The Hardest Part of Pay Transparency Reporting Is Already Solved.

Executive summary

Member states must bring the EU Pay Transparency Directive (2023/970) into national law by June 2026. Employers with 150 or more workers must file first pay gap reports by 7 June 2027, covering 2026 pay, and employers with 100 to 149 workers must file first reports by 7 June 2031. Each member state’s national law sets the final rules. Most multinationals will hire advisory firms such as Mercer, WTW and Korn Ferry to help with pay equity analysis, reporting frameworks and remediation.

Every advisory engagement starts with the same request: “Give us your data.”

The data request is where the problem starts. Aon’s 2025 Global Pay Transparency Study surveyed more than 1,400 organizations across more than 40 countries and found that only 19% of organizations globally consider themselves ready for pay transparency. In EMEA, 26% of organizations report that they are “not ready.” Only 26% of organizations have conducted a pay equity analysis in the past 12 to 18 months. The unadjusted gender pay gap in the EU is 12% (Eurostat, 2023) and has narrowed by only 4 percentage points over the past decade, from 16% in 2013.

Advisory partners can run the analysis, interpret the regulations and build reporting frameworks. Advisory partners cannot consolidate your data for you. Employee demographics are held in one HRIS, sometimes two, plus local systems of record in different countries. Payroll data is managed by one provider in one region, a different provider in another, a local solution in Germany and niche providers everywhere else. Each system exports data differently and defines compensation components differently, and none of the systems exchange data with each other.

The main point

Consultants provide the analysis, and the Directive requires auditability. An employer that cannot show how the employer reached its pay figures can face a shift in the burden of proof in court. Employers need a defensible, continuous data foundation as well as a report, and datascalehr was built for that purpose.

Sources: Aon 2025 Global Pay Transparency Study (1,400+ organizations, 40+ countries, July 2025); European Commission / Eurostat gender pay gap data, 2023.

Why the Directive creates a data trap

The EU Pay Transparency Directive sets continuous operational obligations in addition to a reporting exercise. Five requirements create a trap for companies that rely on manual processes or static consultant reports:

1.
The 5% trigger and mandatory joint pay assessments

If pay reporting shows a gap of 5% or more in any category of workers, the gap cannot be justified by objective, gender-neutral criteria and the employer has not remedied the gap within six months, the employer must carry out a joint pay assessment with worker representatives. The joint pay assessment is mandatory.

How datascalehr helps: Most companies discover a 5% gap only after the consultant finishes the report, when the chance to fix the gap quietly has passed. datascalehr provides continuous monitoring, so HR teams can find and close gaps before the reporting deadline triggers a mandatory joint pay assessment.

2.
The single source principle (Article 19)

The Directive contains a requirement that companies often overlook. If a central body such as a global headquarters sets pay conditions for several subsidiaries, employees of those subsidiaries can be compared across legal entities for equal pay claims.

How datascalehr helps: Traditional systems treat each legal entity as a silo. datascalehr consolidates the legal entities into one view, so global headquarters can see cross-entity risks that would otherwise stay hidden until a legal discovery process reveals the risks.

3.
Individual right to request information (two-month deadline)

Any employee can request information on the employee’s own pay level and on average pay levels, broken down by sex, for workers doing the same work or work of equal value. Employers must respond within two months.

How datascalehr helps: Answering a request for “average pay for similar roles in Germany” means pulling data from local payroll, matching the payroll data with HRIS demographics and calculating the average. Doing the work by hand for every request is an administrative burden that grows with headcount. datascalehr makes the data for each request available on demand.

4.
Burden of proof shift (Article 18)

If an employer has not complied with the Directive’s transparency obligations, the burden of proof shifts to the employer to show there was no discrimination.

How datascalehr helps: Compliance depends on proving that the process was objective as well as on the final number. datascalehr keeps a permanent, auditable trail of how pay data was reconciled and normalized, which protects the company against procedural non-compliance that could otherwise remove the company’s legal defense.

5.
Granular variable pay reporting

The Directive requires a breakdown of “complementary or variable components,” such as bonuses, allowances and benefits in kind.

How datascalehr helps: Base salary is relatively easy to track. Variable pay is where the data breaks: overtime in France, car allowances in Belgium, 13th-month pay in Italy and mandatory profit sharing (participation and intéressement) in France. A typical German SAP payroll runs hundreds of distinct wage types across earnings, deductions and statutory contributions. KMod™ was designed to handle the fragmented pay components that consultants typically spend weeks cleaning by hand in spreadsheets.

Audit-ready vs. analysis-only

Any consultant can help produce a pay equity report. The harder test is whether the company’s data foundation supports continuous monitoring, on-demand employee requests, cross-entity visibility, auditable process documentation and variable pay normalization without manual work each cycle. Consultants provide analysis, and the Directive requires auditability.

What options do you have?

Organizations facing the data challenge typically consider three approaches:

Option 1: Manual data collection and spreadsheets

Benefits:

• No new software required.
• Uses existing HR team resources.
• Familiar process for one-off exercises.

Downsides:

• Takes 6 to 12 months per reporting cycle.
• Error-prone across 20 or more countries.
• Not repeatable: each period restarts from scratch.
• Does not scale as reporting frequency increases.
• No audit trail to defend against a burden of proof shift.

