Overview
Introduction to Osprey
Welcome to Introduction to Osprey. In this training module, we'll explore something every industrial organization depends on -- data you can trust. By the end of this section, you will understand what governance, trust, and visibility mean in the context of operational data. You'll see how these concepts address common pain points such as stale tags, broken AF references, and missing lineage. You'll also learn how Osprey delivers these outcomes through features like lineage, audit trails, and automated data quality checks.
The Bigger Picture
Every industrial plant, factory, or utility runs on data. Yet most teams can't answer three simple but critical questions:
- Where did this number come from?
- Has it changed recently?
- And can I trust it?
These questions determine whether you can rely on your systems to make operational decisions. Governance, trust, and visibility are not abstract ideals -- they are how you keep your PI System and the processes it supports from silently eroding in reliability over time.
Governance
Governance means having clear ownership, traceability, and control over your data landscape. In Osprey, governance ensures that you know who owns each tag, calculation, and display. It allows you to trace every change back to its source and apply standard checks that enforce data quality before bad data spreads downstream.
Without governance, organizations face inconsistent calculations, changes made without proper notification for other teams, and finger-pointing when something breaks. With governance, you create accountability and repeatability -- the foundation for trust. Governance is what separates data chaos from a managed, auditable system.
The audit trail records who changed what, when, and why. No more mystery changes or invisible errors. When people can see what changed and why, they can act quickly, correct root causes, and make better decisions. That's how you build a culture of trust -- not just a long list of issues.
Trust
Trust is the confidence that what you're seeing is real, accurate, and current. Osprey builds that confidence through automation and transparency. Automated data quality checks surface issues before they become costly. Lineage graphs, overlaid with trust indicators, show how every display, calculation, and tag connects -- revealing dependencies and the cascading effects of poor data that most teams can't see.
Visibility
Visibility turns raw metadata into actionable insight. Dashboards in Osprey provide a clear picture of system health across your PI environment -- from Vision displays to AF calculations to interface nodes. Visibility shifts your approach from reacting to problems to proactively managing your data infrastructure.
With visibility, stale tags and hidden calculation errors can no longer stay buried. Everything becomes discoverable and explainable. When people can see their entire data landscape, they can finally control it.
Bringing It All Together
Governance gives you control.
Trust gives you confidence.
Visibility gives you awareness.
Together, they form the foundation for reliable, auditable, and explainable data -- the kind engineers, analysts, and regulators can all depend on. In the next sections, we'll see how Osprey brings these principles to life through dashboards, assets, and workflows that make governance, trust, and visibility not just concepts, but daily practice.
Knowledge Check
Question 1
What is the main purpose of governance in the PI data environment?
A. Increase data collection rate
B. Ensure ownership, standards, and traceability
C. Optimize compression settings
D. Reduce historian storage cost
Question 2
What is the primary benefit of lineage tracking in Osprey?
A. It speeds up data transfer between systems
B. It identifies relationships and dependencies between assets and tags
C. It replaces manual AF model building
D. It allows editing tags directly from Osprey
Question 3
How does an audit trail strengthen trust?
A. By recording every change and enabling traceability for accountability
B. By preventing all changes
C. By reducing tag count in AF
D. By recalculating averages automatically
Question 4
Why is data quality monitoring critical?
A. It ensures consistent refresh intervals
B. It detects stale, missing, or corrupted data before decisions rely on it
C. It tracks user logins
D. It improves network throughput