Gartner defines augmented data quality (ADQ) solutions as a set of capabilities for enhanced data quality experience aimed at improving insight discovery, next-best-action suggestions and process 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 be used to increase automation and effectiveness across a broad range of data quality use cases. These purpose-built solutions include a range of functions such as profiling and monitoring; data transformation; rule discovery and creation; matching, linking and merging; active metadata support; data remediation and role-based usability.
The data integration tools market comprises stand-alone software products that allow organizations to combine data from multiple sources, including performing tasks related to data access, transformation, enrichment and delivery. Data integration tools enable use cases such as data engineering, operational data integration, delivering modern data architectures, and enabling less-technical data integration. Data integration tools are procured by data and analytics (D&A) leaders and their teams for use by data engineers or less-technical users, such as business analysts or data scientists. These products are consumed as SaaS or deployed on-premises, in public or private cloud, or in hybrid configurations.
Gartner defines a Data and Analytics Governance Platform as a set of integrated business and technology capabilities that help business leaders and users to develop and deploy a diverse set of governance policies and monitor and enforce those policies across their organizations’ business systems. These platforms are unique from data management in that data management focuses on policy execution, whereas these platforms are used primarily by business roles — not only or even specifically IT roles.
Master data management (MDM) is a technology-enabled business discipline where business and IT organizations work together for the uniformity, accuracy, stewardship, semantic consistency and accountability of enterprises’ shared master data assets. Organizations use MDM solutions as part of an MDM strategy, which should be part of a wider enterprise information management (EIM) strategy. An MDM strategy potentially encompasses management of multiple master data domains (e.g., customer, citizen, product, “thing,” asset, person/party, supplier, location, and financial master data domains). Data and analytics (D&A) leaders procure MDM tools for data engineers or less-technical users, such as data stewards.