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Organizations are increasingly focusing on creating and operationalizing data products to enhance business outcomes, leading to a need for robust data quality monitoring to prevent silent data issues that can negatively impact these products. Soda's Schema Evolution Monitor, part of an automated monitoring suite, addresses this by detecting and alerting on changes in dataset schemas, such as additions, deletions, or type changes of columns, without requiring configuration. This ensures that data teams are promptly notified of potential issues, allowing them to mitigate downstream impacts like incorrect reports or flawed machine learning models. While the current system effectively monitors schema changes, Soda is developing a new language to enhance data reliability by enabling schema testing at various stages of data processing, which will allow for quarantine of problematic data. This upcoming feature is expected to significantly improve the ability to maintain data quality and trust in data products.