Pay Transparency Is A Data Problem

I can’t believe I haven’t written about this before, but I am receiving more questions about data collection and pay transparency. So it’s time to share a proper overview so you have an idea of what you’re looking at.

What’s the real challenge with pay transparency reporting?

It’s not the total compensation calculation. It’s not the regulation itself, even though the final local details are still unknown. The real problem? Your compensation data is scattered across half a dozen systems that were never designed to talk to each other. And now you need to pull it all together to report on pay equity.

Here’s where this gets complicated: this data isn’t just stored in different places, these systems also define things differently. What your HRIS calls “base salary” might not match what payroll calls “base salary” because one includes certain components and the other doesn’t. Your sales compensation tool might track “bonus” as commissions, while your finance spreadsheet tracks “bonus” as discretionary payments, and your HRIS tracks “bonus target” as a percentage. Are those the same thing? How do you even know what each field actually represents when the person who set up the system left years ago?

Even something as basic as “full-time” can mean different things across systems. Is it based on hours worked, contract type, or benefits eligibility? You need all these definitions to align perfectly, or your pay equity analysis will be comparing apples to oranges. And you might not even realize it until someone questions your numbers. This topic is especially important when you manage pay transparency for a number of countries: are you following country definitions or corporate ones?

Where is this data hiding exactly?

What I typically see when I work with organizations:

The HRIS has base salary, job titles and levels, employment dates, and sometimes annual bonus targets.

The payroll solution tracks actual payments including overtime, shift differentials, allowances that flow through payroll, and sometimes bonuses.

Sales compensation tools handle commission calculations and sales incentive payments. Sometimes these flow through payroll, sometimes they’re separate.

The expenses system has travel allowances, meal allowances, and sometimes car allowances.

Stock administration platforms manage equity grants, vesting schedules, and exercise data.

Benefits administration tracks pension contributions, health insurance values, and other benefits with monetary value.

And don’t forget the HR and finance spreadsheets: discretionary bonuses, one-off payments, retention bonuses, sign-on bonuses. You know, the stuff that lives in someone’s Excel file because there is no easy place to store it.

What happens when you try to combine all this?

You need to pull data from all of these sources, remove all duplications, validate it, and aggregate it by employee. Pay careful attention: these systems often don’t share common employee identifiers. One system uses employee ID, another uses email address, another uses some legacy code from a system you no longer have access to.

And then comes the hard question: for every field that exists in multiple systems, which one is your single source of truth? Does ‘base salary’ come from the HRIS or the payroll system? When they don’t match (it happens more often than you think) which one do you trust? Your HRIS might have the contracted salary, while payroll has what was actually paid after adjustments. Both are “correct” in their own context, but you can only use one number in your pay equity analysis. Multiply this decision across dozens of data fields and hundreds or thousands of employees, and you start to see why this isn’t something you can just figure out in Excel a few weeks before June 7.

So how do you actually get all this data in one place?

You’ve got five main options. I’m sure there are more, but I’ve seen these most often. Let me walk you through them.

Option 1: Manual aggregation

Export data from each system, use Excel or similar tools to combine it, match on employee ID or name, validate and aggregate.

What works here: You don’t need IT investment, and you maintain direct control over the data.

What doesn’t: It’s time-consuming, error-prone, doesn’t scale, and is nearly impossible to audit. If someone asks you in six months how you calculated a specific person’s total compensation, good luck explaining that.

Option 2: Data warehouse or reporting database

Build integrations that pull data from all systems into a central reporting database or data warehouse, with regular automated refreshes.

What works here: It’s scalable, repeatable, more accurate, and easier to audit. Once you’ve built it, it runs automatically.

What doesn’t: It requires significant IT investment and ongoing maintenance. You’re essentially building custom integration infrastructure.

Option 3: Compensation management platform

Implement a dedicated compensation platform that integrates with your other systems and provides pay equity analytics.

What works here: These platforms are purpose-built for this exact problem and include analytics and reporting features out of the box.

What doesn’t: You’re adding another system to maintain, they can be expensive, and implementation takes time. Plus, you still need to maintain integrations with all your source systems.

Option 4: Enhance your HRIS

If your HRIS has strong compensation management modules, enhance it to capture and aggregate variable pay. Many vendors offer pay transparency reporting on top.

What works here: You’re leveraging an existing system, creating a single source of truth without adding more platforms. Even if not all fields exist, an HRIS typically has the option of adding custom fields.

What doesn’t: It may require significant customization, and not all HRIS platforms handle complex variable pay well. Your mileage will vary significantly depending on which system you have.

Option 5: Pay Equity tech

Implement a specialized pay equity platform that is specifically designed to aggregate compensation data and analyze pay gaps.

What works here: These solutions are laser-focused on getting pay information in and building comprehensive overviews of pay gaps. They offer sophisticated multi-variate analysis that goes beyond simple comparisons and can control for legitimate factors like experience, location, and performance while identifying unexplained gaps. They’re built specifically for pay equity compliance and can generate audit-ready documentation.

What doesn’t: They’re another specialized tool to implement and maintain, though many offer pre-built integrations with common HR systems. The investment makes sense if pay equity is a significant concern or regulatory requirement, but might be overkill for smaller organizations.

Which option should you choose?

I only recommend option 1 for organizations with fewer than 30 employees. Since organizations under 150 employees are not required to report until 2031, option 4, the HRIS, might not (yet) offer the required functionality for small entities. For mid-sized organizations, I typically recommend option 2 (data warehouse), option 4 (enhanced HRIS), or option 5 (pay equity tech), depending on your specific situation.

If you’re facing regulatory requirements like the EU Pay Transparency Directive in multiple countries or have complex compensation structures, option 5 might be your best bet. If you already have a robust HRIS with good compensation modules, option 4 could save you from adding another platform. If you need flexibility and have strong IT resources, option 2 gives you the most control.

The key insight? The investment pays off because you’ll need this capability not just for annual reporting, but for ongoing pay equity management. Annual reports are public and should tell a story: consistency towards employees is key.

What’s the bottom line here?

Don’t think about pay transparency reporting as an annual compliance exercise. It’s an ongoing data management challenge that requires proper infrastructure. Yes, you can do this with manual aggregation the first time. But it will be incredibly difficult to properly align year on year reporting. If you’re serious about pay equity (and you should be!) you need a sustainable solution that gives you accurate, auditable data whenever you need it. And that ensure a consistent pay transparency approach over a longer period.