In ESG reporting, data quality management is crucial for ensuring that the final report meets compliance standards and is auditable. The CSRD (Corporate Sustainability Reporting Directive) provides flexibility but also demands transparency in handling data gaps or uncertainties. This article will cover the key challenges and strategies for managing data quality and addressing cases where data is unavailable.
1. Types of Data Quality Challenges in ESG Reporting Initiatives
There are three common data quality challenges companies face when preparing ESG reports:
a) Data is Not Available
In some cases, companies may not have sufficient data to answer certain questions. This may be due to:
Gaps in data collection processes
Missing data infrastructure
Lack of historical information
From a compliance standpoint, this can lead to non-compliance penalties and damage the company’s reputation, as stakeholders may view the company as lacking transparency or being underprepared to manage material ESG topics.
b) Data is Available but Based on Assumptions or Judgments
Sometimes, companies may rely on approximations or assumptions to answer certain questions. While this demonstrates an attempt to comply, it introduces a risk of inaccuracies. Regulatory bodies and auditors may require full transparency on these assumptions, and relying too heavily on approximations could weaken the credibility of the report.
c) Data is Available but Uncertain
In some cases, data is available but comes with significant measurement uncertainty. This may happen due to limitations in measurement methods, incomplete datasets, or the use of emerging metrics. If not properly disclosed, this uncertainty can undermine the reliability of the reported data and may result in compliance risks if it leads to misleading reports.
2. Why Data May Be Unavailable
The CSRD comes into full effect in 2025, and its broad scope means that companies may struggle to provide data for every question assessed as material. The reasons for this include:
Not all ESG initiatives have been launched: Companies may not have developed all the systems and practices needed to answer every question.
Inadequate measurement tools: Companies may lack the internal tools or infrastructure to collect certain data.
Strategic challenges: Some questions require challenging the company’s business model, which may take time to develop.
Reliance on external stakeholders: Some data relies on external partners, which can delay or prevent accurate data collection.
For these reasons, the CSRD offers flexibility for some questions, allowing time for companies to gather or improve their data.
3. Handling Data Non-Availability: Metrics and Non-Metrics
Case 1: When the Question is a Metric
A metric is a quantifiable measure used to assess the impact of a company on various ESG aspects. These metrics are usually numerical and must be reported for consistent and verifiable sustainability disclosures. Examples include GHG emissions, employee statistics, or salary data.
No data available? Unfortunately, for metrics, the “I cannot answer this question” option is not allowed. You must report something, even if it is based on a proxy or rough estimate. In such cases, it is important to explicitly mention this in the comments, for audit purposes.
Common metrics that may require estimates include:
GHG emissions (Scope 1, Scope 2, etc.)
Employee statistics (e.g., percentage of disabled employees)
Salaries (e.g., average salaries per geographic area)
To help create proxies or estimates, consult your ESG knowledge database or access Greenly’s Helpcenter for methodological insights.
Case 2: When the Question is Not a Metric
For questions that fall under the categories of Policies, Actions, or Targets, the platform offers more flexibility.
Handling data non-availability: If the data is not available, you can:
Select the option “I cannot answer this question.”
Mark it as “Data Not Available.”
Optionally, provide a justification for the data unavailability in the explanation field.
All questions marked as “Data Not Available” will be the foundation for your ESG Reporting Improvement Roadmap to guide future reporting efforts.
4. Transparency in Handling Data Quality Issues
The CSRD framework offers some flexibility regarding the quality of the provided data, but transparency is mandatory. Companies must disclose situations of data quality issues in specific parts of the reporting.
Disclosures for Data Quality Issues:
Assumptions, Approximations, and Judgments
Must be disclosed in the datapoint ESRS 2, 11b.
This relates to the "Measurements subject to uncertainty" form, which should include each KPI affected by assumptions.
Quantitative Metrics with Measurement Uncertainty
Must be disclosed in the datapoint ESRS 2, 11a.
Use the "Measurements subject to uncertainty" form for each affected metric or KPI.
Future Plans to Improve Data Accuracy
For metrics involving indirect data (such as value chain metrics), companies must disclose plans to improve data accuracy in the future. This is reported in ESRS 2, 10d, through the "Value chain metrics" form and the "Enhancement of future estimations" question.
Transparency in these areas helps companies manage compliance risks while continuously improving their data reporting processes.