Disclosure focus
The practical focus is on coverage and consistency across the workforce, not just a few showcase programmes. A useful report should make clear whether training is available across different sites, functions and worker groups, how participation is managed, and whether there are gaps between what is offered and what people actually receive.
This LRA educational guidance supports disclosure preparation. For the exact requirements, always refer to the official European Commission source.
Prepare
What to collect before drafting
Tick each field as the evidence lands. Owners are the teams that usually hold the source files.
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Employee review coverage
HR / People teamThe share of employees included in the review, with the numerator and denominator clearly defined and the period covered.
Evidence HR or performance review records, review completion reports, and the calculation file used to derive the percentage.
Watch for The percentage is often built from a different employee count than the review tracker, so the numerator and denominator do not reconcile to the HR system.
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Training hours total
Learning and DevelopmentThe full number of training hours delivered in the reporting period, using one consistent basis for all sessions and participants.
Evidence Learning management system exports, attendance logs, course records, and the roll-up calculation supporting the total.
Watch for Training time is often summed from mixed sources or mixed time bases, so the total does not reconcile to the learning system or attendance records.
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Average training per employee
Learning and Development / HRThe average training hours per employee, together with the employee count used in the calculation and the period covered.
Evidence The total training-hours calculation, the employee population file, and the working paper showing how the average was derived.
Watch for The average is often calculated using a different headcount than the training total, so the per-employee figure is a scope or period mismatch.
Before you start
- Have you fixed which employee groups count for this training review, so the percentage is based on the right population?
- Will you use the same reporting period and cut-off for both the headcount share and the training-hours figures?
- Can you trace the total hours back to a source record, such as a learning log, payroll file, or HR system extract?
- Have you agreed how to calculate the average hours per person, including which employees sit in the denominator?
- Are you clear whether the review covers only formal training activity, or also other learning time your organisation records in the same way?
How to prepare it
Request the data
Request the training review data from People Analytics
Translate the disclosure into an internal business question — then adapt it to your organisation's own language.
How many people were reviewed for training, and what training time was recorded for the reporting period?
Please use your organisation’s own terms first (for example, learning review, development check-in, skills review, course time, learning hours), then map them to the disclosure fields. Keep the wording in your internal language and check the official source before sign-off.
Weak request
Please provide the ESRS S1:S1-12 training metrics for the year, including the percentage of employees reviewed, total training hours, and average hours per employee.
Why it fails: This uses framework language that many operational teams will not use day to day, and it does not tell the owner what source, boundary, counting basis, or internal labels to use. It also leaves room for inconsistent interpretation of who is included and how time is measured.
Better request
Please send the learning review extract for [period] for [boundary]. We need the count of employees who had a learning or development review, the total learning time recorded, and the average time per employee, using your team’s own labels. Please include the source system, population covered, counting basis, time basis, and any exclusions or duplicates.
Formal email template
Subject: Request for training review and learning time data for [reporting period] Dear [name/team], Could you please provide the data needed for our sustainability reporting on employee learning for [reporting period]? We need, for the agreed reporting boundary ([boundary]), the following in your own internal terms and source extracts where available: - the number of employees who had a training or learning review during the period; - the total learning or training time recorded for those employees; and - the average learning or training time per employee. Please also include: - the system or file used; - the population covered; - the counting method used for people reviewed; - the basis used for time recorded; - any exclusions, duplicates, or missing records; and - the name of the person who prepared the extract. If helpful, please send this in a simple table. Adapt this to your organisation’s own language, and check the official source before sign-off. Many thanks, [Your name]
Short Teams / Slack version
Hi [name], could you send the training / learning data for [period] for [boundary]? We need: people reviewed, total learning hours/time, and average per employee, plus the source system and any notes on exclusions or duplicates. Please use your team’s own terms in the extract. Thanks.
Industry examples
Manufacturing
Context. A plant uses shift-based toolbox talks, mandatory safety refreshers, and role-specific machine training recorded in a local learning log.
Adapted request. Please share the training log for [period] for the plant and support teams. We need the number of employees who had a learning review, total recorded training time, and average time per employee. Please use your site terms, and include the log source, shift coverage, and any sessions that were cancelled or repeated.
Example response. The site learning log shows 428 employees reviewed, 1,964 total training hours, and 4.6 hours per employee. Source: site learning tracker. Coverage: plant and maintenance teams. Two cancelled sessions were excluded; duplicate entries were removed.
Professional services
Context. A firm tracks mandatory compliance modules, client-skills workshops, and coaching sessions in a central learning platform.
Adapted request. Please provide the learning platform extract for [period] across the firm. We need the number of staff who completed a learning review, total learning time, and average time per person. Please include the platform name, business units covered, your completion rules, and any manual adjustments.
Example response. The platform extract shows 1,250 staff reviewed, 7,500 total learning hours, and 6.0 hours per person. Source: central learning platform. Coverage: all UK entities. Completion counted once per person per module; one duplicate enrolment was removed.
