Gartner defines augmented data quality (ADQ) solutions as a set of capabilities that deliver advanced features to streamline the identification of quality issues, offer context-aware suggestions for corrective actions, and automate key data-quality processes to ensure cleaner, more reliable data. These purpose-built data-quality solutions support profiling and monitoring, rule discovery and creation, active metadata use, data transformation, data remediation, matching, linking and merging, and role-based usability. The solutions have AI-assistant-enabled features that enhance user experience.
Data observability tools enable organizations to understand the state and health of their data, data pipelines, data landscapes, and data infrastructures, as well as the associated financial costs, across distributed environments. This is accomplished by continuously monitoring, detecting, alerting, analyzing, and troubleshooting data workflows to identify and resolve issues, thereby reducing and preventing data issues and system downtime. The tools also provide information on data lineage, collaborations, and incident management. They go beyond traditional network or application monitoring by enabling users to observe changes, discover unknown issues, and take appropriate actions to deliver reliable data and prevent business interruption.