Augmented Data Quality Solutions

Augmented data quality solutions are transforming traditional data quality processes and the market, empowered by AI/GenAI to deliver trusted AI-ready data. This research helps data and analytics leaders understand emerging technologies and the vendor landscape to make better purchasing decisions.

Market Definition

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 abroad 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.

These packaged solutions help implement and support the practice of data quality assurance, mostly embedded as part of a broader data and analytics (D&A) strategy. Various existing and upcoming use cases include: Analytics, artificial intelligence and machine learning development, Data engineering, D&A governance, Master data management, Operational/transactional data quality

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