Context is Your Competitive Edge
OT data without clarity is noise. OT data with meaning is power.
Matrikon Data Broker transforms unstructured, inconsistent plant-floor data into high-quality, analytics-ready inputs that your enterprise applications and teams can use to generate actionable intelligence.
From Raw Tags to Real-Time Insight
Before: Incomplete Data. Inconsistent Meaning.
OT data varies across sites, vendor systems, and assets. As it moves farther from its source, context is lost, making it harder to understand and slower to use. Teams spend hours cleaning fragmented datasets, feeding analytics systems with partial or unreliable inputs.
After: Structured Context That Speaks Your Language
Matrikon Data Broker preserves context for every data point it federates. Through OPC UA modeling and semantic mapping, it unifies disparate sources into cohesive, consumer-ready data views. Engineers, analysts, and applications get the complete picture without the manual prep, accelerating decisions and amplifying the value of every data source.
Meaning Makes It Matter
Context transforms raw OT data into clarity, turning millions of points into meaningful information your enterprise can actually use. This is the foundation that enables your applications to deliver smarter insights and executive-level visibility.
Raw Data Doesn’t Make the Cut
Your OT data should be useful from the moment it’s generated, not only after it’s been staged, cleaned, and reconciled.
Structured Data. Clear Insight.
When data lacks structure, it lacks value. Matrikon Data Broker leverages built-in OPC UA modeling to represent assets by function, complete with device types, units of measure, alarm states, and system hierarchies.
This level of context, closest to the data sources, replaces proprietary bolt-on modeling layers and delivers usable data to power automation, reporting, and analytics.
One Language. All Sources.
Field devices call the same variable by different names. Matrikon Data Broker abstracts diverse data sources into a common address space, maps items into standard models, and delivers contextualized data to consumers.
MDB removes the need for spreadsheet lookups, feeds a consistent, trusted stream into analytics and dashboards. It also maximizes sustainability by allowing data source changes without disrupting downstream systems.
Transformed As It’s Streamed
Why wait to cleanse, compute, or convert data when it can happen live? Beyond real-time contextualization, Matrikon Data Broker will enable inline Python scripting (coming in 2026), so you can also calculate averages, detect anomalies, and apply thresholds instantly.
This upcoming tool will reduce historian and to-cloud bloat, speed up time-to-insight, and eliminate the lag of post-processing workflows. It’s edge-side intelligence that scales with your needs.
Built-In Context
MDB dynamically maps raw OT data to custom data views based on OPC UA Companion Spec models. This eliminates manual context translation and works for both modern sensors and legacy sources, delivering values expressed in the semantics that matter to their consumers.
Analytics-Ready from the Start
Most systems rely on brittle ETL pipelines to convert OT data into something usable. MDB reduces that extra stage.
By embedding structure, tags, and inline logic at the source (coming in 2025), it provides data that’s immediately consumable — speeding up deployments, reducing error-prone handoffs, and keeping your teams focused on insights, not prep work.
Native Modeling
Standardized Context
Real-Time Logic
Automatic Enrichment
Zero-Staging
Structured Data. Clear Insight.
When data lacks structure, it lacks value. Matrikon Data Broker leverages built-in OPC UA modeling to represent assets by function, complete with device types, units of measure, alarm states, and system hierarchies.
This level of context, closest to the data sources, replaces proprietary bolt-on modeling layers and delivers usable data to power automation, reporting, and analytics.
One Language. All Sources.
Field devices call the same variable by different names. Matrikon Data Broker abstracts diverse data sources into a common address space, maps items into standard models, and delivers contextualized data to consumers.
MDB removes the need for spreadsheet lookups, feeds a consistent, trusted stream into analytics and dashboards. It also maximizes sustainability by allowing data source changes without disrupting downstream systems.
Transformed As It’s Streamed
Why wait to cleanse, compute, or convert data when it can happen live? Beyond real-time contextualization, Matrikon Data Broker will enable inline Python scripting (coming in 2026), so you can also calculate averages, detect anomalies, and apply thresholds instantly.
This upcoming tool will reduce historian and to-cloud bloat, speed up time-to-insight, and eliminate the lag of post-processing workflows. It’s edge-side intelligence that scales with your needs.
Built-In Context
MDB dynamically maps raw OT data to custom data views based on OPC UA Companion Spec models. This eliminates manual context translation and works for both modern sensors and legacy sources, delivering values expressed in the semantics that matter to their consumers.
Analytics-Ready from the Start
Most systems rely on brittle ETL pipelines to convert OT data into something usable. MDB reduces that extra stage.
By embedding structure, tags, and inline logic at the source (coming in 2025), it provides data that’s immediately consumable — speeding up deployments, reducing error-prone handoffs, and keeping your teams focused on insights, not prep work.
See How MDB Powers Context-Driven Operations
Matrikon helps your enterprise transform raw OT data into strategic outcomes by making the data meaningful and ready for any consuming system.
How It Works: Context Built Into Every Stream
Matrikon delivers intelligence at the source, so insight flows freely across your enterprise.
OPC UA Modeling
Enrich plant data with engineering units, device types, and harmonized real-world asset hierarchies.
Tag Normalization
Align variable tag names across vendors and sites using structured mapping logic.
Python-Based Transformation (2026)
Perform live calculations, format conversions, or quality filtering directly in-stream.
Companion Spec Loading
Dynamically apply standardized semantic models to raw signals for instant clarity.
Abstract Legacy Systems
Integrate Modbus, OPC DA, and other legacy data into modern workflows with enriched context.
Data Context in Action: Chemical Industry Example
When plants run equipment from multiple vendors, the resulting data often arrives with inconsistent tags, formats, and hierarchies. This mismatch slows down analysis, reporting, and troubleshooting. Matrikon Data Broker can help unify inputs by modeling assets, standardizing tags, and applying live scripting at the source.
Modeled
Hierarchies
Data Source Abstraction
Standardized
Tags
Zero-ETL
Readiness
Unlock the Full Potential of Your OT Data
Put your operational data to work securely, strategically, and at scale.