Guide
The ultimate guide to managing your PI System Download now
Find unused, duplicate, and low-value tags while preserving the data paths that operations, engineering, and reporting still rely on.
THE CHALLENGE
Large PI environments accumulate years of tags from projects, migrations, temporary work, retired equipment, and changing standards.
Many tags appear unused but still feed calculations, Asset Framework attributes, PI Vision displays, reports, or downstream applications.
Without usage and dependency visibility, teams either keep everything or take unnecessary risk by deleting the wrong tags.
1
Inventory
Build a complete view of PI tags and their configuration.
2
Understand usage
See which tags feed AF, analyses, PI Vision, reports, and downstream systems.
3
Prioritize
Identify unused, duplicate, nonstandard, or low-value tags for review.
4
Validate before retirement
Check downstream dependencies before changing or removing tags.
THE SOLUTION
Rationalize tags with usage and dependency context
Osprey combines tag inventory, OT lineage, configuration history, and data integrity information to help teams decide what should stay, change, or retire.
Teams can see how each tag is used before making a decision.
This turns tag cleanup from a manual review into a structured engineering workflow.
USAGE ANALYSIS
See which tags are actually being used
Understand where tags are referenced across the PI System and downstream applications.
What this enables:
Find tags used in AF
Find tags used by analyses
See PI Vision usage
Identify downstream consumers
DEPENDENCY ANALYSIS
Know what can break before retiring a tag
Trace downstream dependencies before a tag is renamed, replaced, or removed.
What this enables:
Find affected calculations
Identify impacted displays
See downstream applications
Reduce cleanup risk
TAG HEALTH & QUALITY
Use signal behavior to improve cleanup decisions
Combine usage with data health information to identify tags that may no longer provide useful operational information.
What this enables:
Find stale and flatlined tags
Identify tags with no recent data
Review poor-quality signals
Prioritize tags for investigation
STANDARDS & GOVERNANCE
Find tags that no longer follow your standards
Identify inconsistent names, metadata, units, and configurations across large PI environments.
What this enables:
Check naming conventions
Find missing metadata
Identify inconsistent configurations
Support standardization programs
Common Use Cases
Where Tag Rationalization helps
PI System cleanup
Reduce accumulated tags and configuration debt.
Migration preparation
Move only the tags that still matter.
License and infrastructure optimization
Reduce unnecessary historian footprint.
Standards enforcement
Find tags that do not meet current engineering standards.
Modernization programs
Create a cleaner foundation for AF, cloud, analytics, and AI.
Frequently Asked Questions
Get answers to common questions here
GET STARTED