Why this step matters
Gather evidence under controlled conditions.
This is where the agreed approach meets the real asset, team, or application. Repeatability, operating context, safe boundaries, and complete field notes determine whether later interpretation is defensible.
Follow the planned method, record exceptions and limitations, and preserve enough context for another qualified person to understand the evidence.
Applied to Reliability Data Architecture for AI and Machine Learning
- Technical focus: Time alignment, units, naming, and operating-state context
- Where it applies: Mixed-vendor sensor fleets
- Expected evidence: Integration and context requirements
- Working principle: A reliable program using AI and Machine Learning begins with reliable context: the asset, time, operating state, measurement method, and maintenance history.
What happens in practice
- 01Prepare the context
Confirm the asset, people, records, operating state, and boundaries needed to address time alignment, units, naming, and operating-state context.
- 02Make the work traceable
Follow the planned method, record exceptions and limitations, and preserve enough context for another qualified person to understand the evidence.
- 03Confirm the handoff
Check that the result can support integration and context requirements and that unresolved uncertainty is visible.
A sample of how this step may unfold
A site is comparing technology options and wants the selected system to fit the asset, users, data workflow, and decision—not only a feature list. During execution, the team works in the context of mixed-vendor sensor fleets. It records operating conditions, method settings, unusual observations, and any departure from the plan so the evidence can be interpreted later without guesswork. The example closes with the principle that a reliable program using AI and Machine Learning begins with reliable context: the asset, time, operating state, measurement method, and maintenance history.
This is an educational example, not a description of a specific client engagement or a guaranteed result.Evidence to expect
What should be visible before moving on.
- Time-stamped observations or readings
- Operating state and measurement settings
- Exceptions from the planned method
- Photos, notes, or records that preserve context
Common mistake
What weakens this step.
Collecting technically precise information without recording the load, speed, configuration, environment, or limitation that gives it meaning.
What good looks like
- Another qualified person can understand how the evidence was collected
- Operating context is attached to the data
- Limitations are documented rather than hidden
Where this step ends
A clear record, a clear limit, and a clear next move.
A good closeout leaves the next person with a practical explanation of what was done, what the evidence supports, what remains uncertain, and what should happen next. For this capability, the working principle remains: A reliable program using AI and Machine Learning begins with reliable context: the asset, time, operating state, measurement method, and maintenance history.