Distinctions
Conceptual distinctions that clarify why many industrial and regulated systems fail despite extensive data collection.
Most system failures are not caused by missing technology, but by blurred concepts.
The Distinctions section separates ideas that are often treated as interchangeable — and shows why that confusion leads to architectural weakness.
These articles clarify differences such as:
- logging versus audit trails
- events versus intervals
- reconstruction versus observation
- data availability versus trustworthiness
Each distinction sharpens how systems should be designed, validated, and evaluated.
These texts are intentionally precise. They are meant to be referenced, quoted, and reused when explaining why “having the data” is not the same as understanding or trusting it.
Why traceability fails even in systems with extensive logs and why auditability requires explicit decision context.
Why reconstructing context after the fact is not sufficient and how missing context undermines auditability and trust.
Why time intervals are not a technical detail but a formal commitment that defines meaning, responsibility and comparability.
Why log streams fail compliance and what true audit trails must guarantee in regulated industrial systems.
Compare MQTT audit trail solutions by identity, topic, outcome, decision reason, delivery proof, integrity scope, retention and reviewabi...