Some additional considerations for high-quality data include:
- Data quality metrics: Establishing clear metrics to measure data quality, such as accuracy, precision, recall, and F1-score.
- Data validation: Validating data against known rules, constraints, and expectations.
- Data cleansing: Removing or correcting errors, duplicates, and inconsistencies.
- Data normalization: Normalizing data to a consistent format, scale, or range.
- Data augmentation: Augmenting data with additional information, such as noise, perturbations, or transformations, to improve model robustness.
- Data curation: Curating data to ensure it is relevant, accurate, and complete.
- Data documentation: Providing clear documentation and metadata about the data, including its origin, creation date, and any relevant context.
- Data provenance: Tracking the origin, history, and changes made to the data.