3 Steps to AI Ready Industrial Data

Here are three key steps to modernizing your industrial data architecture for AI readiness.

‍

𝐒𝐭𝐞𝐩 1: 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐯𝐢𝐭𝐲 & 𝐓𝐫𝐚𝐧𝐬𝐥𝐚𝐭𝐢𝐨𝐧

‍

You can’t analyze what you can’t access.

Manufacturing environments are deeply heterogeneous, a patchwork of machines, protocols, and data formats.

Just “connecting” isn’t enough. Imagine trying to train a predictive model on Machine A and reuse it on Machine B, only to discover their outputs are incompatible.

So the first step is to normalize communication, translate unlike data sources into a unified language.

However, It’s not just about getting the data, it’s about capturing the structure behind it.

‍

𝐒𝐭𝐞𝐩 2: 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚, 𝐃𝐨𝐧’𝐭 𝐅𝐥𝐚𝐭𝐭𝐞𝐧 𝐈𝐭

‍

Too many systems treat machine data as a flat list of points.

That’s a mistake.

Machines are systems of relationships; temperature sensors tied to motors, motors tied to production stages, and so on.

If your data flattens those relationships, you lose the context that makes AI useful.

You need to adopt hierarchical structures, often using nested JSON payloads, to capture those relationships.

This enables:

⇨ Roll-ups and aggregations

⇨ Reusability of subcomponents across machines

⇨ Better alignment with programming principles like composition

The result?

⇨ A structured digital representation of the asset, not just a raw dump of values.

‍

𝐒𝐭𝐞𝐩 3: 𝐀𝐝𝐝 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐰𝐢𝐭𝐡 𝐂𝐫𝐨𝐬𝐬-𝐃𝐨𝐦𝐚𝐢𝐧 𝐑𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩𝐬

‍

Now, here’s where it gets interesting, and where many stop too soon.

Data relationships aren't just vertical (i.e., machine => component => sensor).

They’re also orthogonal, machines relate to jobs, operators, suppliers, and even ambient temperature.

Let’s say a machine operates differently depending on the job it’s performing. The job itself becomes a contextual data source, defining limits, tolerances, and outputs that change how the machine behaves.

Or consider environmental conditions. A spike in energy consumption might not make sense unless you also know the temperature on the plant floor at that moment.

Modern architectures need to support these non-hierarchical relationships, moving from a static namespace to a dynamic graph of context.