How We Evaluate Training Data
The methodology behind Data Scorecards
Most AI teams agree that training data quality matters.
Far fewer evaluate it systematically.
Data Scorecards were designed to create a structured evaluation layer for training datasets across provenance, information utility, and safety.
Our goal is not to produce a single score.
It is to make datasets more measurable, comparable, and improvable over time.
Provenance & Metadata
We evaluate whether a dataset is understandable, traceable, and reusable.
This includes source attribution, licensing clarity, metadata completeness, collection transparency, and documentation quality.
Strong provenance becomes increasingly important as datasets scale across organizations and synthetic pipelines.
Information Utility & Quality
We evaluate the usefulness and integrity of the data itself.
This includes signals such as duplication, formatting consistency, information density, structural integrity, and linguistic quality.
Importantly, quality is contextual.
A dataset optimized for one downstream task may perform poorly for another.
Safety & Security
We evaluate potential policy and safety risks across datasets and subsets.
This includes harmful content, adversarial patterns, unsafe generations, anomalous examples, and distributional asymmetries.
Many important risks are sparse rather than evenly distributed. Aggregate evaluation alone often misses them.
Beyond Static Scoring
We view dataset evaluation as the foundation for continuous learning systems.
Over time, training data infrastructure should not just measure datasets once. It should learn from interventions, downstream outcomes, and recurring failure modes.
That is the broader direction behind Data Scorecards: building standardized feedback loops for training data itself.