Making Data Work For You: Data Observability Starts with Data Quality

The text discusses the challenges faced by large enterprises in managing their data effectively due to insufficient discipline and lack of visibility. It highlights that over 70% of employees have access to unauthorized data, and 80% of analysts' time is spent on manual data analysis due to poor initial data quality. The text emphasizes the importance of data observability in monitoring and managing data at scale across hybrid data lakes and warehouses. It also distinguishes between data sources and their derivatives, and explains the significance of data quality for various stakeholders like data producers, consumers, and critical data elements. Furthermore, it discusses different types of data outages and testing methods to ensure data quality throughout the data lifecycle. The text concludes by stating that data observability can help solve data quality issues and suggests using Acceldata's platform for automating data quality and reliability at scale.