How To Build Industrial Data Context That Actually Works

Industrial operations are messy, diverse, and constantly evolving. So why are we still trying to squeeze them into rigid ontologies?

In theory, standard frameworks like ISA-95 offer a neat, one-size-fits-all approach to organizing factory data and building context.

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But here’s the problem: 𝐞𝐯𝐞𝐫𝐲 𝐟𝐚𝐜𝐭𝐨𝐫𝐲 𝐢𝐬 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭.

⇨ Different equipment
⇨ Different processes
⇨ Different teams
⇨ Different priorities

Rigid ontologies often don’t match this reality.

When we apply them blindly, we create limitations instead of unlocking value.

True context isn't delivered by a standard. It's built, iteratively, through curiosity and questioning.

You don’t start with a rigid ontology.

You start with:

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✅ What does this data mean here?
✅ Why does it matter to our operation?
✅ How do we use it to improve?

That’s how we build context, through meaning-making, not just metadata. It’s a process of learning.

And that learning needs to happen quickly, because the pace of change on the shop floor doesn’t wait for you to finish your data model.

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