Manufacturing’s Next Leap, Part 2: Getting Your Data Ready

In Part 1, we explored the shift from reactive decision-making to real-time interrogation of your data. We introduced the MCP Server and how it makes it possible to ask your plant questions and get answers instantly.

But those answers are only as good as the data foundation underneath them. If your data is scattered, misaligned, or buried in tribal knowledge, no AI system can help you make smart decisions. This post is about taking the next step to fix that.

Most Plants Have the Data, They’re Just Not Using It

You already have the signals.

• Sensors are wired
• PLCs are spitting out values
• SCADA, MES, and historians are logging everything

But none of it is truly contextual until it’s mapped, modeled, and made useful.

That’s where your data foundation comes in. It’s not about collecting more data. It’s about organizing what you already have so that it’s accessible, understandable, and usable both by humans and machines.

What a Strong Data Foundation Looks Like

We often refer to three key layers to this foundation. While we’ve been applying these for years, we’re grateful to voices like Walker Reynolds who have helped simplify this into frameworks that the broader community can rally around.

Here’s how we interpret it in practice.

1.Connect

Create a structured, real-time digital representation of your plant using a Unified Namespace (UNS).

Model your systems using ISA-95 (adjusted to your business needs):
Enterprise → Site → Area → Line → Cell → Equipment.

The UNS becomes a single source of truth, linking PLCs, historians, MES, and context layers into one organized hierarchy. No more searching across systems or dealing with naming inconsistencies.

2. Contextualize

Collect more than just values, collect meaning.

You don’t just want to know a valve opened. You want to know:

• During what batch?
• On which product?
• At what step in the process?
• Who was the operator?
• Was it part of a startup or a cleaning cycle?

This is how you turn data into contextualized events. Context gives raw signals business value.

3. Store Smart

Storing data isn’t about dumping it into a database. It’s about storing with purpose.

Ask yourself:

• Can it be queried by the MCP Server?
• Can it power real-time dashboards and AI?
• Can a human read it, and can a model explain it?

You don’t need a massive data lake. You need data that’s organized, accessible, and aligned to the questions you want to ask.

Why Most AI Initiatives Fall Flat

Here’s what we see too often:

• Great AI model
• Clear use case
• No context in the data

The result? The AI guesses. Or worse, it’s accurate but not useful.

Without proper context, models can detect anomalies but can’t tell you why they matter. Dashboards built on weak foundations become noise machines. They look impressive, but they’re blind to the big picture, delivering little to no value.

The Foundation Is the Strategy

Even with proven ROI and defined use cases, without a strong data foundation every project is at risk.

You burn time. You create technical debt. You erode trust.

That’s what happens when data pipelines are built project-by-project instead of being designed intentionally. The organizations that succeed long-term aren’t the ones with the flashiest dashboards. They’re the ones that start with a clear data strategy, not just at the enterprise level, but at the plant and line level too.

That strategy includes:

• A cross-functional steering committee to align business and operational priorities.
• A clear understanding of your current systems, silos, and data sources.
• Information and semantic modeling standards for assets, tags, and events.
• Governance processes for data quality, access, and lifecycle management.
• A commitment to open, vendor-neutral architecture (MQTT, UNS, OPC UA, open APIs).

When these are in place, something powerful happens: you stop building one-off solutions and start scaling them.

Moving from downtime analysis to energy optimization to predictive maintenance becomes incremental, not foundational, every time.

This is what Industrial DataOps enables. It’s not about building one good system, it’s about building a strategy where every system that comes next is easier, cheaper, and more valuable.

The foundation isn’t just infrastructure. It is the strategy.

Up Next

In Part 3, we’ll show how this foundation unlocks real-time, on-demand intelligence. We’ll explore how teams are transforming their operations by turning structured data into immediate decisions and answers that come to them.

Written by: Bruce Slusser