ESRS 2026 Datapoints: How to Build a Reliable and Verifiable ESG Data Collection System
The ESRS 2026 datapoints translate European sustainability reporting requirements into individual pieces of information that companies must identify, collect, control and, where applicable, disclose.
On 28 August 2026, EFRAG published the new Draft List of Datapoints for the revised European Sustainability Reporting Standards. The list contains 530 datapoints and offers companies a simpler structure than the previous framework. However, EFRAG clarifies an essential point: the list does not replace the standards, does not constitute official implementation guidance and should not be used as a checklist to be completed in full.
Each company must first assess which sustainability matters are material. Only then can it select the relevant information and design its ESG data collection system.
This clarification changes the way organisations should approach the process. The challenge is not to find 530 answers. It is to understand which information genuinely describes the organisation’s impacts, risks, opportunities, policies and performance.
The new EFRAG list can therefore become a useful tool. However, it creates value only when the organisation connects it to robust processes, clear responsibilities, reliable sources and consistent controls. The quality of sustainability reporting depends on the quality of the organisational system that generates the data.
In summary
The ESRS 2026 datapoints identify the individual pieces of information covered by the European Sustainability Reporting Standards. Companies should not treat them as a checklist. They should select the relevant datapoints through double materiality and assign each one an owner, a source, a calculation method, supporting evidence and a control process. An effective system makes ESG data traceable and verifiable while enabling management to use them in business decisions.
What are ESRS datapoints?
ESRS datapoints are the elementary units of information that make up the disclosures required by the European Sustainability Reporting Standards. Each datapoint describes a specific piece of environmental, social or governance information.
A datapoint may require a narrative description, a yes-or-no answer, a percentage, a monetary value, a number of people, an amount of energy consumed, a volume, an emissions figure or a date.
The same disclosure may therefore include very different types of information. For example, a company may need to state whether it has adopted a particular policy, describe its content, explain how it applies the policy and provide a metric representing the results achieved.
The EFRAG list connects each datapoint to the relevant standard, disclosure requirement and paragraph. It also specifies the data type, any applicable conditions, possible alternative presentation methods and the required level of disaggregation.
This structure helps companies turn the regulatory text into an operational system. The datapoint becomes the connection between the ESRS requirement, the internal source, organisational responsibility and the final reported information.
For a broader understanding of the framework, see AR19’s analysis of how to integrate ESRS and ESG into strategy, culture and governance.
On 28 August 2026, the EFRAG Secretariat published the draft datapoint list for the revised European Sustainability Reporting Standards, which the European Commission adopted on 3 July 2026. The document brings together 530 datapoints and organises them within a clearer and more accessible structure. However, EFRAG specifies that this is non-authoritative supporting material: the list does not replace the ESRS, does not constitute official implementation guidance and should not be used as a comprehensive compliance checklist.
The draft will remain open for public fatal-flaw review until 23 October 2026, while the final version is expected by the end of the year.
What changes with the new ESRS 2026 datapoint list?
The new ESRS 2026 datapoint list reduces the complexity of the previous framework and clarifies the nature of the information required.
The European Commission adopted the revised ESRS on 3 July 2026. According to the Commission, the revised standards reduce mandatory datapoints by more than 60% and the overall number of datapoints by more than 70%. EFRAG subsequently translated the revised ESRS into the Draft List published on 28 August.
The list contains 530 datapoints. Most depend on the double materiality assessment. Some, however, concern general information about the sustainability statement under ESRS 2 BP-1 and do not follow the same assessment process.
EFRAG has also introduced a more precise classification of data formats. The list distinguishes between:
narrative and semi-narrative information;
numerical data expressed as integers or decimals;
percentages and monetary values;
data relating to mass, volume, energy and emissions;
information expressed through dates, years or alternative presentation methods.
The simplification helps companies navigate the requirements. However, it does not eliminate the need to give each piece of information an organisational meaning.
Emissions data require a calculation methodology. Workforce information requires consistent criteria across companies, sites and reporting periods. A disclosure concerning policies requires approved documents, assigned responsibilities and evidence of effective implementation.
