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How to manage data quality?

This article describes how you may use ValveTrack to support your data quality process for critical valve data

Updated over 2 weeks ago

Business context

ValveTrack underpins safe and reliable execution of critical valve operations. When an operator prepares to close a valve, they must have accurate, trusted information—such as nominal input torque, direction to open, direction to operate, and other key parameters. Without this, there is a risk of over‑torquing the valve, which can ultimately lead to failure.

The process described here provides a structured way to verify and mark these critical settings as quality‑assured, ensuring a robust interface between all personnel involved in operating manual valves.

Critical settings

Some data is now, based on discussions in the community, characterized as more critical then other data. The data set is outlined in the Overview:

There is also an indication whether the critical settings are quality assured or not.

Quality assuring critical settings for valves valve

Who can do this?

You need to have the "Valve quality control" role to change the QC status of a valve. Read more about this in this article.

Changing the QC status for one valve

You can easily change the QC status from Draft to Approved:

You then have to add a mandatory comment:

When this is submitted all critical settings are set to read-only and the history for the valve is updated:

And yes, you can change the QC status back to draft - this will also be logged in History.

Changing the QC status for many valves

The same process can be done for several valves - in the example below a subset of valves is selected and Set QC Status is executed:

The process is then executed for the subset:

QC status in the APIs

The QC status is reflected in the APIS: Swagger UI

This can be used to easily visualize the QZ status in other applications, PowerBI, digital twins etc

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