Acceldata & ServiceNow tie data quality to AI workflows

Acceldata has partnered with ServiceNow to integrate its data quality monitoring with the ServiceNow Data Catalog, within ServiceNow's Workflow Data Fabric ecosystem.

The integration is intended to help businesses using AI agents and automated workflows determine whether data is reliable enough for operational use. It also feeds incident context from Acceldata into ServiceNow, allowing teams to investigate and fix problems within existing workflow tools.

At the centre of the arrangement is a link between Acceldata's data observability software and the ServiceNow Data Catalog, previously known as data.world. Through that connection, data quality and reliability scores from Acceldata are synchronised with catalogued data assets, giving users a view of data condition alongside business metadata and governance processes.

Business and technical teams can see which data sets meet internal standards and which have been flagged by policies or rules. They can also move from the catalog to more detailed diagnostics in Acceldata when they need to inspect the source of a problem.

The partnership targets a common issue for companies connecting data from cloud platforms and on-premise systems into broader workflow and AI environments. If a single data source contains errors, gaps or other reliability issues, the effects can spread through multiple business processes and make the root cause harder to identify.

In practice, the companies are pitching the integration as a way to give organisations more confidence in the data used by AI systems. On ServiceNow's platform, AI agents can consume data assets that have been tagged and approved for use, with the aim of keeping those systems aligned with business quality thresholds.

Data oversight

Acceldata already connects to a range of modern data platforms that feed into wider workflow systems. Its software monitors data health, quality and reliability across those environments, and the new integration extends that visibility into ServiceNow's catalog and workflow layer.

The arrangement is also designed to support incident handling. When Acceldata detects an underlying data reliability problem, it can pass relevant context into ServiceNow so support and operations teams have more information when a case is raised.

The companies said this should reduce unnecessary ticket volumes and improve visibility into downstream business effects. In organisations where data supports customer service, internal operations, finance or supply chain processes, a clearer view of impact may help teams prioritise issues more quickly.

Rohit Choudhary, Founder and Chief Executive Officer of Acceldata, framed the partnership around trust in operational and AI-driven systems. "Enterprises are moving quickly to connect data, workflows, and AI, but trusted action depends on trusted data," he said. "By partnering with ServiceNow as part of its Workflow Data Fabric ecosystem, we can help customers bring greater visibility into the health and trustworthiness of their data, understand downstream business impact, and accelerate resolution with richer operational context."

ServiceNow positioned the deal as part of a broader effort to connect data across enterprise systems while attaching business context. That context is becoming more important as companies seek to use AI in routine work without introducing avoidable errors from poor source data.

Pramod Mahadevan, Vice President of Data & Analytics Product Ecosystem at ServiceNow, said the company sees data quality as central to workflow automation. "Workflow Data Fabric is designed to help organizations connect data across systems, add business context, and power more intelligent workflows - but that only works when you can trust the data moving through it," he said. "Working with Acceldata brings proactive data observability and richer incident context directly into ServiceNow Data Catalog, so issues are spotted sooner, downstream business impact is understood, and problems are resolved faster - not just by people, but by AI agents acting autonomously. Enterprises need that operational confidence to run AI and agentic workflows at scale."

AI scrutiny

The announcement comes as companies face growing pressure to tighten controls around the data used in AI systems. Many businesses have spent years building analytics and workflow tools on fragmented data estates, often spanning legacy databases, cloud warehouses and third-party applications. As AI tools are layered on top, weaknesses in data quality become more visible and potentially more consequential.

ServiceNow has been expanding its ecosystem around workflow orchestration, data management and AI-led automation, while partners such as Acceldata seek to place specialist monitoring tools into those environments. For software suppliers, that creates an opening to frame data reliability not as a back-office technical issue, but as an operational risk that can interrupt business processes if left unresolved.

For enterprise customers, the practical question is whether tighter links between observability and workflow systems can shorten the time between identifying a data problem and fixing it. The integration is intended to bring data quality signals into the tools where teams already work, rather than requiring separate monitoring and response processes.

As organisations continue to combine data, workflows and AI across multiple systems, the ability to judge whether information is fit for use is becoming a more visible part of technology operations. The integration between Acceldata and ServiceNow is aimed at making that judgement easier to surface within the workflow itself.