As ESG reporting moves from voluntary to mandatory across jurisdictions, the quality of underlying data has become a critical concern. Organizations that built their sustainability reporting on spreadsheets and estimates now face the challenge of producing audit-ready, investor-grade ESG data.
The Current State of ESG Data
Studies consistently show that ESG data quality lags significantly behind financial data:
Inconsistent methodologies — Different frameworks (GRI, SASB, TCFD, now ESRS) use varying definitions and calculation methods, making data comparison difficult.
Manual collection — Many organizations still rely on email-based data collection from subsidiaries and business units, introducing errors and delays.
Estimation gaps — Scope 3 emissions, supply chain impacts, and social metrics often rely on industry averages rather than actual measurements.
Limited assurance — While financial statements undergo rigorous audit, ESG data has historically received limited independent verification.
Building a Data Quality Framework
Step 1: Define data ownership — Assign clear responsibility for each ESG metric, including collection, validation, and reporting.
Step 2: Standardize definitions — Create an internal data dictionary that maps your metrics to regulatory requirements and ensures consistent interpretation across the organization.
Step 3: Automate collection — Replace manual processes with integrated data systems that capture ESG data at source, reducing transcription errors.
Step 4: Implement controls — Apply the same internal control principles used for financial reporting: segregation of duties, reconciliation, variance analysis, and management review.
Step 5: Prepare for assurance — Engage with auditors early to understand their expectations and build evidence trails that support each reported metric.
The Role of Technology
Modern ESG data platforms can significantly improve quality by automating collection from operational systems, applying validation rules in real-time, and providing audit trails. However, technology alone is insufficient without proper governance, clear processes, and skilled personnel.
The transition to high-quality ESG data is not a one-time project but an ongoing journey of continuous improvement.