Ataccama & ServiceNow surface data quality in catalog

Ataccama has partnered with ServiceNow to display its data quality signals in ServiceNow Data Catalog, placing data quality information alongside catalogued data assets.

The integration is aimed at users and AI agents selecting data inside ServiceNow workflows, where Ataccama's assessments will appear before that data is used in a model, process or business decision.

Under the arrangement, quality scores, findings and observability signals from Ataccama will be shown on individual data assets in ServiceNow's catalogue. This is intended to give teams a direct view of whether a dataset is suitable for use at the point of discovery, rather than after problems emerge downstream.

Those signals include a colour-coded quality tier badge, issue findings showing what failed and why, and observability alerts such as schema changes and volume anomalies. ServiceNow users reviewing an asset will be able to see those indicators in the same interface where they browse and select data.

Point of use

The partnership reflects a broader issue for companies rolling out artificial intelligence in production environments. Many businesses have focused on workflow automation and model deployment, but data quality checks often remain separate from the systems where staff and software agents actually choose and use data.

Ataccama and ServiceNow are positioning the integration as a way to close that gap. By surfacing quality information in the catalogue itself, they aim to reduce the risk that teams rely on flawed, incomplete or recently changed data without realising it.

The integration also brings data quality and observability information together in one place. Users can see not only whether a dataset has failed a quality check, but also whether something about it has changed, such as its structure or volume, which could affect whether it still needs to be validated before use.

ServiceNow's catalogue will also add context around Ataccama findings, including lineage, downstream impact and business criticality. That is intended to help data teams assess which issues matter most and where a problem may be originating.

Shared view

One aim is to give data stewards, engineers and business users access to the same information. In many organisations, different teams rely on separate systems for monitoring data quality and making operational decisions, which can lead to disputes over whether a dataset is reliable.

Bringing those signals into ServiceNow could simplify that process for companies already using the platform as a central interface for workflows and internal services. It also points to a broader shift as data governance tools are tied more closely to software used by business teams and automated agents.

Ataccama described the move as an early example of a data trust layer being brought into agent-based platforms. The phrasing reflects the growing focus across the technology sector on AI agents that can trigger actions, make recommendations or execute parts of workflows with less human intervention.

If those agents rely on poor data, the consequences can spread quickly across automated processes. The companies are therefore seeking to place quality checks where both people and software agents can see them before any action is taken.

Jay Limburn, chief product officer at Ataccama, outlined what he said customers are facing as AI projects move beyond experimentation.

"Enterprises are building AI on the assumption that their data is good enough, and that assumption is where initiatives often fail," said Jay Limburn, Chief Product Officer, Ataccama. "By surfacing Ataccama's quality signals directly on data assets inside ServiceNow, we're replacing that assumption with evidence at the exact moment a team reaches for data to power an AI workflow or drive a decision. When high-quality data is the default condition, AI initiatives can drive meaningful impact because they are consistently powered by data that is verified, fit for purpose, and trusted."

ServiceNow framed the partnership around a similar concern as more customers use AI in operational settings.

"AI and agentic workflows are only as reliable as the data moving through them. As our customers use ServiceNow as the AI control tower for business reinvention, trusted data isn't a nice-to-have; it's the foundation everything else is built on," said Pramod Mahadevan, VP of Data & Analytics Product Ecosystem, ServiceNow. "With Ataccama's data health signals surfaced directly on assets in the ServiceNow Data Catalog, agents and users have the visibility they need to know their data is ready before it powers an agent, a workflow, or a critical business decision. That's how our customers move from experimenting with AI to running it with confidence at scale."