The text discusses the importance of reliable data in decision-making processes, emphasizing that good data quality is not enough. It suggests assessing data reliability from three perspectives: data quality, data reconciliation, and data drift. Data quality involves ensuring that data meets certain standards, while data reconciliation checks if data can be traced back to its sources. Data drift refers to changes in the structure or distribution of data over time. The text proposes a framework for data reliability that automates these processes and integrates them into an overall data observability system.