can do meaningfully different work. Most methodologies recommend keeping groups reasonably sized, generally at least five people per group, so the statistical comparison holds up rather than reflecting one or two individual salary decisions.
Getting this step wrong is one of the most common reasons a pay equity analysis tool produces misleading results: comparing genuinely different roles as if they were interchangeable skews everything downstream.
Step 4 : Identify the Legitimate Factors That Explain Pay
For each comparator group, list the factors that legitimately justify pay differences: tenure, years of relevant experience, specialized skills or certifications, documented performance, and any formal market or retention adjustments. This is also the moment to watch for what’s sometimes called a tainted variable, a factor that sounds neutral but actually correlates with a protected characteristic in a way that quietly launders bias into the results.
Document the rationale for including each factor before you run anything. If a factor can’t be justified on paper, it probably shouldn’t be in the model.
Step 5 : Run the Regression and Read Both Models
This is where the actual math happens inside your pay equity analysis tool. Run an unadjusted model first, comparing raw pay by demographic group alone, then run an adjusted model that controls for the legitimate factors identified in Step 4. Comparing the two tells you something important: whether a gap that exists in the raw numbers shrinks, disappears, or persists once legitimate factors are accounted for.
A persistent, statistically significant gap in the adjusted model is the signal worth investigating further. A gap that fully explains itself through tenure or performance differences is a different conversation entirely, though still one worth documenting.
Step 6 : Investigate Outliers Before You Act
Don’t treat every flagged gap as an automatic pay adjustment. Review individual cases: hiring records, promotion history, retention offers, and any documented reason for a specific salary decision, and categorize each flagged gap as explained, partially explained, or unexplained.
This step protects you from two opposite mistakes: adjusting pay for someone whose gap is fully justified by a documented factor you simply hadn’t coded into the model, or dismissing a real disparity because it superficially resembles an explained case.
Step 7 : Build and Document the Remediation Plan
For gaps that remain unexplained after investigation, calculate specific adjustment amounts, prioritize by gap size and urgency, and set a realistic timeline and budget with finance and leadership sign-off. Then go a step further: update hiring, promotion, and raise practices so the same disparities don’t quietly reopen next review cycle.
Finally, communicate a clear compensation strategy to employees and commit to reviewing pay equity on a recurring schedule, not just whenever the topic resurfaces. A pay equity analysis tool that only gets used once a year tends to miss the gaps that open up in the months between audits.
Quick Reference: The Seven-Step Sequence
| Step | Core action | Who’s involved |
|---|---|---|
| 1. Protect the process | Establish attorney-client privilege | Legal counsel, HR leadership |
| 2. Define scope | Pull a clean employee census | HR, HRIS administrator |
| 3. Build comparator groups | Group by job family, level, location | Compensation team |
| 4. Identify legitimate factors | Document tenure, skills, performance | Compensation team, legal |
| 5. Run the regression | Compare unadjusted vs adjusted models | Analyst or pay equity analysis tool |
| 6. Investigate outliers | Categorize gaps as explained or not | HR, managers, legal |
| 7. Remediate and document | Adjust pay, update policy, communicate | Leadership, finance, HR |
If building this entire seven-step process internally feels like more than your team can take on alone, our strategic HR consulting team can help you run it correctly the first time.
Frequently Asked Questions about running a Pay Equity Analysis Tool
What data do I need before running a pay equity analysis tool?
At minimum, you need an employee identifier, current base salary, job title and family, job level, department, hire date, tenure, performance rating, education level, work location, and demographic data such as gender, race, and age.
How is an adjusted pay gap different from a raw pay gap in a pay equity analysis tool?
A raw pay gap compares average pay across groups without controlling for anything else, while an adjusted pay gap, calculated through regression inside a pay equity analysis tool, controls for legitimate factors like tenure and performance first, isolating what’s left as the potentially unexplained disparity.
Why should legal counsel be involved before running a pay equity analysis tool?
Involving legal counsel early allows the analysis to be conducted under attorney-client privilege, which can protect preliminary findings and internal discussions from becoming discoverable if the results are ever challenged in litigation.
How often should a company re-run its pay equity analysis tool?
Most guidance recommends running a full analysis at least annually, with ongoing monitoring throughout the year, since new hires, promotions, and raises can reopen gaps that were closed in the previous review cycle.
Conclusion
Running a pay equity analysis tool the right way isn’t about the software alone, it’s about the sequence you follow before, during, and after you press run. Protect the process legally, build honest comparator groups, control for real factors, read both the raw and adjusted numbers, investigate before you act, and document everything that follows. Get these seven steps right, and the resulting analysis is something you can actually stand behind if anyone ever asks how you got there.
Turn Clean Data Into Defensible Pay Equity
The PowerHR helps organizations centralize workforce and compensation data, so every pay equity analysis tool you run starts from accurate, audit-ready information. Request a free, personalized demo today and see how the right platform supports every step of a fair pay strategy.
