Data quality issues can have severe consequences, including incorrect insights, flawed decisions, and financial loss. Traditional data quality management approaches are often reactive and fragmented, leading to costly and inefficient methods that cause delays and persistent errors. To address this, organizations should consider integrating automated data pipelines with integrated quality checks, which contribute to precise analysis and stable operations. The use of frameworks like Great Expectations (GX) and dbt tests can help enforce data quality standards. Implementing structured data validation across the data lifecycle can significantly reduce errors and enhance data usability. Collaborative efforts between teams are essential to uphold data standards, and modern tools and frameworks can automate the process, ensuring consistency and reliability in data quality checks. Ensuring data quality is critical for making reliable business decisions and maintaining operational efficiency, and organizations should consider using tools like Dagster to integrate comprehensive data quality checks into their data pipelines.