How to Audit Your PI System: A Technical Checklist for PI Admins

Industrial organizations rely on the PI System to run operations, investigate incidents, and optimize performance. Engineers trust historian data to understand what is happening in the plant and why.

But over time, even well-maintained PI environments accumulate hidden problems:

• Tags that silently stop updating
• AF attributes pointing to stale signals
• Calculations running with incorrect inputs
• Dashboards referencing deprecated assets
• Compression settings quietly distorting data

These issues rarely appear immediately. They emerge gradually as systems evolve and infrastructure changes.

When they finally surface, the symptoms often look like operational problems:

• Operators see conflicting values across dashboards
• Engineers cannot reproduce KPI calculations
• Analytics fail without clear explanation
• Troubleshooting takes hours instead of minutes

A PI System audit helps identify these issues before they impact operations. This guide outlines a practical audit checklist used by PI administrators to maintain operational data reliability.

Quick Self-Check: Does Your PI System Need an Audit?

Before diving into the checklist, ask yourself a few questions:

• Do you know which tags stopped updating in the last 24 hours?
• Can you quickly identify which dashboards depend on a specific tag?
• Do you know how many AF analyses are currently failing?
• Can you detect compression settings that may be dropping important signal variation?
• Do you know which tags are unused but still consuming license capacity?

If these questions are difficult to answer quickly, your PI environment likely contains hidden reliability risks.

Many teams only discover these issues when dashboards break or engineers begin questioning the data.

What a PI System Audit Actually Examines

A comprehensive PI audit examines reliability across the entire operational data pipeline.

Problems can occur at any layer:

• Interface failures creating data gaps
• Stale tags in the archive
• Broken AF calculations
• Dashboards referencing outdated signals

An effective audit examines each layer of this pipeline to ensure the historian remains trustworthy.

PI System Audit Checklist

1. Audit Interface Health

Interfaces and connectors are the entry point for historian data.

Verify that all configured interfaces are operating correctly.

Check for:

• Interfaces that have stopped sending updates
• Communication latency between interfaces and the Data Archive
• Data gaps caused by temporary outages
• Interfaces still configured but no longer connected to live equipment

Inactive or misconfigured interfaces can silently create blind spots in operational data.

2. Identify Dead or Abandoned Tags

Large PI environments often accumulate tags that are no longer useful.

Common examples include:

Flatlined tags

Signals that have not updated in days or weeks, often caused by sensor or interface failures.

For detailed guidance on detecting stale tags, see: How to Detect Stale or Flatlined PI Tags in the PI System

Unused tags

Tags that exist in the archive but are never referenced in Asset Framework or dashboards.

Ghost tags

Tags originally created for assets that have since been removed or renamed.

Beyond clutter, these tags consume system resources and license capacity.

3. Review Compression and Exception Settings

Compression settings directly affect the reliability of historical data.

During an audit, review tags that have:

Overly aggressive compression

Important signal variation may be dropped, reducing historical accuracy.

Minimal compression

Too little compression can significantly increase storage load and degrade system performance.

Compression issues often remain invisible until engineers attempt detailed analysis.

4. Audit Asset Framework Structure

PI Asset Framework provides the contextual layer that organizes operational data.

An AF audit should confirm that:

• Templates follow consistent modeling practices
• Asset hierarchies reflect the real plant structure
• Attributes reference valid source tags
• Asset models remain synchronized with equipment changes

Outdated or inconsistent AF models are a common source of confusion for engineers.

For comprehensive AF design strategies, see: PI Asset Framework Best Practices: Designing Reliable Asset Models in the PI System

5. Audit AF Calculation Health

AF analyses generate many of the derived values used in dashboards and reports.

During an audit, check for:

Broken calculations

Analyses that are failing or producing invalid outputs.

Performance bottlenecks

Calculations consuming excessive CPU or memory.

Orphaned calculations

Analyses referencing tags that have been deleted or moved.