Option 2: Buy dedicated pay transparency software

Benefits:

• Purpose-built analytics and reporting.
• Regulatory templates for EU jurisdictions.
• Audit trails and compliance workflows.

Downsides:

• Still requires consolidating and cleaning data first, which is the same underlying problem.
• Single-purpose tool that does not help with payroll operations, GL or reconciliation.
• Another vendor and system to manage.

Option 3: Solve the data problem first with datascalehr

Benefits:

• Connects all HR and payroll systems of record.
• Setup in under an hour per country, followed by AI-driven normalization and first-pass analysis.
• Gives consultants clean data on day one instead of month three.
• If you license dedicated pay equity software, your data is ready to use on day one.
• Turns a one-off project into permanent, audit-ready infrastructure.
• The same platform also runs payroll control, GL and reconciliation.

Downsides:

• Requires investing in software licenses instead of ongoing consulting and operational costs.

Option 3 solves the data consolidation problem, lets consultants focus on the analytical work and creates a permanent context layer: data infrastructure that serves pay transparency today and AI-driven HCM uses later.

The solution

Build the last mile, then let your experts do the rest

datascalehr works alongside your compensation consultants. datascalehr gives consultants clean, consolidated, normalized compensation data from every country where you operate, with setup in under an hour per country and a complete audit trail of how every data point was reconciled.

Step 1: Connect your systems of record

datascalehr connects to all your HR and payroll systems, including Workday (with native GPC and DCOD connectivity), SAP SuccessFactors and any combination of global and local payroll providers, including CSV and PDF exports. datascalehr reads each file by meaning as the file arrives, whatever the file’s format, structure or language, instead of forcing the file into a fixed format. The setup needs no custom development, and you keep your existing systems.

Step 2: Normalize and prepare your data

KMod™, datascalehr’s AI-powered knowledge model, learns what payroll data means from the decisions of payroll experts who confirm or reject KMod™ suggestions. KMod™ normalizes compensation components across countries, mapping base salary, bonuses, allowances, statutory contributions and variable pay into consistent, comparable categories. KMod™ keeps country-specific detail intact (lossless translation) while creating the unified view needed for cross-border analysis.

Step 3: AI-assisted first-pass analysis

Once the data is consolidated and normalized, widely available AI tools can run a first-pass analysis on the clean dataset. The first pass identifies potential pay gaps by worker category, flags anomalies and points to areas that may exceed the 5% threshold. Consultants can then spend their time on judgment calls instead of data assembly.

Step 4: Expert last mile

Your compensation consultants, whether Mercer, WTW, Aon, Syndio or your internal team, receive clean, categorized data ready for statistical analysis, job evaluation and remediation planning. Consultants can focus on analysis instead of spending months on data requests and reconciliation.

Step 5: Permanent, audit-ready capability

Instead of a one-off project repeated from scratch every reporting period, the company has a permanent data pipeline. The pipeline supports month-by-month monitoring, early gap detection, employee information requests answered in minutes and a complete audit trail that shows the process was objective if the burden of proof shifts. The Directive requires ongoing compliance, so the data infrastructure needs to run continuously too.

As AI agents become standard tools in HR and finance, the same normalized, jurisdiction-aware data layer gives AI agents the data AI agents need to work reliably, without a rebuild.

Under an hour per country

You keep your existing systems and your advisory relationships, and you add the connectivity layer that makes both work.

Beyond compliance: the data dividend

Pay transparency compliance is the immediate requirement. The underlying asset is a normalized context layer for global HCM data, a permanent data layer that any application, analytics tool or AI agent can build on. Once HR and payroll systems of record are connected and normalized, the same data supports a payroll control dashboard, payroll reconciliation, workforce analytics, GL integration, payroll leakage detection and M&A readiness.

Payroll control dashboard

Real-time visibility into global payroll spend, variances and anomalies.

GL integration

Automated general ledger postings from payroll data, which removes manual journal entries.

Payroll reconciliation

Automated cross-system reconciliation that catches discrepancies before the discrepancies become costly.

Payroll leakage detection

AI-driven identification of overpayments, duplicate entries and compliance gaps.

Workforce analytics

Headcount, labor cost, attrition and planning from one consistent dataset, so reports do not conflict.

M&A readiness

Connect an acquired company’s payroll data in under an hour per country, and run payroll for new entities from day one.

The compliance work becomes the starting point for broader change. Instead of paying for data consolidation once for pay transparency and again for each later use case, the company invests once in the context layer and uses the context layer across HR and finance.

Results with datascalehr’s platform

Next steps

Your advisory partners are ready, and your systems hold the data. The remaining piece is the last mile that connects your systems to your advisory partners.

For CHROs and heads of total rewards: Get compensation data consolidated and ready for analysis before consultants engage, and cut months off the compliance timeline.

For CFOs: Invest once in data infrastructure that serves pay transparency, payroll control, GL integration and workforce analytics, instead of funding four separate projects.

For CIOs and IT: Remove manual data extracts and reconciliation spreadsheets. datascalehr connects your systems and delivers clean data automatically.

For general counsel: Month-by-month monitoring with a complete audit trail means no surprises when reporting deadlines arrive, and a defensible process if the burden of proof shifts.