Most manufacturers have made real progress on data modernization. Cloud platforms are in place, data models are cleaner, and self-service analytics is gaining traction. In many cases, there’s even an AI roadmap starting to take shape.
And yet, the most operationally valuable data in the enterprise—sensor readings, machine states, quality signals, line throughput—is still locked away in systems that the broader business can’t access or use.
That’s not a technology problem. It’s a strategic one.
The Parallel Worlds of IT and OT
Operational technology has always lived in a different world. Systems like OSIsoft PI System, Ignition, and AVEVA were designed for reliability, and real-time control.
But they weren’t built for cross-functional analytics, enterprise-wide visibility, or AI-driven decisioning.
In other words, IT and OT have been operating as parallel universes. For a long time, that separation worked. Today, it’s become a serious constraint.
Where the Real Competitive Edge Is Emerging
The manufacturers pulling ahead aren’t just running better ERP analytics or cleaner financial reporting. They’re correlating machine behavior with yield loss, predicting failures before they cascade, and connecting what happens at 2:00 in the morning on Line 4 to what shows up in customer complaints weeks later.
That mindset shift requires OT data—at scale, in context, alongside enterprise data.
Why This Is Finally Becoming Possible
The idea at the heart of this change isn’t a new one. What’s changed is the feasibility.
Platforms like Snowflake have made it significantly easier to bridge the gap between OT and IT without disrupting existing systems.
This isn’t because Snowflake replaces your historian—it doesn’t—but because it does give OT data somewhere to land that the rest of the business can actually use. Connectors to OSIsoft, time-series handling, and the broader ecosystem of OT-to-cloud pipelines have matured enough that this is no longer a research project.
With these same modern connectors, time-series support, and an expanding ecosystem of OT-to-cloud pipelines, manufacturers can now:
- Ingest and contextualize historian data
- Combine it with enterprise and external data sources
- Make it accessible to analysts, data scientists, and AI systems
What used to require heavy, brittle integration work is now achievable through more flexible, scalable patterns.
The Question That Matters
If you’re leading data, analytics, or digital transformation in manufacturing, there’s a simple question worth asking: Can your data platform explain what’s happening on your factory floor and connect it to business outcomes?
If the honest answer is no, it might be your biggest gap. Not your data catalog, your governance framework, or your GenAI pilot.
Closing the Gap
The factory floor generates some of the most valuable, high-frequency, and actionable data in your organization. It’s time to treat it like the asset it is instead of an isolated operational exhaust.
The next wave of manufacturing leaders will be defined by how well they connect what happens in production to what happens in the business and how quickly they can act on it.
If you’re exploring how to bring OT data into your broader data and AI strategy or struggling to unlock value from your existing historian investments, you don’t have to do it alone.
Reach out to our industry data experts to start the conversation today.
