Industrial data needs an engineering question.
Start with the maintenance or operating decision you need to make. Then establish whether the available historian, condition or work-order data can support it.
Define the decision before choosing a dashboard.
What equipment or failure mode is in scope? Which signals and records exist, and can they be related to the same asset? A useful first task is to establish data quality, coverage and the decision the analysis should support.
We scope analysis and reporting alongside industrial engineering work. Predictive models, dashboards and scheduling interfaces are distinct tasks, each with its own inputs and validation requirements.
Keep the pilot measurable.
- Identify the asset tags, source systems and available history.
- Agree how missing values, changes and event records will be treated.
- Define a baseline and evaluation criteria before assessing a model.
- Name the person who validates the finding against operations.
- Separate a technical finding from an approved maintenance or production action.
This page does not promise a percentage improvement in downtime or labour cost. A result needs a defined baseline, validation and evidence from the actual operation.
Connect findings to the plant information.
Consistent equipment references matter in both datasets and drawings. Where analysis depends on the asset register, SLD or control documentation, include those interfaces in the scope.
Put the scope on the table.
Tell us which documents need to change, which inputs are available and when your team needs to review them.
Scope your project