Data Collection Methodology
Learn how Tracenable collects, standardizes, and validates corporate waste data through a five-step human-in-the-loop methodology, with links to detailed subpages on sources, standardization, and QAs.
Introduction
The value of waste 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 corporate waste 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 Waste Data Collection Approach
Defining the Schema through Research
We start by grounding our work in foundational references such as the EU Waste Framework Directive, the U.S. RCRA, and the Basel Convention. From there, we study voluntary frameworks like GRI 306, ESRS E5, and SFDR to understand disclosure expectations.
This theoretical research is paired with empirical research: analyzing how companies actually report waste in practice across industries and regions. By combining both, we design a data schema that strikes the right balance: as granular as possible, but general enough to apply across thousands of companies worldwide.
Comprehensive Collection of Disclosures
Corporate waste data can appear in many places: sustainability reports, regulatory filings, 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 corporate waste data globally.
Converting Disclosures into Structured Data
Waste disclosures come in many formats: PDFs, Excel annexes, HTML tables, and narrative text. Our AI-driven pipelines first convert raw files into a unified structure (e.g., PDF to markdown).
From there:
Computer vision parses tables and figures.
NLP models identify waste-related passages, detect units, and extract values.
Classification rules map waste into hazardous, non-hazardous, radioactive, or unclassified types, and into recovered vs disposed methods.
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 waste data, with arbitration applied where discrepancies remain.
This process allows us to:
Correct errors where AI misclassifies complex waste categories.
Preserve context from narrative disclosures.
Continuously improve our models through feedback.
The outcome is audit-grade waste data that users can trust.
Rigorous Quality Assurance
Finally, our Waste dataset undergoes multi-layered quality checks:
Automated tests catch obvious anomalies (negative values, implausible spikes, inconsistent units).
Machine learning models detect statistical outliers through unsupervised methods and unusual time-series patterns.
Manual audits ensure nothing slips through the cracks.
This combination of automation and human oversight guarantees that every waste metric delivered is reliable, comparable, and ready for use in compliance, benchmarking, and research.
Learn More
To explore the methodology in detail, visit:
Data Sources - Where waste data comes from and how it is collected.
Standardization Guidelines - How disclosures are normalized into consistent waste types and treatment methods.
Calculation Logic - How missing values are inferred and totals are computed using transparent accounting rules.
Quality Assurance - The validations and controls that safeguard data integrity.