Data Observability in the PI System: The Four Pillars

Data observability is essential for ensuring the reliability, accuracy, and overall health of data within the PI System ecosystem, which includes PI System, PI Asset Framework (AF), and PI Vision. These systems capture and visualize massive amounts of operational data in real time, so maintaining observability is critical to supporting data integrity and enabling effective decision-making. The four pillars of data observability—metrics, metadata, tag usage, and logs—provide a structured approach to managing these data environments.

The Four Pillars of Data Observability for PI System

1. Data Quality Scores

Metrics are foundational for monitoring the health of data systems within the PI System, including AF and PI Vision. They provide quantitative indicators for evaluating performance, reliability, and data quality.

• Data Freshness: This metric assesses how timely the data is, ensuring the information shown in PI Vision and derived from AF models remains up-to-date. This is critical for real-time monitoring of assets and processes.
• Data Completeness: Completeness metrics ensure all expected data points, such as sensor readings or calculated values, are recorded without gaps. Incomplete data can result in inaccurate analysis, impacting decision-making.
• Data Accuracy: Accuracy metrics verify that data reflects the correct values and ranges, preventing erroneous data from affecting downstream applications and visualizations. This is essential for maintaining trustworthy insights in PI Vision.

These metrics are monitored over time to track performance, detect anomalies, and take corrective action. For example, thresholds can be set for data freshness, triggering alerts if data becomes outdated.

2. Metadata

Metadata provides descriptive context, essential for understanding the nature, origin, and transformations of data within the PI ecosystem.

• Data Context: Metadata in PI AF includes information on asset hierarchies, attributes, and templates, giving users context for data analysis. This is essential for interpreting PI Vision visualizations correctly.
• Transformation Tracking: In complex systems, data often undergoes transformations (e.g., calculations, aggregations). Metadata captures these transformations, helping users track data flows and processes in AF.
• Governance and Compliance: Metadata supports data governance by providing traceability and enforcing data usage policies. This is important for complying with industry regulations, as metadata can document data provenance and access histories.

Comprehensive metadata improves discoverability and interpretation within the PI ecosystem, promoting effective use of data assets across teams.

3. Tag Usage

Data usage maps out the path data takes within PI System, from ingestion through various transformations in PI AF and finally to its visual representation in PI Vision.

• Data Flow Tracking: Tag usage tracks how data moves from sources like sensors or external systems through the PI System and into AF for organization and visualization in PI Vision. Understanding data flow helps ensure consistency and correctness.
• Blast Radius Analysis: When changes are made to data sources or AF templates, tag usage information helps teams assess the downstream impact on other data consumers, ensuring changes don’t disrupt operations or visualizations in PI Vision.
• Error Tracing: Tag usage allows teams to trace back through transformations and data sources to identify and correct issues, minimizing downtime and improving troubleshooting efficiency.

With data usage tracking, teams gain clarity into data dependencies and transformations, reducing the risk of unintentional disruptions and ensuring data quality across interconnected systems.

4. Logs

Logs capture detailed records of events, errors, and other activities within the PI System, supporting real-time troubleshooting and root-cause analysis.

• Error Diagnosis: When errors occur, logs provide a timeline of events, helping to pinpoint issues and support efficient troubleshooting. For example, logs might show when a data feed stopped updating, helping users to restore data freshness.
• Audit Trails: Logs serve as audit trails, essential for governance, compliance, and reporting. They offer a historical record of data handling within PI System, ensuring transparency for internal and regulatory reviews.

Logs are integral for maintaining system reliability and operational efficiency, giving teams the details needed for rapid problem identification and resolution.

Integrating the Pillars for a Cohesive Data Observability Strategy

By combining these four pillars, organizations gain a comprehensive view of their PI System environment, enhancing reliability and quality:

• Integrating Data Quality Scores and Logs: Data Quality Scores highlight issues, while logs provide granular details, creating a feedback loop for real-time monitoring and troubleshooting.
• Metadata Contextualization: Metadata enriches metrics and logs by adding context about data origins and transformations, supporting effective diagnosis and resolution of data issues.
• Understand Tag Usage for Impact Analysis: Tag Usage helps visualize dependencies, aiding impact analysis when changes occur and reducing the risk of errors in data pipelines.
• Establishing a Continuous Feedback Loop: Regular analysis of metrics, metadata, tag usage, and logs allows for proactive monitoring and improvement of data observability practices.

Together, these pillars form a robust foundation for ensuring the accuracy, reliability, and integrity of data in PI System environments, helping organizations maintain trustworthy data for operational efficiency and strategic insight.