Draft your disclosure
Notes that turn data into a disclosure
LRA training templates — adapt them to your organisation, and check the official source before sign-off.
Method note
State how you defined the employee population, how you counted those included in the review process, and how you calculated total learning time and the per-person average.
Context note
Explain what the review coverage percentage and training figures indicate about how widely employees were assessed and how much development time was recorded overall and per person.
Fluctuation statement
If the figures move materially, note whether the change reflects a wider or narrower review reach, more or fewer training hours, or a shift in the average time per employee.
Content index entry
S1-12 Training — [location / page] / [notes]Download Centre
Preparation tools & forms
Download preparation tools for S1-12 with free verified email access for 24 hours. Source-report links remain public.
Check
For each claim, check the evidence
We calculated the coverage figure from the training activity we could evidence for the period, using our own employee count as the denominator and the recorded training time as the numerator.
Risk The assurer may test whether the calculation basis is consistent, whether the employee population used is the right one for the period, and whether any training time was omitted, double-counted, or included without support.
Evidence to check Calculation workbook; source training records; employee headcount extract for the reporting period; methodology note showing the numerator and denominator used; reconciliation from raw records to the published figure.
We limited the figure to the workforce group we intended to report on and applied the same scope rule throughout the dataset, so the published percentage reflects one consistent population.
Risk The assurer may probe whether the scope was set before calculation, whether exclusions or inclusions were applied consistently, and whether the reported population matches the underlying records.
Evidence to check Scope memo; population definition used for the metric; HR extract showing included and excluded employees; audit trail of any adjustments; sign-off confirming the scope applied before publication.
We relied on source records from the reporting period and checked the underlying data for obvious gaps, duplicates, and mismatches before finalising the number.
Risk The assurer may question data completeness, accuracy, and whether the figure was built from reliable source data rather than estimates or partial records.
Evidence to check Training attendance logs; learning system export; performance review tracker; data quality checks; exception log; evidence of follow-up on missing or inconsistent entries.
We kept evidence that links the published figure back to the original records, so the calculation can be traced from the final number to the source files.
Risk The assurer may test whether the audit trail is complete enough to support the reported figure and whether the evidence retained is contemporaneous and traceable.
Evidence to check Version-controlled calculation file; source system extracts; supporting documents for any manual entries; change log; approval trail; retention of the files used in the final calculation.
Before publication, we reviewed the result for reasonableness against prior periods and internal expectations, and we resolved any unusual movements or data issues we found.
Risk The assurer may probe whether pre-publication checks were substantive, whether anomalies were investigated, and whether the final disclosure could be misleading because of unresolved issues.
Evidence to check Reasonableness review notes; prior-period comparison; variance analysis; issue resolution records; management review or approval evidence; final sign-off confirming no outstanding data concerns.
Evidence pack to prepare
Common reporting gaps
- Figures are stated without the supporting narrative, or narrative without figures.
- Scope is inconsistent between the text and the numbers.
- The reporting boundary is left undefined.
- Material changes since the previous period are not disclosed.
- Estimates and measured values are not distinguished.
- Source records for the figures are not identified.
Where drafts go wrong
Mistakes to avoid and where judgement is needed
The left column is what preparers get wrong while collecting; the right column is where you will have to make and explain a judgement.
Mistakes to avoid
- Wrong ownerChasing the learning team alone can miss the people who hold headcount, payroll, or local training records, so the figures come from the wrong source.
- Framework language, not business languageAsking for the data using disclosure labels instead of the organisation’s own terms can leave teams unsure which course lists, HR fields, or finance reports to pull.
- No clear boundaryIf you do not define which entities, countries, and worker groups are in scope, different teams may count different populations and the totals will not match.
- Wrong period basisPulling training activity from a different date range than the reporting year can mix current-year and prior-year records and distort the result.
- Mixed counting methodCombining headcount, course completions, and attendance records without one agreed basis can make the percentage reviewed and the hours total inconsistent.
- Source labels lostCopying numbers into a summary sheet without keeping the original file names, report titles, or extract dates makes it hard to trace where each figure came from.
- Populations mergedPutting mandatory and optional learning, or different employee groups, into one bucket can hide the separate patterns needed for the disclosure.
- Missing evidence trailIf the extract date, version, and reviewer sign-off are not kept with the data, no one can show how the final figures were checked before use.
Judgement needed
- Choose the employee population before calculating the review rateSet out which people sit in the training population for the year, explain any exclusions or inclusions at the margins, and keep the same basis when you compare periods unless a change is clearly described.
- Decide how to handle business bought or sold during the yearState whether people who joined or left through acquisitions, disposals or similar changes are counted only for the time they were in the group, and explain any restatement or non-restatement of earlier figures.
- Use one practical rule where local job categories do not matchIf country-level role labels or worker groupings differ, map them to a single internal approach for reporting and disclose the rule used so readers can see how the numbers were made comparable.