Reducing the number of datapoints does not therefore automatically reduce the organisational work involved. The revised framework removes redundancies but increases the importance of the quality and relevance of the information selected.
This topic is connected to AR19’s previous analysis of European ESG simplification and the revision of the CSRD and ESRS.
The role of materiality in selecting ESRS datapoints
The list published by EFRAG organises the information required by the revised ESRS and facilitates its operational analysis. However, it does not determine which datapoints are relevant to each company. This selection depends on the double materiality assessment and the professional judgement supporting it.
Using the list as a comprehensive checklist risks shifting attention from information quality to formal compliance. The organisation may collect non-significant data, increasing time, costs and the possibility of inconsistencies. Alternatively, it may focus on completing individual fields without assessing whether the information accurately represents its impacts, risks and opportunities.
Double materiality helps define priorities by considering:
the effects of the organisation’s activities on people and the environment;
the influence of sustainability matters on its financial position, performance, development and business continuity.
Based on this analysis, each organisation can connect its material impacts, risks and opportunities to the relevant disclosures and datapoints.
The EFRAG list therefore provides a reference structure. Governance remains responsible for ensuring that the information selected reflects the organisation’s profile, business model and genuinely relevant sustainability matters. From this perspective, AR19 supports companies in turning double materiality into a concrete process by connecting reporting requirements with risk management, internal responsibilities and strategic decisions.
How should ESG data collection governance be structured?
ESG data collection governance begins when the company defines who produces, controls, approves and uses each piece of information.
Many organisations assign coordination to the sustainability function. This may be appropriate, but the ESG function does not directly own all the required data. The information resides within the systems and activities of other departments.
HR manages data on employment, diversity, remuneration, training and wellbeing. EHS collects information on safety, emissions, waste, water and energy. Procurement understands suppliers and the value chain. Operations controls production processes, consumption and operational performance. Finance has expertise in consolidation, reconciliation and controls. IT manages systems, access permissions and integrations.
Management defines objectives and priorities.
For each material datapoint, the system should identify at least four roles:
the data owner, responsible for the meaning and quality of the data;
the person or function that collects or calculates the information;
the person responsible for performing the control;
the person who approves the data before disclosure.
This distinction reduces ambiguity. It also prevents a single department from having to interpret, reconstruct and validate information produced elsewhere.
Governance must also clarify deadlines, escalation procedures and the management of anomalies. If a source is unavailable or two systems produce different values, people must know who makes the decision and how it should be documented.
What makes ESG data reliable?
ESG data are reliable when they accurately represent the phenomenon being measured, use consistent criteria and can be reconstructed through verifiable sources and supporting evidence.
Numerical precision alone is not enough. A value may be mathematically correct but methodologically weak. It may use the wrong reporting perimeter, exclude certain sites, apply outdated conversion factors or compare periods constructed using different criteria.
Each datapoint should therefore have a methodology record containing:
a definition of the data;
the organisational and time boundaries;
the unit of measurement;
the primary source;
the formula or calculation method;
any estimates and assumptions;
the update frequency;
the responsible owner;
the available evidence;
the planned controls.
A reliable system also maintains a record of changes. If the company changes a formula, source or reporting perimeter, it must document the change and assess its effect on comparability.
The objective is not to eliminate every estimate. In many cases, direct measurement is not possible. However, the company must make the methodology transparent, explain its assumptions and apply the same criterion consistently.
How can ESG data collection become verifiable?
ESG data collection becomes verifiable when a person independent of the process can trace a reported value back to the source that generated it.
This process is often called data lineage. It describes the sequence connecting the original source, subsequent processing, controls and the final value.
Energy consumption data, for example, may originate from invoices or meter readings. The company assigns the data to the relevant sites, converts the units of measurement, excludes any non-relevant consumption, consolidates the values and submits the result to review. Each step must leave accessible evidence.
Traceability requires simple and consistent rules. Files must use recognisable names and versions, systems must restrict access, approvals must be documented and each change must leave an audit trail. Supporting evidence must also remain connected to the relevant reporting period and datapoint.
