ESRS S1 Data Collection

The ESRS S1 data source map

ESRS S1 draws from five primary data systems — understanding which system provides which data is the starting point for infrastructure planning.

HRIS (Human Resources Information System): Primary source for — headcount by employment type, contract, gender, country (S1-6); age group distribution (S1-6); employee category distribution (S1-6); parental leave take-up and return rates (S1-15); discrimination and grievance case count (S1-17); new hires and leavers for turnover calculation (S1-1 context).

Payroll system: Primary source for — total remuneration by employee for CEO pay ratio calculation (S1-16); gender pay gap calculation using payroll data (S1-12); working time and overtime hours (S1-9); part-time vs full-time classification verification; total wages and benefits for social protection coverage assessment (S1-11).

H&S management system: Primary source for — injury and illness incident counts and severity (S1-14); lost days due to work-related injury (S1-14); hours worked for rate calculation (S1-14); H&S management system coverage (S1-14); near-miss reports (context).

LMS (Learning Management System): Primary source for — training hours by employee (S1-13); training completion by employee category and gender (S1-13); compliance training (anti-corruption, H&S) completion rates; performance review completion rates (S1-13).

Legal and compliance system: Primary source for — confirmed discrimination incidents and status (S1-17); whistleblower and grievance case counts (S1-4); collective bargaining agreement documentation (S1-5); regulatory fines and sanctions (context).

Closing the most common data gaps

Six data gaps are almost universally identified in Wave 1 CSRD S1 data collection — address these proactively before your first reporting year begins.

Gap 1 — Non-employee worker count (S1-8): Most HRIS systems do not track contractors, agency workers, and supervised workers. Solution: build a contractor registry as a joint project between HR and procurement. Capture: worker name or ID; contracting organisation; work location; work type; start/end dates; and supervised vs independent classification.

Gap 2 — Hours worked for supervised workers (S1-14): Agency and contractor hours worked are not typically available in internal systems. Solution: include hours worked reporting in contractor and agency supplier contracts. Require monthly or quarterly hours confirmation from all agencies and contractors providing supervised workers.

Gap 3 — Non-employee training data (S1-13): LMS systems typically only capture employee training. For supervised workers who participate in mandatory H&S or compliance training, capture completion in the LMS or a separate training tracker.

Gap 4 — Payroll data quality for pay gap calculation (S1-12): Total annual compensation including bonuses and LTI vesting in the same year is often not available in a single payroll report. Solution: build a year-end compensation extraction process involving both payroll (base, bonus, allowances) and equity compensation management (LTI values). Finance and HR must collaborate on this extraction.

Gap 5 — Parental leave entitlement denominator (S1-15): Identifying employees who became parents during the year (the denominator for take-up rate) requires a process beyond just tracking leave requests. Solution: implement an HR process for employees to notify parental status change — whether or not they intend to take leave.

Gap 6 — Geographic segmentation for country-level disclosures: Where S1 disclosures require country-level breakdowns (wages vs minimum wage by country, CBA coverage by country), HRIS data must be segmented by country of employment — not country of payroll entity, which may differ for expatriates.

Building the audit trail for assurance

Every ESRS S1 reported figure must have a documented audit trail from source data to final disclosure — this is the primary requirement for assurance readiness.

Source data retention: Retain the underlying data extracts used for each S1 metric for at least 5 years. This includes: HRIS extracts for headcount metrics; payroll reports for remuneration data; H&S system incident registers; LMS training completion reports; and contractor hours confirmation documents from agencies.

Calculation documentation: For metrics that require calculation rather than direct extraction (TRIR, gender pay gap ratio, CEO pay ratio, average training hours), document the calculation methodology and the inputs. A calculation workbook showing: source data extract → formula applied → reported figure is the standard assurance evidence format.

Review and sign-off: Each S1 metric should be reviewed and signed off by the system owner before being provided to the sustainability reporting team. HRIS data by CHRO or HR Director; payroll data by Finance Director; H&S data by H&S Director; training data by L&D Director. This governance creates accountability and reduces data quality errors.

Variance analysis: Compare year-on-year figures for all S1 metrics and investigate movements above 10%. Unexpected changes require explanation — either a genuine underlying change (workforce restructuring, M&A, policy change) or a data quality issue. Document the variance analysis and explanations before assurance begins — assurers will ask the same questions.

For ESG platform users: platforms like ESGMaster maintain the source data links, calculation documentation, and audit trail automatically — each data point is traceable to its source without manual documentation effort. This is the primary operational efficiency of using a dedicated ESG platform for S1 data management.

Frequently asked questions