Augmented Data Quality Solutions

Augmented data quality solutions detect and fix errors, remove duplicates, standardize formats, and validate data so it can be trusted for business operations, reporting and decision making. The research helps D&A leaders understand these AI-enhanced solutions to make better purchasing decisions.

Market Definition

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.

Packaged ADQ solutions help implement and support the practice of data quality assurance, mostly embedded as part of a broader data and analytics (D&A) strategy. Typical use-case scenarios include:

Analytics and AI readiness: Data-quality capabilities supporting the preparation and ongoing monitoring of structured, semistructured and unstructured data for operational analytics, performance management, sentiment analysis, improving the quality of data used for training AI models or algorithms, and actual data feeds to production.

Data engineering: Data-quality capabilities supporting various key data processing in the context of data engineering initiatives, which include general data integration or data migration scenarios.

D&A governance: Data-quality capabilities supporting the data governance initiative and its associated key roles (such as chief data and analytics officers [CDAOs] and data stewards) with a focus on increasing the value of data assets while managing risks and compliance.

Master data management (MDM): Data-quality capabilities supporting various key master data domains in the context of MDM initiatives and the deployment of custom or packaged MDM solutions.

Operational/transactional data quality: Data-quality capabilities support control over the quality of data created by, maintained by, and housed in operational/transactional applications, including Internet of Things (IoT) systems.

Report 2026

Here is a summary of the vendors featured in the Gartner magic quadrant 2026 report.
For the full analysis and detailed insights, you can read the report here and view the magic quadrant graphic here.

Market Status	Market Vendor
Leader	IBM
Leader	Salesforce (Informatica)
Leader	Qlik
Leader	Ab Initio Software
Leader	Ataccama
Visionary	DQLabs
Niche Player	Irion
Niche Player	CluedIn
Niche Player	Anomalo
Niche Player	Soda
Niche Player	Acceldata
Challenger	Precisely
Challenger	Experian

Report 2025

Here is a summary of the vendors featured in the Gartner magic quadrant 2025 report.
For the full analysis and detailed insights, you can read the report here and view the magic quadrant graphic here.

Market Status	Market Vendor
Leader	Informatica
Leader	Qlik
Leader	Ataccama
Visionary	DQLabs
Niche Player	SAS
Niche Player	Irion
Niche Player	CluedIn
Niche Player	Anomalo
Challenger	IBM
Challenger	Ab Initio
Challenger	Experian
Challenger	Precisely