Litmus Data Catalog is generally available today. Industrial metadata visibility, lineage, and governance for teams that need to find, trust, and scale AI across sites.
Industrial companies have invested heavily in OT-to-IT connectivity, dashboards, historians, cloud pipelines, and AI pilots. Machines are connected. Data is moving. Analytics are running. Yet one problem continues to slow down scale: teams still struggle to find, understand, and trust the data behind the outcome. A KPI may not match what operators see on the plant floor. A dashboard trend may look wrong, but no one can quickly trace the source. An AI model may produce an insight, but teams still question whether the underlying data is complete, current, and properly contextualized. In many industrial environments, data exists everywhere, but visibility into metadata, lineage, ownership, and business meaning remains fragmented.
Manufacturers are not struggling because they lack data. They are struggling because data environments have grown faster than visibility and governance. Across plants and enterprise operations, data now flows through PLCs, SCADA systems, historians, edge platforms, cloud systems, analytics tools, business applications, and AI workflows. But as the number of systems grows, the ability to understand and govern metadata often lags behind.
Data infrastructure exists and dashboards are operational, but confidence in the underlying data foundation remains fragile. Without a strong metadata layer, industrial teams spend too much time asking basic questions such as:
Where did this data come from?
How does it flow across systems?
What does this KPI actually mean?
Who owns this asset?
What downstream systems are affected if something changes?
Can this data be trusted for analytics, reporting, or AI?
Litmus Data Catalog is built to answer those questions faster and with more confidence.
Litmus Data Catalog first became available in a private preview, where industrial teams used it to catalog metadata across plant and enterprise systems and told us where the gaps were. That feedback shaped the release available today: broader source coverage, deeper governance controls, lineage down to the device and tag level, drift root-cause analysis, adoption analytics, and an AI layer that answers questions across the whole catalog rather than one asset at a time.
Those capabilities sit on top of the data infrastructure Litmus already provides. Litmus Edge standardizes and contextualizes OT data at the source. Litmus Unify governs how that data moves across the enterprise through a Unified Namespace. Litmus Data Catalog records what the data means, where it came from, and who is accountable for it, so the same foundation that makes industrial data available also makes it explainable.
Litmus Data Catalog is a centralized industrial metadata cloud-hosted platform powered by AI. It is the metadata visibility and governance layer of the industrial data architecture. It automatically discovers and documents metadata across the manufacturing landscape and creates a unified metadata layer across OT and IT systems. Instead of focusing only on moving industrial data from one system to another, Litmus Data Catalog helps teams understand the metadata that defines how that data is used across analytics, KPIs, reporting, and AI.
Industrial data passes through several stages before it reaches a dashboard or a model. It has to be connected and contextualized at the source, managed across distributed sites, published and governed in motion, and then understood well enough to trust. Litmus Edge, Litmus Edge Manager, Litmus Unify, and Litmus Data Catalog each cover one of those stages.
Litmus Edge connects machines and industrial systems, standardizes OT data, contextualizes information, runs applications and analytics at the edge, and integrates trusted data with cloud and enterprise systems. It is the operational data foundation at the source.
Litmus Edge Manager provides centralized control across distributed edge environments. It helps manufacturers onboard devices, monitor fleets, manage software updates, deploy applications, govern data models, and control AI model rollout across sites.
Litmus Unify governs real-time OT-to-IT data exchange through a structured Unified Namespace. It standardizes how industrial data is published, organized, secured, and consumed across the enterprise through a governed real-time data layer.
Litmus Data Catalog adds the metadata visibility and governance layer. It helps industrial teams discover metadata assets, understand lineage, standardize business language, assign ownership, and improve trust in the data used across analytics and AI.
Together, the products create a connected and governed operating model. This combination helps manufacturers reduce fragmentation between data creation, data movement, operational management, and enterprise level governance.
In practical terms, Litmus Data Catalog helps manufacturers:
Find datasets, tags, and topics faster
Understand where metadata originated and how it flows
Standardize KPI language and industrial terminology
Organize metadata into scalable business-aligned domains
Classify critical assets by business relevance
Enrich metadata with AI-driven context
Govern metadata quality and drift over time
The result is a stronger foundation for trusted analytics, better operational alignment, and more scalable Industrial AI. These are the persistent industrial data challenges it is designed to address.
Limited lineage visibility: Many teams can see reports and dashboards, but they cannot easily trace the path behind the result. When lineage is unclear, troubleshooting takes longer, impact analysis becomes harder, and trust in analytics declines.[RK1]
Inconsistent terminology across teams: Operations, engineering, IT, and business teams often define the same metric or asset differently. That creates ambiguity in dashboards, reports, and enterprise decision-making.
Low discoverability of important data assets: As data volumes grow, valuable metadata becomes harder to find. Teams duplicate work, miss governed assets, or depend on tribal knowledge instead of a shared system of record.
Weak ownership and stewardship: When nobody clearly owns an asset, governance becomes reactive. Critical datasets may be used widely without accountability for quality, change control, or documentation.
