Datafold addresses prevalent challenges in data quality testing faced by data teams, highlighting the limitations of traditional methods such as assertion-based tests in identifying unforeseen changes in data. The text recounts personal experiences of data quality mishaps, illustrating how even minor code changes can lead to significant inaccuracies and distrust among business users. It identifies five common pain points, including the slowing down of dbt development and the erosion of trust between data teams and business users due to inaccurate data. Datafold introduces data diffing as a solution, a method that compares tables to detect changes before they impact production, thereby enhancing data quality testing and allowing teams to work with greater confidence and efficiency. This proactive approach aims to restore trust and streamline development processes by ensuring data integrity and transparency throughout the data pipeline.