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 and Analytics refers to products and services that enable organizations to collect, integrate, analyze, and act on data to drive informed decision-making and business outcomes. This category includes markets that focus on empowering enterprises to manage data pipelines, ensure data quality and governance, extract insights through advanced analytics, and machine learning across structured and unstructured data environments.
A data and analytics governance platform is a set of integrated business and technology capabilities that help business leaders and users develop and manage a diverse set of governance policies and enforce those policies across business and data management systems. These platforms are unique from data management in that data management focuses on policy execution, whereas D&A platforms are used primarily by business roles — not only or even specifically IT roles — for policy management. Data and analytics (D&A) leaders who are investing in operationalizing and automating the work of D&A governance should evaluate this market. The work of D&A governance primarily includes policy setting and policy enforcement, and collaborates with data management (policy execution). Use cases are employed across numerous governance policy categories and multiple business scenarios and asset types (data, KPIs, analytics models). The intersection of use-case/business scenarios, policy categories and assets to be governed is then used to identify the technology capability. These capabilities may share similar names across policy categories, but may not mean the same thing, or may be used differently by various governance personas. For example, data classification in a data security implementation would be quite different from a data classification effort for creating trust models, which would be based on lineage and curation.
Master data management (MDM) is a technology-enabled business discipline that enables business and IT to collaborate on the uniformity, accuracy and semantic consistency of an enterprise’s shared master data assets. Organizations buy MDM solutions to enable their MDM strategy, which is critical for data, analytics and AI strategies. These typically manage multiple data domains (e.g., customer, product, supplier, location), served by a combination of analytical and operational use cases, utilizing one or more implementation styles as per the organization’s needs and data ecosystems.
Master data management (MDM) of customer data solutions are software products that: Support the global identification, linking and synchronization of customer information across heterogeneous data sources through semantic reconciliation of master data. Create and manage a central, persisted system of record or index of record for customer master data. Enable the delivery of a single, trusted customer view to all stakeholders, to support various business initiatives. Support ongoing master data stewardship and governance requirements through workflow-based monitoring and corrective-action techniques. Are agnostic to the business application landscape in which they reside; that is, they do not assume or depend on the presence of any particular business application(s) to function.
Gartner defines metadata management solutions as applications to enable the collection, analysis and orchestration of metadata related to organizational data assets. These solutions enable workflow and operational support to make data easy to find, use and manage. They do this by collating metadata in any form from within its own application and third-party systems, and providing the ability to search, analyze and make decisions on the collated results. They also provide transparent cross-referencing over all related metadata, and derive insights from data (such as usage patterns and performance) through analysis of metadata to support a wide range of data-driven initiatives.