As digital business requires innovations in data quality tools, vendors are competing fiercely by enhancing existing capabilities and creating new capabilities in eight key areas: audience, governance, data diversity, latency, analytics, intelligence, deployment and pricing. Gartner's definition of the market for data quality tools focuses on innovative technologies and approaches intended to meet the needs of end-user organizations in the next 12 to 18 months. It is also linked to broader initiatives in the field of enterprise information management (EIM), including information governance and master data management (MDM). It includes program management, roles, organizational structures, use cases and processes (such as those for monitoring, reporting and remediating data quality issues). It covers much more than just technology. The discipline of data quality assurance ensures that data is "fit for purpose" in the context of existing business operations, analytics and emerging digital business scenarios. This market does not include vendors that only provide DBMSs hosted in infrastructure as a service (IaaS), such as in a virtual machine or container, and managed by the customer. These DBMSs reflect optimization strategies designed to support transactions and/or analytical processing for one or more of the following use cases: Traditional and augmented transaction processing, Traditional and logical data warehouse, Data science exploration/deep learning, Stream/event processing, and Operational intelligence Data is stored in a cloud storage tier (such as a cloud object store, a distributed data store or other proprietary cloud storage infrastructure), and may use multiple data models - relational, nonrelational (document, key-value, wide-column, graph), geospatial, time series and others.
Gartner defines the cloud database management system (DBMS) market as being that for products from vendors that supply fully provider-managed public or private cloud software systems that manage data in cloud storage.
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