The CSRD requires assurance over sustainability reporting for companies within its scope. Controls cannot therefore begin when the report is nearly complete. The organisation must design verifiability when it defines the process, responsibilities and data collection methods.
The quality of the evidence also depends on everyday behaviours. Accurately recording an incident, consumption figure, deviation or critical issue requires attention, accountability and a willingness to make less favourable information visible. A strong safety and sustainability culture therefore increases data reliability by making traceability part of the way people work rather than an additional control performed at the end.
Digital tools can support this system, but they cannot replace clear responsibilities, shared procedures and consistent behaviours. This distinction leads to the next question.
Is an Excel file enough to manage ESRS datapoints?
An Excel file can support the initial mapping process, but it does not constitute a reliable ESG data collection system on its own. EFRAG publishes the datapoint list in Excel format because the tool facilitates consultation, analysis and connections between requirements. However, the file does not replace processes, controls, responsibilities or corporate information systems.
The appropriate technical solution depends on the size and complexity of the organisation. A company with a small number of sites and limited sources may use relatively simple tools. A group with multiple companies, countries, facilities and suppliers will require more structured workflows, integrations and automated controls.
Before selecting software, the company must clarify the process. Digitising unclear responsibilities or unshared formulas makes errors faster, not data more reliable. Technology should be introduced after defining the glossary, roles, sources, frequencies, controls and consolidation criteria.
Leadership and organisational culture as the foundations of ESG data quality
AR19’s experience in complex organisational environments shows that ESG data quality also depends on the culture through which the organisation produces, shares and uses information. A significant proportion of sustainability data originates from people’s everyday behaviours.
A procedure can establish how to record an incident, classify waste, measure consumption or report a critical issue. However, the reliability of that information depends on how consistently people apply the procedure and how responsibly they make anomalies visible.
If managers consider ESG data collection a secondary compliance task, teams will tend to postpone it. If the organisation penalises those who report problems, some information may remain incomplete. If functions operate separately, they may use different definitions, boundaries and sources. If objectives encourage people to present positive results only, the risk of errors and unbalanced representations increases.
Leadership must therefore explain why the data matter, how they will be used and which decisions they can support. Leaders must also create an environment in which people can report anomalies, methodological limitations and negative results without concealing them.
This principle is already central to safety culture: identifying and communicating a problem is the prerequisite for managing it. The same applies to sustainability. An organisation that values transparency, accountability and learning generates more complete and reliable information.
This is where reporting and organisational culture meet. Verifiability requires technical expertise and appropriate systems, but it becomes established through consistent behaviours, cross-functional collaboration and leadership capable of turning ESG data into a tool for awareness and improvement.
From disclosure to decisions: the value of ESRS datapoints
The value of ESRS datapoints extends beyond reporting. Information becomes genuinely useful when the company connects it to risks, objectives, actions and operational performance.
Data collected only for annual publication provide a static snapshot. When management integrates them into business processes, the same information can help identify deviations, compare sites and functions, assess investments and verify the effectiveness of the actions taken.
An emissions indicator can guide energy priorities. Data on injuries and near misses can reveal where exposure is increasing. Information on turnover, training and absenteeism can highlight organisational issues. Supplier data can support qualification processes, audits and supply chain continuity.
EFRAG analysed 905 sustainability statements covering the 2025 financial year. Companies identified an average of 6.4 material ESRS topics but defined measurable targets for only 3.3 topics. This gap shows that recognising a priority does not yet mean having a system capable of managing it.
To reduce this gap, organisations must move from disclosure to management. ESG data must become part of periodic reviews, risk management processes, managers’ objectives and management decisions. Data collection then becomes part of a continuous cycle connecting observation, assessment, action and verification of results.
This is where the datapoint system expresses its most concrete value. From the AR19 perspective, collecting reliable data means developing an organisational capability: understanding what is happening, recognising critical issues promptly and making more informed decisions. This approach requires organisations to integrate risk management, safety, sustainability and business performance within the same governance model.
The quality of sustainability reporting therefore depends on the quality of the organisation that produces and uses the information. When responsibilities, culture, processes and leadership operate consistently, ESRS datapoints stop being a set of obligations to fulfil and become tools for guiding improvement and creating value over time.
