Quality Assurance
Discover how Tracenable validates Climate Targets data through automated checks, statistical tests, and human review to deliver audit-grade reliability.
Introduction
High-quality Climate Targets 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 data point 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 climate target data points. These checks are designed to quickly spot issues that should never occur in valid data, such as:
Impossible values – negative or unrealistic reduction percentages (e.g., targets above 100% or below 0%).
Date inconsistencies – target years that precede the baseline year, or implausibly distant time horizons.
Structural errors – missing key attributes such as baseline year, reduction magnitude, or target coverage.
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 – Flag abrupt changes in target ambition, such as sudden increases or decreases in reduction percentages or target years.
Outlier detection – Identify targets that deviate significantly from industry or regional benchmarks (e.g., unusually short time horizons or extreme reduction rates).
Distribution analysis – Verify that disclosed targets follow expected patterns across sectors and geographies, ensuring realism and comparability.
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 climate targets 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 Climate Targets dataset becomes more robust over time.