ESRS metrics explained: a practical guide to ESG data points

Assurance providers will not accept a sustainability report built on undefined data. Every figure in a CSRD disclosure should trace back to a specific ESRS datapoint, and each datapoint needs a clear definition, a named owner, a documented source and a control that supports its reliability.

Getting this right is less about software and more about discipline. The guide below explains how to scope, define, govern and evidence the data points that underpin your disclosures.

Key takeaways

• Scoping starts with double materiality. A structured materiality assessment determines which ESRS disclosures apply, reducing both gaps and unnecessary data collection.

• A data dictionary is the foundation. Each datapoint needs a formal entry covering its definition, ESRS reference, unit, calculation method, source system, owner and evidence requirements.

• Ownership matters as much as data. Clear RACI roles across finance, sustainability, data owners and internal audit reduce ambiguity during collection and review.

• Assurance-ready controls close the credibility gap. Preventive, detective and review controls help turn raw data into evidence an assurance provider can test.

• Phased implementation is realistic. Start with the highest-risk, most material datapoints, then expand coverage in later reporting cycles.

What ESRS covers

The European Sustainability Reporting Standards are organised into two layers. Cross-cutting standards, ESRS 1 and ESRS 2, set out general requirements that apply to every reporting entity, including governance, strategy, impact management and the reporting approach. Topical standards then address specific environmental, social and governance themes.

Each topical standard contains disclosure requirements, and each disclosure requirement breaks down into one or more concrete datapoints. A datapoint is the individual piece of information your team must define, collect, calculate and evidence. For the exact number and scope of datapoints, refer to the latest final standards and implementation guidance published by EFRAG, as the set has evolved through delegated acts and related guidance.

Key terms at a glance

Term	Practical meaning
Datapoint	A single, reportable piece of information, such as total Scope 1 GHG emissions in tonnes CO2e for the reporting period.
Metric	A quantitative datapoint, usually with a defined unit and calculation method.
Disclosure requirement	The mandatory reporting obligation within a standard. One disclosure requirement may contain several datapoints.
Application guidance	Explanatory material that helps teams interpret a disclosure requirement.
Policy, action, metrics and targets	The categories under which ESRS organises many topical disclosures.

Scoping is driven by double materiality. Your organisation reports on topics where it has material impacts on people or the environment, known as impact materiality, or where sustainability matters create material financial risks and opportunities, known as financial materiality. This assessment determines which topical standards, and therefore which datapoints, are applicable.

After the materiality assessment, teams often need a practical checklist to compare their data dictionary against the relevant disclosure requirements and identify where a definition, owner or evidence source is still missing.

For a consolidated summary of data points and a downloadable Excel workbook that can help teams review disclosure scope, KEY ESG's guide to ESRS metrics can be a useful starting point. Teams should still cross-check against official EFRAG publications for the authoritative text.

How to scope material data points

Turning a broad set of standards into a working reporting perimeter takes a structured workflow. The steps below provide a practical sequence.

1. Confirm the reporting boundary and governance roles. Verify which entities fall within scope using official CSRD guidance and your national regulator's portal. Appoint a scoping lead, typically from sustainability or finance, and set up cross-functional workshops.

2. Perform a double materiality assessment. Score each ESRS topic for impact materiality and financial materiality. Document the rationale for including or excluding topics. This step determines which topical standards apply.

3. Draft an ESRS data dictionary. For every applicable datapoint, create a dictionary entry with the following fields: name, plain-language definition, ESRS reference, data owner, source system, unit of measurement, reporting period, calculation and estimation rules, evidence source, collection frequency and control owner.

4. Prioritise by risk, readiness and stakeholder relevance. Not all datapoints carry the same assurance risk. Rank them so the team can focus first on high-impact areas where data gaps are largest.

5. Map to existing frameworks and internal metrics. Where a datapoint aligns with GRI, TCFD, or internal KPIs, note the mapping. If SFDR alignment is relevant, cross-check definitions and calculation methods against the official SFDR Regulatory Technical Standards.

6. Define acceptance thresholds and the treatment of estimates. For each datapoint, state the acceptable margin of error, when proxies or estimates may be used and what documentation is required when they are.

Operating model considerations

A data dictionary is only useful if someone is responsible for each entry and a clear process surrounds it. Break the operating model into people, process and technology.

People

Use a RACI matrix to assign responsibilities. The CFO or finance director is typically accountable for reported figures. The sustainability lead is responsible for definitions, methodology and stakeholder engagement. Data owners in business units are responsible for collection and first-level validation. Internal audit provides independent review, and an assurance liaison coordinates with external providers. For broader context on how business operations teams build cross-functional accountability, this business operations resource can be a useful read.

