Litmus has made Litmus Data Catalog generally available, adding a metadata and governance product to its industrial data platform. The company says the software "makes industrial data AI-ready" by documenting metadata from OT and IT systems in one searchable layer. It was previously in private preview for existing Litmus customers.
The catalog collects metadata from PLCs, SCADA and HMI, historians, OPC servers, edge gateways, IIoT platforms, cloud warehouses and ERP systems. Lineage runs from the PLC to the cloud, down to the device and tag. A team can follow a KPI back to the individual tag that produced it, and impact analysis shows what sits downstream of a tag or source before anyone changes it.
For manufacturing and automation engineers, that tracing addresses a familiar problem: a dashboard number that does not match what operators see on the plant floor. Litmus says that gap comes from missing metadata, not missing connectivity.
Governance features include ownership assignment, business glossaries, hierarchical domains, policies and a full audit trail. Schema drift detection comes with root-cause analysis. Natural language search and metadata Q&A run through Litmus IQ.
The product sits alongside Litmus Edge, which structures operational data at the source, and Litmus Unify, which governs how that data moves across the enterprise. The catalog documents definitions, tag structure, equipment hierarchy, ownership, lineage and change history, including for systems outside the Litmus stack. It reads only metadata, so operational data stays in place, and Litmus says it works in networks that never touch the internet. It is also meant to sit next to an existing enterprise or cloud data catalog, supplying OT metadata those tools are not built to capture.
"Data without metadata are numbers no one can explain," said Vatsal Shah, CEO of Litmus. Jatinder Devgun, senior analyst at Verdantix, called data governance "one of the biggest barriers to scaling industrial AI," particularly for organizations running multiple sites with different naming conventions and data models.
ARC Advisory Group, which covered the release, said a catalog alone will not settle these problems. Successful deployments also need clearly defined ownership, consistent asset models and continued involvement from operations and engineering staff, according to the firm.
