Gartner defines augmented data quality solutions as set of capabilities for enhanced data quality experience aimed at improving insight discovery, next-best-action suggestions, and automation by leveraging AI/machine learning (ML) features, graph analysis and metadata analytics. Each of these technologies can work independently, or cooperatively, to create network effects that, can then be used to increase data quality automation and effectiveness across a broad range of data quality use- cases. These purpose-built solutions include a range of functions, including profiling and monitoring/detection, data transformation, rule discovery and creation, matching, linking, merging, active metadata support, data quality remediation and role-based usability.