Data Quality Solutions

Data Quality Solutions is the old name. Gartner's active scorecard is the Magic Quadrant for Augmented Data Quality Solutions, whose February 2026 edition, published February 11, 2026, evaluated thirteen vendors, and the added word is the story: data quality stopped being a hygiene discipline and became AI infrastructure, with a prediction attached. By 2027, Gartner expects 70 percent of organizations to adopt modern data quality solutions to support AI adoption.

The word that changed the category

Data quality used to be the discipline of catching bad records: deduplication, validation, the rules that kept the warehouse honest. It was bought by the data governance team and funded reluctantly.

Augmented data quality is the same discipline with an AI assistant embedded in every step: identifying quality issues automatically, offering context-aware suggestions, and automating the workflows from detection to remediation. The word augmented records a change in the buyer too. Data quality is now purchased as AI readiness, because a model trained on bad data does not know the data is bad, and the models are now the whole point.

Data quality was hygiene. Augmented made it infrastructure.

The February 2026 Magic Quadrant for Augmented Data Quality Solutions, and its three Leaders

The edition published February 11, 2026, authored by Sue Waite, Divya Radhakrishnan, and Amy Bickel, evaluating thirteen vendors.

Three Leaders are confirmed. IBM, leading with its watsonx.data intelligence approach, cited for AI-driven automation scaling data quality workflows, support for structured and unstructured data, integrated governance, and hybrid and multicloud scale. Qlik, for the seventh consecutive year, the Talend lineage now part of its quality and governance stack. And Ataccama, for the fifth consecutive time, positioned furthest on Completeness of Vision, with its ONE Agentic platform integrating data quality, observability, cataloging, governance, and lineage in one architecture.

The field is thin at the top and the streaks are long, which is what a consolidating data quality market looks like.

The digital data steward

Ataccama's placement carries the edition's most specific innovation: an embedded ONE AI Agent that acts as a digital data steward, automating rule creation and accelerating workflows from detection to remediation.

The data steward is a real job, and it has been the market's bottleneck for decades: the human who decides what a quality rule should be, who owns the exception, and what to do about the bad record. Automating that job is the category's inflection point, because the steward's decisions were the last part of data quality that did not scale.

The steward is the first role the agents are automating in this market, and the quadrant rewarded the vendor that shipped it.

What AI does to data quality economics

The 70 percent prediction is the market's economics in one number, and the mechanism deserves precision.

AI adoption raises the cost of bad data. A dashboard built on dirty records misleads a handful of analysts. A model trained on dirty records misleads every customer it serves, at machine speed, with the errors embedded in the weights rather than the reports. The organizations adopting AI are discovering that the quality layer is no longer overhead; it is the cost of entry.

The same mechanism changes the vendor's job. Quality tools now have to understand unstructured data, because the models consume documents, images, and conversation transcripts alongside clean tables, and the Leaders' citations, IBM's structured and unstructured support, Ataccama's observability integration, are the market's answer.

The rename's honest cost

The pasted name carries a real cost for the buyer: every placement earned under the old criteria is re-contextualized by the rename.

The criteria changed when the word was added. A vendor that led the data quality market on rule-based profiling is being re-scored on AI-assisted detection, automated remediation, and agentic stewardship, and the old placements do not transfer cleanly. The streaks, Qlik's seventh year, Ataccama's fifth, are genuinely remarkable because they span the rename, but the buyer should read them as evidence of adaptation, not continuity of the old scorecard.

The rename re-priced the criteria, and the old name's reputation is now part of the market's history, not its scorecard.

Four questions for the data quality buyer

Is the steward agentic or assistive? The edition's frontier is automation of the steward role. Ask what the AI decides on its own, what it escalates, and what the audit trail shows.

Does it handle the data your models actually eat? Structured and unstructured, documents, transcripts, and the streaming sources. Ask for a demonstration against your real data types, not the vendor's demo set.

Is the quality layer wired to the AI lifecycle? The 70 percent prediction makes quality an AI-readiness purchase. Ask how the tool connects to model training, evaluation, and monitoring, not just to the warehouse.

Which scorecard are you actually reading? The pasted name is the old one. Check every vendor claim against the augmented edition's date and criteria, because the market renamed itself under the buyer.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

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This market sits next to Data Governance Solutions, covered separately on this site. A decade of governance advocacy could not make this category matter. Generative AI did it in two years, because a badly governed data estate now produces a fluent, confidently wrong AI answer instead of a disagreement in a meeting.