Quality Assurance
Discover how Tracenable validates waste data through automated checks, statistical tests, and human review to deliver audit-grade reliability.
Introduction
High-quality waste data depends on more than just good collection and standardization: it requires rigorous validation. At Tracenable, we combine automated testing, statistical analysis, and expert human review to ensure that every waste metric meets the highest standards of accuracy, consistency, and reliability.
Our Quality Assurance (QA) process is multi-layered, designed to detect errors, catch anomalies, and confirm that each data point is both faithful to the original disclosure and fit for use in compliance, benchmarking, and research.
Automated Validation Checks
The first layer of QA relies on automated rules that run across all waste metrics. These checks are designed to quickly spot issues that should never occur in valid data, such as:
Impossible values – negative or implausibly large waste quantities.
Unit inconsistencies – figures reported in mismatched or conflicting units across years.
Structural errors – totals that do not match the sum of their components.
These rules ensure that obvious errors are flagged immediately and never propagate into the dataset.
Statistical and Machine Learning Tests
Beyond simple rules, we apply more advanced techniques to identify subtle anomalies:
Time-series consistency checks – highlight sudden spikes or drops in reported waste generation.
Outlier detection – identify company disclosures that deviate significantly from industry norms.
Distribution analysis – verify that waste metrics follow expected statistical patterns across sectors.
These methods help us flag values that may be technically valid but require closer review.
Human-in-the-Loop Review
Not all issues can be resolved automatically. Our QA process therefore includes a human-in-the-loop review, where trained analysts validate flagged data points:
Contextual review – analysts check values against the original disclosure to confirm interpretation.
Dual validation – two independent reviewers may assess the same data point.
Arbitration – discrepancies between analysts are escalated to senior analysts for final decision.
This ensures that ambiguous or complex waste disclosures are interpreted correctly, and that every value remains fully traceable to its source.
Continuous Improvement
Each QA outcome feeds back into our systems:
Automated rules are updated when new error patterns are identified.
Machine learning models are retrained to improve anomaly detection.
Documentation is refined to capture new edge cases and classification challenges.
This iterative loop ensures that the Waste Dataset becomes more robust over time.