Organizations incur significant financial losses, averaging $15 million annually, due to poor data quality, prompting the adoption of post-mortem analyses to understand and prevent recurrences of such issues. Originally a medical term, the concept of post-mortems has been embraced by software engineers and major tech companies to identify the root causes of system failures and enhance reliability, as seen in Google's blameless post-mortems. By applying this practice to data quality incidents, data teams can not only pinpoint causes of errors but also improve collaboration and communication within the team, fostering a culture where mistakes are viewed as learning opportunities rather than failures. This process not only helps in improving internal processes but also in maintaining transparency and trust with stakeholders, as demonstrated by Facebook's handling of its video metric issues. However, while post-mortems are invaluable, proactive data quality management through automated tools can prevent such incidents, allowing data teams to focus on strategic initiatives rather than repetitive troubleshooting.