Published: 11 March 2024
Augmented data quality solutions represent a fundamental shift in solving data quality issues through the application of active metadata, AI (e.g., NLP) and graph technologies. Data and analytics leaders can evaluate these solutions’ critical capabilities and common use cases in this research.
Scaling data quality is increasingly dependent upon augmented, two-way data flow with data governance platforms.
Generative AI (GenAI) is rapidly being integrated into data quality solutions, enabling business users to perform more complex data quality tasks, such as interpretation of profiling results and development of data quality validation rules.
Most efforts to augment data quality solutions largely focus on identifying data quality problems by emphasizing profiling, monitoring, rule discovery and rule creation. Less emphasis is placed on data transformation and remediation workflows.
Data observabilityis often used as a low-friction method for introducing data quality to an organization. Many data quality vendors
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Gartner Critical Capabilities research complements Gartner Magic Quadrant™ analyses by offering deeper insights into products and services provided by multiple vendors in specific markets, focusing on how well each product meets detailed functional requirements rather than on the vendors’ overall market positioning.
For more information, see the FAQ on How Products and Services Are Evaluated in Gartner Critical Capabilities or visit the Gartner Critical Capabilities methodology page.
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