Data Quality refers to how accurate, complete, consistent, and reliable a dataset is. High-quality data is essential for AI systems to make accurate predictions and informed decisions.
Key factors of data quality:
- Accuracy: Data reflects the real world correctly.
- Completeness: No important data is missing.
- Consistency: Data is uniform across different sources.
- Timeliness: Data is up-to-date and relevant.
Example: For an AI system predicting weather, data quality means having accurate, complete, and recent measurements of temperature, humidity, and wind.
Poor data quality can lead to errors or bias in AI models, making it a critical part of any AI project.