Poor AI readiness: AI needs trustworthy, explainable, and well-governed data. If metadata lacks context, lineage, and governance, AI initiatives struggle to scale with confidence.
Litmus Data Catalog brings six major capabilities together in one solution.
This is the metadata ingestion layer of the catalog. It pulls metadata from SCADA and HMI systems, historians, PLCs, OPC servers, CNC and RTU controllers, edge gateways, message brokers, and IIoT platforms, and extends across the IT side to cloud warehouses, lakehouses, and ERP and business systems. Network asset discovery helps teams identify sources they had not cataloged yet, scheduled sync keeps metadata current across every connected source, and offline ingestion covers plants and segments that are air-gapped or intermittently connected.
This helps users quickly find the right data asset and understand what it is. Teams can search by keyword, filter results, browse by category or domain, and view asset profiles that make metadata easier to explore and interpret. A home dashboard gives each user a starting point into the catalog, saved views keep frequent searches and filter sets reusable, asset documentation captures the context that usually lives in someone’s head, and bulk metadata management lets stewards update ownership, labels, and descriptions across many assets at once instead of one at a time.
This is a graphical flow representation that shows how data is connected across systems from source to downstream use. It helps users understand where a dataset came from, what it depends on, and what could be affected if something changes. Lineage now resolves down to individual devices and tags, so a controls engineer can follow a single signal from the PLC through the edge layer and into the dashboard or model that consumes it. Impact analysis turns that view into a pre-change check: before a tag is renamed or a source is reconfigured, teams can see which pipelines, reports, and consumers are downstream.
This is the control layer that makes metadata manageable and accountable. It defines ownership, standard terminology, permissions, and classification so teams can govern industrial data assets consistently. Hierarchical data domains let governance follow the way the business is actually organized, by site, area, line, or function, and domain-level visibility control keeps each team working with the metadata that belongs to it. Custom policies and granular privileges support stewardship models that differ by plant or region, an audit log and activity trail record who changed what and when, and deprecation status marks assets that should no longer be used before they end up in a new report.
This capability helps teams monitor whether metadata is staying accurate as systems evolve. It detects schema drift and tracks metadata changes over time, so issues can be spotted early before they create confusion in analytics, reporting, or AI workflows. A schema health overview shows where the environment stands at a glance, and drift root-cause analysis points to the change behind a break instead of leaving teams to reconstruct it. Usage metrics, asset insights, search analytics, and data landscape insights add the other half of the picture: which assets teams rely on, what people are searching for and not finding, and where coverage is still thin. Catalog adoption analytics tracks how governance is spreading across sites and teams.
This adds an intelligence layer on top of the catalog so users can interact with metadata more naturally. It improves search, generates summaries, and provides contextual assistance, making it easier for both technical and business users to understand data without manually decoding every asset. Questions can now be asked across the whole catalog rather than one asset at a time, and answers are written to be usable by business teams, not only by data engineers. AI surfaces relationships and dependencies between assets that are not obvious from a single profile view, and applies the same interpretation to governance itself, highlighting gaps in ownership, classification, and documentation that need attention.
Litmus Data Catalog is especially relevant for industrial organizations that have already invested in digital infrastructure but still struggle with trust, explainability, and governance. This includes teams responsible for:
Industrial data and analytics teams
Industrial AI initiatives teams
Governance and compliance teams
Digital transformation program teams
Enterprise architecture teams
If a manufacturer already has connectivity, dashboards, cloud pipelines, or AI pilots in place, Litmus Data Catalog becomes the layer that helps bring more visibility and trust to the foundation behind them.
Litmus Data Catalog is generally available today. Book a demo at litmus.io/get-started to see it against your own systems, or read the product documentation at docs.litmus.io to review connectivity, governance, and lineage in detail.
Litmus Data Catalog is a centralized industrial metadata visibility and governance solution powered by AI. It helps manufacturers discover, understand, standardize, classify, and govern metadata across OT and IT environments.
It helps industrial teams solve common data trust problems such as unclear lineage, inconsistent KPI definitions, weak ownership, poor discoverability, and limited governance. This makes industrial data easier to trust and use for analytics, reporting, and AI.
It improves trust by exposing metadata lineage down to the device and tag level, standardizing terminology, organizing assets into hierarchical domains, classifying critical metadata, enriching context with AI, and adding governance controls, audit trails, and drift monitoring that reduce inconsistency over time.
Litmus Edge connects industrial systems, contextualizes OT data, runs applications and analytics at the edge, and integrates data with cloud and enterprise systems. Litmus Data Catalog focuses on the metadata layer, making industrial data easier to discover, understand, and govern.
Industrial AI depends on trusted, explainable, and well-governed data. Litmus Data Catalog supports AI readiness by making metadata easier to find, standardize, interpret, and govern across plants, systems, and enterprise workflows, and by letting teams ask questions of the catalog in natural language.
It is well suited for industrial data teams, digital transformation leaders, OT and IT teams, governance stakeholders, and organizations building analytics or Industrial AI programs across multiple sites.