Data Observability Provides the Foundation for Data Quality at Scale

A survey of Fortune 1000 companies found that improving data quality and usability by just 10% could increase return on equity (ROE) by 16%, resulting in over $2 billion in additional revenue per year for the average company. To achieve this, enterprise data teams need a data observability solution with advanced AI/ML capabilities to automatically detect data and schema drift, anomalies, and lineage. Data observability provides full visibility across the data lifecycle, helping data teams predict, prevent, and resolve unexpected data downtime or integrity problems caused by fragmented data. A multidimensional data observability approach offers a single unified view of the entire data pipeline, enabling automatic monitoring and tracking of data lineage. Advanced AI/ML capabilities can automatically identify anomalies based on historical trends and help data engineers identify root causes of unexpected behaviors in their production environment.