In 2018, Amazon scrapped an AI recruiting tool that showed bias against women. The reason? The data it trained on reflected years of gender bias in hiring. This wasn’t a model flaw. It was a data quality failure. And it’s far from rare.

Data quality in AI is a foundational concern. Even the most advanced machine learning algorithms can’t make good predictions if they’re fed poor-quality data. Yet, this critical aspect is often overlooked until something goes wrong.

Common Data Quality Issues in AI

Here are some commonly prominent data quality issues most businesses face in AI.

Bias and Imbalance

Training data that underrepresents certain groups or overrepresents others can lead to skewed models, like facial recognition systems that perform poorly on darker skin tones.

Incompleteness

Missing values or incomplete records can mislead training processes, leading to inaccurate or inconsistent predictions.

Inconsistency

If similar data is labeled or formatted differently (e.g., “NYC” vs. “New York”), the model struggles to generalize effectively.

Noise and Errors

Outliers, typos, or irrelevant data introduce noise that can distract or mislead learning algorithms.

Stale Data

Data that was accurate yesterday may be irrelevant today. In rapidly changing environments, outdated data undermines model performance.

Best Practices to Improve Data Quality

Here are some best practices for you to improve data quality.

Audit Before You Train

Perform a comprehensive audit to identify gaps, anomalies, and inconsistencies in your dataset before feeding it into a model.

Diversify Data Sources

Use data from varied and representative sources to reduce bias and improve model generalizability.

Implement Data Validation Pipelines

Use automated checks during data ingestion to catch missing, malformed, or duplicate entries early.

Continual Monitoring

Model performance should be tracked continuously. Poor predictions often signal underlying data drift or degradation.

Human-in-the-Loop Systems

Include human review in the data labeling process to reduce mislabeling and inject contextual understanding.

Conclusion

Bad data is the silent killer of AI. No algorithm can outperform the quality of the data it’s given. Treat your data like code: test it, monitor it, and never assume it’s perfect.