Poor data quality can lead to significant financial losses and damage an organization's reputation and customer trust. Maintaining clean, reliable datasets is crucial for effective decision-making and operational efficiency in today's data-driven world. Data hygiene practices involve identifying and correcting inconsistencies, errors, and anomalies within data. By implementing robust data hygiene strategies and best practices, organizations can ensure the reliability of their data assets and maintain a solid foundation for decision-making processes.