Data Collection Methodology
Learn how Tracenable collects, standardizes, and validates EU Taxonomy data through a five-step human-in-the-loop methodology, with links to detailed subpages on sources, standardization, and QAs.
Introduction
The value of EU Taxonomy data lies not just in its availability, but in its clarity, comparability, and traceability. At Tracenable, we designed a data collection methodology that combines rigorous research, comprehensive sourcing, and advanced human–AI workflows to produce EU Taxonomy metrics that are both granular and broadly applicable.
Our approach is built around four principles: define with authority, collect comprehensively, standardize precisely, and validate rigorously.
Our Five-Step Data Collection Approach
Defining the Schema through Research
Our EU Taxonomy data is meticulously standardized to align with the Commission Delegated Regulation (EU) 2023/2486, capturing both activity-level details and aggregate metrics across turnover, CAPEX, and OPEX. This rigorous standardization ensures seamless regulatory compliance and cross-company comparability.
Comprehensive Collection of Disclosures
EU Taxonomy data can appear in many places: annual reports, sustainability reports, regulatory filings (e.g., SFDR/CSRD templates), investor presentations, standalone data spreadsheets, or hidden on a webpage deep in a company’s site.
Our infrastructure is designed to capture all of it. Through automated web monitoring and targeted expert retrieval, we ensure that no disclosure is overlooked. This comprehensive approach minimizes blind spots and provides the broadest possible coverage of EU Taxonomy reporting worldwide.
Converting Disclosures into Structured Data
EU Taxonomy disclosures vary widely in format: from PDFs and Excel annexes to HTML tables or embedded text within narrative sections. Our AI-driven pipelines convert these raw files into a unified, machine-readable structure (e.g., PDF to markdown).
From there:
Computer vision extracts and parses tables and figures.
NLP models detect taxonomy-related passages, extract KPI values, and identify eligibility classifications.
Classification rules map disclosures into aligned, eligible but not aligned, non-eligible, and combined categories under each KPI.
The result: machine-readable, standardized data points that preserve traceability to the original disclosure.
Data Human-in-the-Loop Validation
AI brings speed and scalability, but human expertise ensures accuracy and context. Each extracted data point is flagged with quality indicators, guiding our analysts in review. Two independent reviewers typically validate taxonomy data, with arbitration applied where discrepancies remain.
This process allows us to:
Correct AI misclassifications when disclosures are complex or ambiguous.
Preserve context from narrative disclosures.
Continuously improve our models through feedback.
The outcome is audit-grade EU Taxonomy data that users can trust.
Rigorous Quality Assurance
Finally, the EU Taxonomy dataset undergoes multi-layered quality checks:
Automated tests flag anomalies (e.g., KPIs not summing correctly, negative percentages, implausible trends).
Machine learning models detect outliers across time series and peer groups.
Manual audits ensure completeness and resolve edge cases.
This combination of automation and human oversight guarantees that every metric delivered is reliable, comparable, and decision-ready for use in compliance, benchmarking, and research.
Learn More
To explore the methodology in detail, visit:
Data Sources – Where EU Taxonomy disclosures come from and how they are collected.
Standardization Guidelines – How activities, KPIs, and eligibility categories are normalized for consistency.
Calculation Logic – How derived values are computed using transparent rules.
Quality Assurance - The validations and controls that safeguard data integrity.