Data Governance | Litmus Industrial Data Foundation for AI

DATA GOVERNANCE
Standardize, Govern, and Trust Industrial Data
Define policies, standardize meaning, and create visibility across data so teams can find, trust, and reuse it across systems, analytics, and Industrial AI.

95 global sites deployed in 18 weeks

When data isn’t governed, it can’t be trusted or reused
As industrial data spreads across sites, systems, and teams, it becomes harder to interpret and control. Without governance, teams interpret the same data differently, policy enforcement becomes inconsistent, lineage is unclear, and accountability breaks down. Data may be available—but it isn’t trusted, reusable, or scalable.

The layer that makes data reusable across teams and systems
Data Governance creates structure, visibility, and control so industrial data can be consistently understood, governed, and used across the enterprise. By enforcing policy, organizing metadata, and establishing ownership, it enables teams to confidently use data across systems, sites, and use cases—without duplication or conflicting interpretations.

Proven at scale across industrial environments
From multi-site deployments to measurable operational gains, Litmus helps manufacturers turn fragmented data into scalable execution.

95 sites deployed in 18 weeks
standardized industrial data architecture
$3M/month savings
optimized packing and volume tracking
126 edge deployments
standardized industrial data architecture
Reduced plant downtime
Real-time monitoring and corrective action

Data governance capabilities
Define, control, and organize industrial data so it can be trusted, discovered, and reused across systems and teams.

Policy & Control
Control how data flows, is retained, and is shared
Governance begins with policy. Defining how data moves, where it can go, and how it is used creates a disciplined operating model for industrial data.

What this enables:

Enforce rules for data flow, routing, and destination control

Apply retention, masking, and approval-based data policies

Govern how data is shared across systems and teams

Create auditable, policy-driven data operations
Metadata Catalog
Create a searchable, structured view of data assets
As data volumes grow, teams need visibility into what exists and what can be trusted. A metadata catalog organizes assets, tags, models, and data products into a unified, searchable layer.

What this enables:

Catalog assets, tags, models, and data products

Improve visibility across operational data sources

Reduce duplication through governed reuse

Make trusted data easier to discover for analytics and AI
Data Lineage
Understand where data comes from and how it’s used
Data trust depends on traceability. Lineage provides visibility into how data is created, transformed, and consumed across systems.

What this enables:

Track data origins across OT and enterprise sources

Map transformations across pipelines, models, and applications

Show how data is consumed across analytics and AI workflows

Improve trust and enable faster impact analysis through traceability
BUSINESS GLOSSARY
Standardize definitions across OT, IT, and business
Governance requires consistent, shared interpretation. A business glossary ensures metrics, assets, and processes are understood the same way across teams.

What this enables:

Define standardized business and technical terminology

Align interpretation across systems and teams

Reduce ambiguity in analytics and reporting

Improve cross-functional collaboration
OWNERSHIP & DOCUMENTATION
Assign responsibility for every critical data asset
Data becomes more reliable when ownership is clear. Governance defines responsibility and preserves context for long-term use.

What this enables:

Assign ownership across data assets and systems

Maintain documentation for operational and analytical use

Improve onboarding across teams and use cases

Strengthen accountability and long-term data trust

Why data governance matters

A strong edge intelligence layer enables:

Standardized meaning across plants, systems, and teams
Policy-driven control of data movement and usage
Improved trust through metadata visibility and lineage
Reduced ambiguity in analytics and reporting
Stronger accountability through ownership and documentation
A scalable foundation for analytics, automation, and Industrial AI

Built for legacy and modern industrial systems
Litmus integrates with the industrial stack you already rely on, including PLCs, SCADA platforms, historians, MES, cloud infrastructure, and analytics tools.

FAQs
Quick answers to your industrial data questions.

Make industrial data easier to trust and reuse at scale
See how Litmus helps you govern data across systems and Industrial AI.

Standardize, Govern, and Trust Industrial Data

95 global sites deployed in 18 weeks

When data isn’t governed, it can’t be trusted or reused

The layer that makes data reusable across teams and systems

Proven at scale across industrial environments

95 sites deployed in 18 weeks

$3M/month savings

126 edge deployments

Reduced plant downtime

Data governance capabilities

Control how data flows, is retained, and is shared

Create a searchable, structured view of data assets

Understand where data comes from and how it’s used

Standardize definitions across OT, IT, and business

Assign responsibility for every critical data asset

Why data governance matters

Built for legacy and modern industrial systems

FAQs

What is Data Governance in an industrial data platform?

Why is policy and control important for industrial data?

What is the role of a metadata catalog in data governance?

Why does data lineage matter in industrial environments?

How does a business glossary improve governance?

Why are ownership and documentation important for data trust?

Make industrial data easier to trust and reuse at scale