A temperature reading from the bearing on Compressor 2 taken during a production run, two hours before an unplanned shutdown — that's not just data. That's knowledge. The signal is the same as any other temperature reading. What changes is the surrounding context: what asset it belongs to, what process was running, what else was happening at the time.
Most factories can't make that connection. Not because they lack data — they have years of historian records, hundreds of sensors, OPC tags for everything from conveyor speeds to coolant temperatures. What they're missing is the layer that makes data mean something: the context that tells you which signals belong to which assets, which processes those assets are part of, and which conditions tend to precede which outcomes.
More data doesn't close that gap. Context does.
Volume creates noise. Context creates signal.
When you have a lot of data without context, the instinct is to add more — more reports, more alerts, more to try and understand. But volume without structure doesn't reduce uncertainty. It adds to it. Every new source is another thing to reconcile, another number someone questions, another spreadsheet someone maintains manually.
Context works differently. When you know which assets are connected to which processes, which signals are upstream of which outcomes — you stop searching through the volume. You already know where to look.
Building context happens bit by bit
The mistake is treating contextualisation as something that has to happen once, up front, before you can get value from the data. By the time that project finishes — asset registers mapped, hierarchies agreed, relationships documented — the urgency has moved on and the map is already out of date.
The approach that works is narrower. Pick a question — why did Compressor 2 go down on the morning shift? Use Thred to pull the relevant signals together, map the assets involved, and connect the relationships that matter for that question. You get an answer. And those relationships stay in the knowledge graph — a living map of how your factory connects — so the next question starts from a better position.
Context accumulates. It doesn't have to be complete to be useful — it just has to be relevant to what you're trying to know right now. That's the shift: from data that sits there to data that answers questions.
Start with one question. See how fast context builds. →