SANTA CLARA, CA / ACCESS Newswire / October 5, 2026 / Litmus, the Industrial Data Platform for AI, today announced the general availability of Litmus Data Catalog, an industrial metadata visibility and governance product that makes industrial data AI-ready. Data Catalog discovers and documents metadata across OT and IT systems, creating one layer where industrial teams can search, understand, classify, govern, and trace the data assets behind their analytics, KPIs, and AI. Data Catalog was previously available in private preview to existing Litmus customers.
Industrial companies have invested heavily in OT-to-IT connectivity, dashboards, historians, cloud pipelines, and AI pilots. Yet industrial teams still can't answer basic questions about the data underneath: what exists, where it came from, who owns it, and whether a KPI matches what operators see on the plant floor. The lack of context and governance is where analytics and AI stall. This gap is metadata, not connectivity.
Litmus Data Catalog closes that gap. With it, industrial teams can now trace a KPI back to the individual tag that produced it, apply consistent asset and metric definitions across sites, and see who is accountable for each data asset before it reaches a report or a model. Impact analysis shows what sits downstream of a tag or source before anyone makes a change, rather than after a dashboard goes wrong.
"Data governance is one of the biggest barriers to scaling industrial AI, particularly for organizations managing multiple sites with different naming conventions, data models and quality standards," said Jatinder Devgun, Senior Analyst at Verdantix. "Litmus Data Catalog provides a metadata layer covering discovery, lineage and governance across OT and IT systems, which are capabilities industrial organizations need if they are to give AI consistent, contextualized data."
Data Catalog complements the existing capabilities of the Litmus platform. Litmus Edge structures operational data at the source, and Litmus Unify governs how it moves across the enterprise. Litmus Data Catalog documents what it means-definitions, tag structure, equipment hierarchy, ownership, lineage, and change history-including capturing metadata from systems outside the Litmus stack. It reads only metadata, so operational data stays in place. It also complements an existing enterprise or cloud data catalog by supplying the OT-to-IT metadata layer that those tools aren't built to capture.
"Moving industrial data was only ever the first step. To scale AI, every plant has to explain and govern that data the same way," said Vatsal Shah, CEO of Litmus. "Data without metadata are numbers no one can explain. Industrial AI needs both, so Litmus delivers both from the same platform, for the same plant."
Litmus Data Catalog brings six capability areas together in one platform:
- Data Source Visibility: Metadata from PLCs, SCADA and HMI, historians, OPC servers, edge gateways, IIoT platforms, cloud warehouses and ERP systems.
- Discovery & Navigation: Unified search, filters and asset profiles across OT and IT metadata.
- End-to-End Lineage: PLC-to-cloud lineage down to the device and tag, with impact analysis before a change is made.
- Data Governance: Ownership, business glossaries, hierarchical domains, policies and a full audit trail.
- Data Quality & Observability: Schema drift detection with root-cause analysis.
- AI & Contextual Intelligence: Natural language search and platform-wide metadata Q&A, delivered through Litmus IQ.
Litmus Data Catalog is available today. Book a demo at https://litmus.io/get-started or review documentation at https://docs.litmus.io.
About Litmus
Litmus is the Industrial Data Platform for AI. Global manufacturers including Panasonic, Niagara Bottling, and Poclain Hydraulics run Litmus across their operations, supported by partnerships with Microsoft, Google Cloud, AWS, Snowflake, Databricks, and Dell. Litmus operates from Silicon Valley, Toronto, Munich, Tokyo, and Pune. Learn more at www.litmus.io.
Media contact:
Krystal Leung
Press Relations
krystal.leung@litmus.io
SOURCE: Litmus
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