This interview explores how a multinational people analytics function is operationalising CSRD and the EU Pay Transparency Directive – not as a tick-box exercise, but as a durable pay-equity capability. It covers defining pay beyond base, bonus and LTI by including taxable and in-kind benefits; why deep partnership with Payroll and Comp & Ben matters; and how a robust global job architecture enables apples-to-apples comparisons across countries. We also address transparency and privacy, methods for measuring and remediating gaps, and where AI can accelerate work with the right guardrails. Take away a practical blueprint – data, processes and governance – to meet mandates and strengthen trust.
Topics covered:
- CSRD versus EU Pay Transparency – complementary purpose, scope and depth; organisation lens versus associate lens; a layered analytics approach that serves both.
- Defining the pay universe – cash, bonus, equity and in-kind or taxable benefits; methods to monetise benefits for analysis and reporting.
- Data sources and quality – joining HRIS with payroll; field-level definitions, taxability codes, controls and stewardship to create a complete pay dataset.
- Global comparability – using job architecture as the backbone; normalising for local market factors; handling small populations and exceptions.
- Transparency, privacy and process – pay ranges, candidate and employee communications; PII safeguards; case management, escalation and audit readiness.
- Analytical workflow – segmentation, matched comparisons and control variables; identifying gaps, setting remediation logic and reporting to leaders.
- AI enablement – human-in-the-loop guardrails, assessing bias versus accuracy, evaluation metrics (e.g. confusion matrices), documentation and monitoring.
Outcomes:
- Separate and align CSRD and EU Pay Transparency requirements while designing one integrated fair-pay capability.
- Identify the critical fields, sources and quality checks to build a complete pay dataset, including benefits and taxability, across countries.
- Apply a robust job architecture to ensure apples-to-apples comparisons and fair remediation globally.
- Design transparency and privacy controls that build trust without exposing personally identifiable information.
- Run a defensible pay-gap analysis and convert findings into remediation plans and board-ready reporting.
- Use AI safely as decision support – establish human oversight, bias checks and clear model documentation.
- Set a 2028 roadmap that turns compliance momentum into a lasting fair-pay programme with cross-functional ownership.
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