Process

Align the sustainability data cycle with your financial close calendar. Define monthly or quarterly cut-off dates, change-control procedures for updating definitions or formulas, variance-analysis thresholds that trigger investigation and an escalation path for unresolved data issues. A clear calendar reduces last-minute work before the reporting deadline.

Technology

Teams need a central place to manage the data dictionary, track lineage from reported figures back to source systems, apply validation rules, store evidence securely and control access by role. A sustainability data platform can support these tasks, but it does not replace governance. 

KEY ESG is one option in this category, positioned as an example audit-grade AI sustainability platform and unified hub for carbon and ESG data, with AI-supported validation and an MCP connector that lets external AI assistants such as Claude, ChatGPT, Mistral and Cursor query live, validated sustainability data.

It should be evaluated alongside other tools based on scope, controls, integrations and assurance requirements, without treating any platform as a guarantee of compliance.

Readiness checklist

Phase	Actions
Before you collect	Lock definitions, confirm owners, agree estimation rules and set up evidence folders.
During collection	Apply validation rules at entry, flag exceptions and run completeness checks.
Pre-assurance review	Reconcile figures to source systems, complete owner attestations and resolve open queries.
Post-assurance improvements	Log findings, update the dictionary and refine controls for the next cycle.

Audit grade governance

The objective of governance controls is to ensure each datapoint meets four qualities: completeness, accuracy, timeliness and traceability. Controls usually fall into three categories.

Preventive controls stop errors before they enter the data set. Examples include authorised data-entry roles, locked definition fields that prevent ad hoc changes and required fields that block submission of incomplete records.

Detective controls identify errors after entry. These include reconciliations between the sustainability ledger and source systems, variance thresholds that trigger review when a value moves beyond an expected range and duplicate checks on entity or period combinations.

Review controls add a layer of judgement. Data owners attest to accuracy at each collection cycle. Management signs off before figures are released for assurance. Internal audit samples a subset of data points each period to test whether controls are operating as designed.

For sampling, define a risk-based approach. High-materiality datapoints may require full-population testing. Lower-risk items can use representative samples. Retain evidence for a period aligned with your national transposition of the CSRD, and verify detailed requirements against official regulator materials.

Putting it together: a worked example

Consider a single datapoint from the data dictionary to illustrate how the pieces connect.

Field	Example entry
Name	Total Scope 1 GHG emissions
Definition	Direct greenhouse gas emissions from owned or controlled sources, reported in [unit] for the [period].
ESRS reference	ESRS E1, [paragraph reference]
Owner	Group environmental manager
Source system	Energy management platform, fuel purchase records
Calculation rule	Activity data multiplied by published emission factors for the reporting year.
Estimation rule	Where meter data is unavailable for the final month, extrapolate from 11-month actuals and flag the result as estimated.
Evidence	Meter readings, invoices, emission factor source document and calculation spreadsheet.
Control owner	Finance business partner, environmental reporting.
Review	Owner attestation at quarter end and management sign-off before external assurance.

This structure makes each datapoint self-documenting. An assurance provider can trace the reported figure back to the source, understand the calculation, review the controls and inspect the evidence without relying on informal knowledge.

Conclusion

Reliable disclosure rests on three things: clear definitions, named ownership and tested controls. When every ESRS datapoint has a governed dictionary entry and an evidence trail, assurance becomes a validation exercise rather than a reconstruction effort.

Organisations that invest in this discipline now can make each subsequent reporting cycle faster, more accurate and more useful to the stakeholders who rely on the data.

FAQ

What is the difference between a datapoint and a metric?

A datapoint is any single piece of reportable information required by an ESRS disclosure, whether quantitative or qualitative. A metric is a datapoint expressed numerically with a defined unit and calculation method. All metrics are datapoints, but not all datapoints are metrics.

How should estimates and proxies be documented?

State when an estimate or proxy is permitted, the methodology used, the margin of uncertainty and the data sources relied upon. Flag estimated values clearly so reviewers and assurance providers can distinguish them from actuals.

Who should own validation checks?

The data owner in the relevant business unit should perform first-level validation. A second line, typically finance or sustainability, should run independent detective controls such as reconciliations and variance analysis. Internal audit provides periodic third-line assurance over the control framework.

How can smaller companies phase their build?

Start with the data points linked to your highest-materiality topics and those most likely to be tested by assurance providers. Build dictionary entries and controls for this core set first, then expand to lower-priority datapoints in later reporting cycles.

What is double materiality in practice?

Double materiality means assessing each sustainability topic from two perspectives. Impact materiality considers your organisation's actual or potential effects on people and the environment. Financial materiality considers how sustainability matters could create risks or opportunities that affect your financial position. A topic is material, and its datapoints are reportable, if it meets either threshold.

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