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Give your data a shared
language you can trust

Model your operation once and every dashboard, report and model downstream reads it the same way, so nobody has to argue about whose number is right.

You cannot build on data that carries no context

Undocumented
Topics published by teams who moved on, vendors who left no documentation, devices nobody can identify. Most estates cannot produce an inventory of what they emit.
Inconsistent
Payloads drift, units differ, and the same asset is named three ways across three sites. Every report needs translating before anyone can read it.
Ungoverned
No owner, no standard and no enforcement, so a change made on the floor breaks a dashboard three departments away and nobody finds out for a week.

Contextualize gives your data a structure everything else can use

Discovery & cataloging

Detect what is actually publishing, infer structure from real payloads, and surface it for a person to classify and promote into the model.

So you can

Find data across sites without asking the engineer who built it, so improvement candidates get investigated instead of abandoned.

Semantic modeling & UNS

Model sites, areas, lines, assets and signals against the ISA-95 hierarchy. Define a structure once, version it, apply it anywhere.

So you can

Turn a tag into an asset with a definition attached, so analysts stop reverse-engineering what a signal means before they use it.

Enterprise data governance

Set the standard centrally: which models are approved, who owns what, and who is allowed to change it, with role-based control.

So you can

Make the same asset mean the same thing at every plant, so cross-site comparison becomes arithmetic rather than a project.

Streaming data governance

Enforce that standard on data in motion, at the moment of publication, with schema validation and classification of every datapoint.

So you can

Apply context at the source rather than after ingestion, so modeled data lands in the lakehouse and raw exhaust does not.

What this changes

Site two costs a fraction of site one

Models are defined once and reused, instead of rebuilt from scratch for every new plant or project.

Reports that compare across plants

Dashboards speak the same language site to site, instead of needing manual translation before anyone can read them.

AI use cases that reach production

Models run on data that already carries its context and its provenance, so they clear review instead of stalling in it.

A number people will defend

When the standard is enforced at the source, the argument about whose figure is right stops happening.

See it in the platform

Screenshot of the HiveMQ Platform Contextualize workspace, showing the Unified Namespace node tree with sites, areas, and assets.
The comprehensive data gathered through HiveMQ provides a fertile ground for developing and deploying a multitude of AI-driven use cases.

Gopalakrishnan Rajaram

Solution Architect, Ford

Your data has a shared language. Next, turn it into intelligence.

Step 1

Connect

Move operational data reliably across OT, IT, edge and cloud.

Revisit Connect →
You are here

Contextualize

Give every signal shared meaning and a governed structure.

Step 3

Analyze

Turn trusted data into operational intelligence.

Continue to Analyze →
Step 4

Act

Put that intelligence to work in governed workflows.

Start with one operational improvement. Scale the results.

Start with the outcome that matters most. Prove value on the data you already have, then scale the pattern.