Your enterprise already knows why things go wrong. Your data systems don't.
Every operations meeting is full of causal language. None of it is modelled anywhere.
Managers reason in triggers. Warehouses store snapshots.
"Churn spiked after response times slipped."
"Revenue dipped when we changed pricing."
"Shipments missed SLA once inventory hit a threshold."
Every one of those is a causal claim with a temporal ordering and a threshold. Your warehouse holds none of that structure — it holds tables of current and historical state.
So the reasoning lives in people's heads, gets rebuilt in every meeting, and disappears when they leave.
What's missing from the model
| Concept | Stored in a warehouse? |
|---|---|
| Entities and their attributes | Yes |
| Historical values | Usually |
| Events — discrete state changes | Rarely, as first-class objects |
| Triggers — thresholds that matter | No |
| Causal edges — what preceded what | No |
| Lag — the delay between cause and effect | No |
Without the bottom four, "why" questions get answered by an analyst constructing the chain by hand, once, for one question.
Modelling causality explicitly
Represent events as typed nodes, triggers as predicates on them, and causal relationships as directed edges with an expected lag. Then a why question becomes a traversal with a proof rather than a narrative.
That has a sharp consequence: the system can distinguish "these two moved together" from "this one preceded that one by the modelled lag, consistently." The first is a coincidence generator. The second is something you can act on.
Give an agent that graph and it stops inventing plausible stories about your business, because the paths it can assert are the paths that exist.
The full breakdown — the event and causal modelling approach, DAG construction, and worked diagnostic examples — is here:
👉 Events, Triggers, and Causal Chains: The Hidden Logic in the Enterprise
Originally published at colrows.com/blogs/events-triggers-and-causal-chains-the-hidden-logic-in-the-enterprise
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