Soda Data Quality

AI agents pose unique challenges in data handling as they continue operating with misplaced confidence even when fed inaccurate or stale data, leading to flawed decisions and actions. Unlike traditional software, AI lacks error codes or alerts, making its failures less visible and more insidious. The McKinsey and LangChain surveys highlight significant negative impacts and production barriers due to AI inaccuracies in enterprises. Scenarios such as stale data usage, schema drift, and anomalous data exposure underscore the need for robust data observability within the context layer, ensuring AI has access to real-time data quality signals. Companies like Atlan propose integrating a comprehensive enterprise context layer that encompasses data lineage, governance, and observability to mitigate risks and enhance AI reliability. This infrastructure is crucial for enterprises aiming to develop AI systems that can discern when to act or flag uncertainties, thereby reducing blind risks and improving decision-making accuracy.