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Core Concepts

Before diving into configuration and features, it's important to understand a few core ideas that guide how Osprey works. These concepts form the foundation of Osprey's approach to automated data quality monitoring and troubleshooting.

Data Lineage

How Osprey tracks data flow between assets

Data lineage is the map of how information flows through your system, from sensors to calculations, dashboards, and reports. In Osprey, lineage helps you understand:

  • Which assets depend on others

  • Where a data issue may have originated

  • What downstream systems might be affected by a configuration change or data issue

Osprey automatically builds a directed graph of relationships between assets, such as:

  • PI Tag ➜ AF Attribute ➜ PI Vision Display

  • Flow Rate Sensor ➜ Totalizer ➜ PI Vision Display

Lineage allows you to answer questions like:

"If this sensor is flatlining, which calculations or displays are no longer reliable?"

Viewing lineage / tag genealogy

Blast Radius

Understanding downstream impact of data issues

The blast radius represents the full extent of an issue's impact, not just where it happened, but everywhere it spreads.

For example, a stale flow sensor may feed into:

  • An asset totalizer

  • A PI Vision trend

  • A regulatory report

Osprey automatically calculates the blast radius of each data quality issue using the lineage graph, so you can:

  • Quantify the scope of bad data

  • Prioritize which issues to fix first

  • Notify the right downstream users

You'll see blast radius information attached to each issue and summarized in dashboards.

Prioritizing on Blast Radius

Data Quality

How Osprey defines and evaluates it

At its core, data quality is about trust. Osprey evaluates the quality of your data based on:

  • Timeliness - Is the data arriving at expected intervals?

  • Freshness - Has the value updated recently?

  • Compression - Is there data loss due to compression?

Osprey runs automated checks on each asset to assess these aspects. Failed checks become issues, and can trigger alerts or visual indicators.

Data quality isn't just binary, it's contextual. You can tailor checks based on the asset type, its business importance, or regulatory needs.

Checks

Common check types include:

Check Type Description
Freshness Has the value updated within the expected interval?
Bad Data Does the tag or attribute have a current status of Bad?
PercentGood Does the tag have a bad value during the scan period?
No Data Is the tag or attribute missing data?
Value is Pt Created Is the value of the tag Pt Created
Value is Configure Is the value of the tag Configure
Duplicate Tag Does the tag have the same point source, location1, and instrument tag fields as another tag
Reference Tag does not exist Does the AF Element Attribute reference a non-existent tag
Overcompression Is the tag's CompDevPercent > 1% and span > 100
Undercompression Is the tag's Compressing turned off
Contains Bad Data Does a display contain bad data

Osprey handles scheduling, evaluation, and issue generation, all automatically.

Domains

Segmentation of responsibility, ownership, and scope

Domains let you segment your environment into logical groups of assets, often by site, department, or system.

Each domain in Osprey has:

  • Its own set of assets

  • Separate ownership and permissions

  • Configurable scanners, checks, workflows, and labels

Domains make it easy to:

  • Delegate responsibility (e.g., one team per plant)

  • Enforce access control

  • Customize quality policies per group

Think of a domain as a workspace: it's where you scan assets, configure checks, and monitor data quality, all scoped to your area of ownership.