Top 7 Data Quality Practices Every ML Team Needs
Top 7 Data Quality Practices Every ML Team Needs
Your model is only as good as your data. Everyone knows this. Almost no one acts on it systematically. Here are seven practices that actually move the needle.
1. Version Your Data Like Code
Every dataset should have a version hash. When a model degrades, you need to know exactly which data it was trained on and what changed. DVC, LakeFS, or even simple checksums - pick something and use it.
2. Validate at Ingestion, Not After Training
Catch bad data before it enters your pipeline. Schema validation, range checks, null detection - all of this should happen at the point of ingestion. Finding a data issue after a 12-hour training run is expensive.
3. Monitor Distribution Drift
Your training data represents a snapshot in time. Production data drifts. Set up automated monitoring that compares incoming data distributions against your training baseline and alerts when they diverge beyond a threshold.
4. Label Audits Are Not Optional
Sample your labeled data regularly and have a second labeler review it. Inter-annotator agreement should be tracked as a metric. If your labelers disagree 30% of the time, your model's ceiling is already low.
5. Document Data Lineage
Where did each dataset come from? What transformations were applied? Who approved it? Data lineage documentation prevents the "nobody knows where this data came from" problem that kills projects months later.
6. Test for Bias Before It Ships
Run fairness audits on your data before training. Check for representation gaps, label imbalances across demographic groups, and proxy variables. Fixing bias in data is easier than fixing it in a trained model.
7. Automate Quality Reports
Generate a data quality report for every dataset version. Include completeness, consistency, accuracy samples, and freshness. Make it part of your CI pipeline so it is impossible to skip.
AI agents that interact with local data face the same quality challenges. The data on your desktop - files, emails, calendar entries - varies wildly in structure and completeness. An agent that handles messy real-world data gracefully outperforms one that only works with clean inputs.
Fazm is an open source macOS AI agent. Open source on GitHub.