PI is no longer just a historian. It feeds operations, dashboards, reliability programs, analytics, enterprise data platforms, and AI initiatives. The quality and reliability of PI data now determines whether those investments deliver real value.
Operators rely on PI Vision displays for real-time decisions. Stale or incorrect data drives bad decisions at the worst moments.
Reports, dashboards, and KPIs pulled from PI are only as good as the tags and AF models underneath them.
AI initiatives built on unreliable historian data fail. Resiliency is the prerequisite, not an afterthought.
Most PI environments have accumulated years of technical debt that is invisible until something breaks. These are the most common failure modes Tycho surfaces.
Thousands of tags with no active data, no owner, and no record of whether anything depends on them.
Analyses referencing missing, renamed, or invalid tags silently producing bad results across asset hierarchies.
Displays showing bad values, missing data, or crashing because of expensive calculations or broken upstream references.
Multiple tags measuring the same signal with no record of which is canonical, used, or safe to remove.
Tag configurations, AF expressions, and templates changed without visibility into what broke downstream.
No record of who owns a tag, what it measures, or what depends on it. Institutional knowledge walks out the door.
Tag health, stale values, bad data, missing data, compression issues, usage, ownership, and naming consistency.
Template consistency, broken analyses, invalid tag references, expression changes, and calculation reliability.
Display-level calculations, symbol dependencies, stale references, performance issues, and operator-visible bad values.
Interface health, data flow gaps, upstream source failures, and connector reliability.
Track changes across tags, AF objects, templates, and calculations so every modification is traceable.
Security configuration consistency, access patterns, and configuration drift across sites and systems.
Most PI health checks produce a report that sits on a shelf. Tycho is ongoing observability: a continuous view of what is healthy, what is degrading, and what changed. PI teams move from reactive firefighting to proactive management of their data infrastructure.
Surface existing issues across tags, AF, displays, interfaces, and change history. Build a clear picture of what is broken and what is at risk.
Not all findings matter equally. Focus cleanup and remediation on issues with the highest operational impact, downstream dependencies, and cleanup risk.
Maintain ongoing visibility so new issues are caught early, changes are tracked, and the PI estate stays reliable as it evolves.
Each page covers a distinct area of PI data resiliency in depth.
Find unused, stale, duplicate, and orphaned tags. Understand blast radius before cleanup. Reduce cost and complexity safely.
Know who changed what in PI and what it affected. Traceable changes across tags, AF objects, and calculations.
Stop guessing why PI Vision displays are slow or wrong. Trace display values back to source and fix the right thing first.
Bring order to the AF wild west. Find broken analyses, enforce template consistency, and make AF usable for enterprise analytics.
AI will not fix unreliable PI data. It will amplify it. Build the reliable foundation that AI initiatives actually require.
Turn PI technical debt into a prioritized modernization roadmap. Assess the full PI operating layer and know what to fix first.
Find out where your PI estate has reliability gaps, what is at risk, and what to prioritize. A focused assessment that surfaces the issues that matter most.
Tycho Data helps teams ensure their operational data is reliable, accessible, and actionable.