- Be clear on who counts when someone sits near the boundaryExplain how you treat agency workers, contractors, secondees, interns or other near-boundary groups, and disclose any judgement used to include or exclude them from the training population.
- State the timing basis for both headcount and training activityMake clear whether the figures reflect an opening point, closing point, or average over the period, and describe how you aligned the people count with the training hours recorded.
- Separate measured records from estimates and explain the methodWhere attendance or hours are not fully captured, say which figures come from system records and which are estimated, and describe the basis used to fill gaps or convert partial records into totals.
- Explain how you calculate the average per personShow whether the average is based on all people in scope, only those who received training, or another internal denominator, and keep the approach consistent or explain any change.
- Round in a way that does not distort the totalsDisclose the rounding rule used for the percentage, the total hours and the average, and make sure the rounded figures still reconcile sensibly to the underlying data.
- Aggregate enough to protect personal dataIf small groups could identify individuals, combine categories or suppress detail as needed, and explain the level of aggregation used so the training figures remain usable without exposing personal information.
Examples
How it reads on the page
Synthetic, written by LRA — not from a company report, not text from any standard.
We reviewed 78% of our workforce during the year, covering 390 of 500 employees. Those reviews took 1,560 training hours in total, which is an average of 3.2 hours per employee reviewed.
Illustrative breakdown of employee review activity (people)
| Category | Reviewed employees | Not reviewed |
|---|---|---|
| Headcount | 390 | 110 |
Synthetic illustration only. This shows how to present the share of employees reviewed, the total time spent, and the average time per reviewed employee in a single quantitative disclosure.
We covered 62% of our staff in the period, equal to 248 of 400 employees. The related learning time came to 744 hours overall, or 3.0 hours for each employee included in the review.
Illustrative breakdown of employee review activity (people)
| Category | Reviewed employees | Not reviewed |
|---|---|---|
| Headcount | 248 | 152 |
Synthetic illustration only. This example combines the proportion of staff covered, the total hours delivered, and the average hours per person covered.
Quality ladder
- Basic
We reviewed 100% of our people and recorded 1,200 training hours.
- Better
We reviewed all employees, logged 1,200 training hours in total, and that worked out at 12 hours per employee on average.
- Best
We reviewed all employees across the year, recorded 1,200 training hours in total, and the average of 12 hours per employee was unchanged because our training plan and headcount mix stayed broadly stable.
Check your understanding
Scenarios to work through
A group of 240 staff completed a learning programme during the year, but the HR report only shows the total course hours and does not say how many people were included in the review. The team also has a separate spreadsheet for managers and another for the rest of the workforce.
A company recorded 1,860 training hours for the year across 310 employees. The draft note shows the total hours, but the average per employee has not been calculated, and one business unit wants to exclude induction sessions because they were short.
A preparer has a report showing 420 staff reviewed for learning needs out of 500 employees, with 2,100 total hours and an average of 5.0 hours per employee. A later payroll update shows that 12 people joined mid-year and were included in the hours, but the review count was not updated.
A business unit can show course attendance logs for 180 employees and a separate learning-platform export with 900 hours. However, the logs do not identify whether the same people appear in both records, and the average per employee has been estimated by dividing hours by the full headcount of 260 without checking.
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Advice, not assurance
Framework references
Relevant ESRS requirements and related disclosures
ESRS
S1-12
within ESRS S1: Own Workforce
Guides that settle this question
Related & explore
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FAQ
Questions this page answers
The page says to prepare three datapoints: employee review coverage, total training hours, and average training per employee. Use those as the starting data set before you draft the narrative.
Use it as a working checklist to move from raw data to a draft disclosure. The page is designed to help you set up the scope, gather the datapoints, and then turn them into report-ready wording.
The page includes an evidence pack with five items to support assurance readiness. Build your pack around those items so you can show where the numbers came from and how they were checked.
The page says there are five assurance claims to verify, each with a claim, risk and evidence angle. Use that section to test whether your data, calculations and narrative are supported before sign-off.
The page lists common reporting gaps and mistakes to watch for. Use that list as a pre-submission check so you can spot missing data, weak evidence or inconsistent calculations early.
The Download Centre includes a Prep & Assurance workbook in .xlsx format. Use it to organise the datapoints, track evidence and work through the preparation and assurance checks in one place.
The printable Library Card is a quick-reference version of the page. It is useful if you want a compact checklist while you are collecting data, reviewing evidence or drafting the disclosure.
The page has a draft-output section with visualisation ideas, narrative starters and a content-index line. Use those prompts to convert the datapoints into a short, consistent draft rather than starting from a blank page.
Yes, it includes synthetic illustrative example disclosures, including a quantitative table. Treat them as examples of presentation only and make sure your own figures and wording match your actual data.
The page has a 'From company reports' table that links to published reports where the topic is disclosed. Use it to see how others present the topic, but keep your own drafting based on your organisation’s data and evidence.
More questions this page can help with