When your data warehouse project is racing against the clock, maintaining data quality is crucial to avoid future complications. To balance speed and accuracy, consider these strategies:

• Automate data validation: Use tools to automatically check data for errors, saving time and reducing human error.

• Implement incremental loading: Load data in smaller, manageable chunks to catch and fix issues early.

• Prioritize critical data: Focus on ensuring the quality of the most important datasets first.

What strategies have you found effective in maintaining data quality under tight deadlines?