SPONSORED BY: Matrikon
On October 7th, we’re hosting an industry event that brings together the leaders setting OT data strategy, the architects designing it, and the engineers building it. In this newsletter, we’re digging deeper into what to expect from the forum, sharing details from a recent client win, and spotlighting a co-authored article with Orilla on the data foundation behind scalable industrial AI.
Industrial Data Forward Forum: Enterprise Data. Open Automation. AI-Ready.
Wednesday, October 7, 2026 | 8:00 AM – 5:00 PM CT
Norris Conference Center: Houston/CityCentre
816 Town & Country Blvd., Houston, TX 77024 (I-10 & Beltway 8)
The Industrial Data Forward Forum brings together the people setting OT data strategy and the people building it. Across the day, we’ll work through reducing integration rework, architecting for the enterprise, federating without fragmenting, and connecting OT and IT securely across brownfield environments.
What we’re tackling
- Reduce Integration Rework: how a shared OT data foundation eliminates repeated connections and duplicated engineering effort across projects.
- Architect for the Enterprise: how a unified data layer connects OT data from the edge to enterprise systems, without every application building its own path to get it.
- Federate Without Fragmenting: what governed, resilient access across distributed OT sources actually requires, and why most environments don’t have it yet.
- Connect OT and IT Securely: how to build secure connectivity across segmented, firewall-restricted brownfield environments.
Bring your own OT data problem
This is not a day of sitting through product presentations. Share an architecture question, integration constraint, governance challenge, or brownfield reality your organization is working through. Compare approaches with peers. Talk directly with Matrikon specialists. See the technology operating in practice.
Matrikon and EMI Online Webinar
Don’t miss this opportunity to learn how Matrikon OPC Tunneller and Redundancy Broker can help strengthen your OPC infrastructure and provide a more reliable and secure communication environment.
Reserve your free seat today!
Date: Wednesday, September 29th, 2026
Time: 12:00 PM – 1:00 PM (KSA Time) | 1:00 PM – 2:00 PM (UAE Time)
New White Paper: The OT Data Foundation Behind Scalable Industrial AI
Industrial AI rarely fails because the models aren’t capable enough. More often, it fails because the data underneath them was never designed to be shared.
In a new white paper, Orilla CEO Lindi Salasko and Matrikon Marketing Director Darek Kominek examine why fragmented OT systems keep promising pilots from surviving contact with production — and outline the two-layer architecture that addresses it: a governed data layer that makes OT data consistently accessible across the enterprise, and an edge AI capability that acts on that data within the latency and reliability constraints operational environments require.
Why OPC UA Matters Here
An OPC UA process value carries its own context: the units it’s measured in, the asset it belongs to, and its quality status. Systems consuming that data don’t need prior knowledge of the device or application that produced it. The data describes itself.
That property is what removes the per-integration translation work that makes OT data architectures expensive to build and brittle to maintain, and it’s why OPC UA serves as the semantic foundation for the Unified OT Data Layer.
Case in Point:
A maintenance team wants to build a predictive model across a fleet of similar assets at four sites. The same physical measurement is named differently, stored in different formats, and accessed through a different interface at each location. Before any modeling begins, the team spends months reconciling data. With a governed UODL in place, that reconciliation work is already done at the architectural level.
Case Study: Real-Time Operator Visibility at Houston Metro
Houston Metro operates 45 light rail stations across one of the largest transit systems in the United States. Its SCADA system tracked every train in real time, but crew scheduling data lived on a separate enterprise network with no connection between the two. Assembling a full operational picture meant manual lookups, phone calls, and days of research.
Matrikon Data Broker created a secure, outbound-only OPC UA connection between the networks without opening inbound firewall ports or disrupting existing infrastructure. Rail controllers now right-click any train icon and see the operator’s name immediately.