An operational roadmap for building an ESG data collection system
No single operating model works for every organisation. However, based on the ESRS requirements and the principles of governance, traceability and information control, the process can be structured into seven progressive steps.
1. Define the reporting perimeter
The company identifies the entities, sites, activities, value-chain relationships and reporting period. It also clarifies the boundaries between consolidated data, exclusions and estimates.
2. Complete the double materiality assessment
The organisation identifies its material impacts, risks and opportunities. It then connects the assessment results to the relevant disclosures and datapoints.
3. Create an ESG data dictionary
Each piece of information receives a shared definition. The dictionary specifies units of measurement, formulas, sources, reporting frequencies and consolidation rules, reducing the risk that different functions interpret the same data differently.
4. Assign roles and responsibilities
The company identifies data owners, the people responsible for collecting the data, those responsible for controls and the individuals who approve the information.
5. Map sources and supporting evidence
Each datapoint is connected to systems, documents, records, invoices, measurements or other verifiable sources. The organisation also defines which evidence must be retained and how it should be associated with the relevant reporting period.
6. Design the controls
The organisation establishes completeness, accuracy and consistency checks. It also introduces comparisons with previous periods, reconciliations with other systems and procedures for managing anomalies or missing information.
7. Integrate the data into decision-making
Management uses the information in planning, risk management, target setting and performance review processes. ESG data therefore move from reporting into the management of the organisation.
The roadmap should not begin with software. It should begin with an understanding of the business context and the information genuinely required to manage material sustainability matters. Technology comes later, making a process more efficient once it already has clear definitions, responsibilities and controls.
Within this process, the contribution of the AR19 approach does not consist of imposing a predefined technical sequence. It supports implementation through employee engagement, leadership accountability, performance measurement and review of results. These elements turn a data collection system into a stable organisational capability that is genuinely integrated into the business.
From reporting to organisational maturity: the AR19 perspective
The ESRS 2026 datapoints provide a simpler structure, but the quality of the final information depends on the maturity of the organisation.
A company may have detailed procedures and advanced digital tools. If functions do not collaborate, the people responsible do not understand the meaning of the information and leadership does not use the results, the system will remain fragile.
AR19 views sustainability, organisational culture, leadership and risk management as interconnected elements. ESG data collection must therefore become part of the organisation’s actual way of operating.
Assessment, clear responsibilities, training, leadership development and cross-functional engagement help the company build a consistent system. The objective is not simply to produce compliant sustainability reporting. It is to create information that enables the organisation to understand risks, monitor progress and make more informed decisions.
The real question is therefore not how many datapoints the company can complete. It is whether the data collected reliably describe what the organisation does, decides and intends to improve.
Article prepared under the supervision of Alberto Rosso, CEO of AR19.
FAQs
Are ESRS datapoints and ESG KPIs the same thing?
No. An ESRS datapoint identifies a specific piece of information required by the reporting standards. An ESG KPI measures performance, a result or a trend. Some datapoints contain KPIs, while others require descriptions, confirmations, policies or qualitative information.
Must all ESRS 2026 datapoints be reported?
No. The company must assess the materiality of sustainability matters and select the relevant information. The EFRAG list is not a checklist to be completed in full. Some general information about the sustainability statement follows specific rules and does not depend on double materiality.
Who should validate ESG data within the company?
Validation depends on the company’s organisational structure. The person responsible for the function generating the data should verify their content and methodology. A second level of control may involve Sustainability, Finance, Risk Management, Compliance or Internal Audit. Management then approves the relevant information.
Do the ESRS 2026 requirements also apply to SMEs?
The ESRS apply to companies that fall within the scope of the CSRD. Smaller companies outside the mandatory scope may use the Voluntary Standard. This standard helps them respond to requests from banks, investors and large customers without adopting the entire ESRS framework.
When should ESG data collection begin?
Data collection should begin at the start of the reporting period, not at the end of the financial year. The company should define sources, frequencies, responsibilities and controls in advance. This allows it to correct anomalies during the year and reduce the need to reconstruct information at the end of the reporting cycle.
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