Focus on minimal, impactful transformations to preserve data integrity while optimizing for model performance:

1. Aggregation: Keep it meaningful—aggregate only when it reduces noise without losing key patterns.

2. Enrichment: Add only relevant external data (e.g., demographics) that directly improves predictive power.

3. Deduplication: Critical for accuracy—remove exact duplicates, but validate fuzzy matches to avoid over-cleaning.

Tools like Alteryx: Use its profiling tools to track how each step affects distributions/outcomes. Test model performance on raw vs. transformed data to find the right balance.

Key: Transform just enough to improve quality without distorting the underlying trends your model needs.