Circular dependencies

Calculations referencing each other, creating evaluation loops.

Broken analyses can silently propagate incorrect values across dashboards and reports.

6. Audit Archive and Storage Health

The PI Data Archive requires ongoing monitoring to maintain reliability.

Key areas to review include:

• Archive growth trends and storage projections
• Available disk capacity on archive servers
• Archive fragmentation and shifting policies
• Backup integrity and restore validation

Storage issues often develop gradually and can affect historian performance if left unchecked.

7. Review Calculation Documentation

Operational knowledge often becomes embedded inside AF analyses.

Without documentation, calculations can become difficult to maintain or troubleshoot.

Check whether analyses include:

• Descriptions explaining their purpose
• Documented inputs and outputs
• Defined units and assumptions
• Comments explaining business logic

Strong documentation improves maintainability and supports knowledge transfer.

8. Audit PI Vision Displays

Dashboards frequently reveal issues deeper in the data pipeline.

During an audit, review displays for:

• References to stale or missing tags
• Deprecated assets
• Calculations producing unexpected values

Operators often rely on these dashboards for operational decisions, making display integrity critical.

9. Review Security and Access Controls

Security audits ensure the PI environment aligns with organizational policies.

Review:

• User access permissions
• Active Directory mappings
• Orphaned users or outdated accounts
• Configuration change permissions

Maintaining clear access control is especially important in regulated industries.

10. Trace Tag Dependencies

One of the most overlooked aspects of a PI audit is understanding how data flows across the system.

For any given tag, teams should be able to identify:

• Which AF attributes reference it
• Which calculations depend on it
• Which dashboards display it
• Which users rely on it

Without this visibility, retiring or modifying a tag can introduce unexpected failures elsewhere in the system.

For comprehensive tag management strategies, see: PI Tag Governance: Best Practices for Naming, Managing, and Cleaning Up PI Tags

The Reality of Auditing Large PI Systems

In small PI deployments, audits can often be performed manually.

But large industrial environments frequently contain:

• Hundreds of thousands or millions of tags
• Tens of thousands of AF elements and attributes
• Thousands of calculations and dashboards
• Multiple interfaces and data pipelines

Running a complete audit manually can take days or even weeks.

Even then, hidden issues can remain unnoticed:

• A calculation buried deep in Asset Framework
• A dashboard referencing deprecated signals
• Compression settings degrading data resolution

This is why many organizations are shifting from periodic audits to continuous reliability monitoring.

Automating PI System Audits with Osprey

Rather than relying on periodic manual reviews, many PI teams are adopting continuous auditing tools.

Osprey provides automated visibility across the PI ecosystem, allowing teams to detect reliability issues early.

Learn more about Osprey: Osprey - PI System Data Observability Platform

Capabilities include:

• Monitoring interface health and signal freshness
• Identifying stale or unused tags
• Analyzing compression configuration across the archive
• Scanning AF models and calculations for broken references
• Mapping dependencies between tags, analyses, and dashboards
• Tracking configuration changes across the PI environment

Instead of discovering problems when users report incorrect data, teams gain continuous insight into the health of their operational data systems.

PI System Audit Checklist (Summary)

A complete PI audit typically includes:

• Auditing interface health and data ingestion
• Identifying stale or unused tags
• Reviewing compression settings
• Validating Asset Framework structure
• Detecting broken AF analyses
• Monitoring archive growth and storage
• Verifying dashboard integrity
• Reviewing security and access controls
• Tracing tag dependencies across the system

Maintaining Trust in Operational Data

A PI System audit is not simply a technical exercise. It is an essential step in maintaining trust in operational data infrastructure.

When historian data becomes unreliable, engineers lose confidence in dashboards, analytics, and automated decisions.

Regular audits help ensure the PI System remains a dependable foundation for operational insight.

By combining disciplined audit practices with automated monitoring, organizations can maintain reliable PI environments as their systems grow and evolve.