Companies are not slowing down their investment in artificial intelligence. According to Gartner®’s 2026 survey of CIOs and technology executives, 81% of organizations plan to increase funding for traditional AI and 84% for generative AI. Yet, again according to Gartner, on average only about 40% of AI prototypes make it into production. Among the main barriers cited by organizations are data availability and quality.
This is a problem that has been known for years. The interesting question is a different one: if the diagnosis is shared, why does the roadblock keep reappearing so systematically? The answer that emerges from Gartner’s Market Overview for Data Quality Solutions, published in July 2026, has less to do with the technologies available and more to do with the point in the data lifecycle at which quality is verified.
A market growing on a specific need
The Data Quality solutions market reached $2.5 billion in revenue in 2026, a 6.5% increase over 2025, and is forecast to reach $3 billion by 2029. It is not explosive growth, but it is steady, and above all it is driven by a very concrete demand: having reliable data on which to train and feed models.
The figure that best qualifies the priority, however, is a different one. Over 64% of data management leaders place quality and governance among the top five areas of investment for the next two to three years. Data quality has stopped being a technical line item within an integration project and has become a standalone budget line, discussed at the executive level.
Three ongoing shifts
The document describes a market reorganizing itself along three lines.Il documento descrive un mercato che si sta riorganizzando lungo tre direttrici.
From correction to prevention. This is the most significant shift. The traditional model intervenes downstream, when data has already entered systems and has already had an impact. The market is moving toward a proactive approach based on data contracts: formal agreements, expressed in natural language, between data producers and consumers, which explicitly define schemas, quality thresholds, and service levels. Applied at the time of ingestion, these contracts allow pipelines to automatically reject non-compliant data before it reaches analytics and AI models.
From automation to agentic. The use of generative AI and conversational assistants for profiling, anomaly detection, and rule generation is now considered a baseline capability for mainstream vendors. The frontier has moved: Gartner describes vendors integrating agents that can reason, identify issues, and autonomously carry out remediation. An enabling role is attributed to the Model Context Protocol, an open standard that allows agents from different platforms to collaborate, share context, and trigger workflows across hybrid environments.
Toward observability. The line separating Data Quality from data observability is growing thinner, with DQ vendors natively integrating pipeline monitoring and automatic anomaly detection. At the same time, controls are migrating into the infrastructure: on one side into orchestration tools, which act as a defensive safeguard in CI/CD pipelines, and on the other into cloud platforms and data warehouses, which perform validation and normalization directly where the data resides, with advantages in scale and cost.
Around these three lines, the document points to others: growing attention to unstructured data, driven by the spread of RAG applications; the democratization of data quality toward non-technical business users through natural-language querying; and the dilemma between buying commercial solutions and building agent-based capabilities in-house.
A grid you can use during selection
The most practical part of the Market Overview is the distinction between mandatory and optional market capabilities. It is a grid that can be taken almost directly into a scouting phase or used to build an RFP.
Among the mandatory capabilities: broad connectivity to heterogeneous sources, continuous profiling and monitoring, rule discovery and lifecycle management, alerting and visualization, and transformations for parsing, cleansing, and standardization.
Among the optional ones, which in practice make the competitive difference: agentic capabilities with the related guardrails, support for unstructured data, matching and reconciliation, issue resolution workflows, low-code interfaces, deployment flexibility across on-premises, cloud, and iPaaS, scalability to volumes in the hundreds of millions of records through push-down execution, and security and role-based visibility management.
The full list, with a detailed description of each capability, is in the complete document.
Irion in the Market Overview
Irion is included among the vendors cited in Gartner®’s Market Overview for Data Quality Solutions, within a selection that the document explicitly declares to be representative and not exhaustive. It is the only vendor on the list headquartered in Italy.
What follows is an Irion editorial reading and is not attributable to Gartner.
Several of the directions the document identifies as market evolution correspond to capabilities that Irion Premium Data Quality & Governance® offers today: rule generation from natural-language descriptions, agentic capabilities applied to profiling and control definition, and push-down execution of controls directly in the environments where the data resides. To this is added a theme that the document treats as a deployment requirement and that, for many European organizations, is also a compliance matter: the ability to choose where and how to run controls, with development carried out entirely in Italy and therefore within European jurisdiction.
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Gartner, Market Overview for Data Quality Solutions, Divya Radhakrishnan, Amy Bickel, 1 July 2026.
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