Make a way to know the statistic of use of every PI TAG in Data Archive
When you have thousands of tags in your Data Archive, you need to know wich tags are beign used and witch don't. It would be very usefull to have some diagnostic that shows wich tags are being cosumed more and from wich client.
Guest
Sep 8, 2026
We maintain a strict naming convention of PLC Tag → Plant SCADA Variable → PI Tag → Downstream Systems so users can easily find signals across all systems.
The problem is that when a source Plant SCADA variable is renamed, there is currently no easy way to identify which downstream systems are consuming the associated PI tag. This makes impact assessments and change management extremely difficult.
A built-in tag usage audit showing which clients access a tag, how often it is accessed, and from where would allow us to identify impacted systems, notify owners, and coordinate changes safely.
Today, the only way to obtain this information is through workarounds such as piartool analysis or network packet inspection. In large environments this is time-consuming and unreliable.
Since PI already tracks client connections and activity, exposing tag-level consumption statistics would provide huge value and improve change management.
It's 2026, having a database without query level audit feature is not great.
We have customers with hundreds of thousands of tags. Being able to find tags that are using resources, particularly those that are calculation outputs, but are never used, would help greatly in maintaining optimum system performance.
We maintain a strict naming convention of PLC Tag → Plant SCADA Variable → PI Tag → Downstream Systems so users can easily find signals across all systems.
The problem is that when a source Plant SCADA variable is renamed, there is currently no easy way to identify which downstream systems are consuming the associated PI tag. This makes impact assessments and change management extremely difficult.
A built-in tag usage audit showing which clients access a tag, how often it is accessed, and from where would allow us to identify impacted systems, notify owners, and coordinate changes safely.
Today, the only way to obtain this information is through workarounds such as piartool analysis or network packet inspection. In large environments this is time-consuming and unreliable.
Since PI already tracks client connections and activity, exposing tag-level consumption statistics would provide huge value and improve change management.
It's 2026, having a database without query level audit feature is not great.
We have customers with hundreds of thousands of tags. Being able to find tags that are using resources, particularly those that are calculation outputs, but are never used, would help greatly in maintaining optimum system performance.