Incident Management in the PI System: Best Practices

Mobilization

When an incident is detected, the relevant team members must be assembled to investigate and resolve the issue. Common roles in this phase include:

• Incident Commander: The lead responsible for coordinating the response. This person may not be an expert in the specific system but will guide the process and make decisions on the next steps. Communicates with stakeholders, ensuring that subject matter experts are not overwhelmed with requests for updates.
• Scribe: Responsible for documenting all actions taken during the incident for future reference.
• Subject Matter Experts: Data Engineers and PI Administrators who can diagnose and address the issues at hand.

Clearly defined roles allow for efficient execution of the response plan, even under pressure.

Diagnosis

During the diagnosis phase, the team assesses the scope, impact, and cause of the incident. It’s crucial to escalate the issue to the appropriate severity level based on its potential impact on operations. Examples of severity levels might include:

• Sev 5: Minor issue; can be fixed later, no operational impact.
• Sev 4: Some degradation to internal systems, minimal user impact.
• Sev 3: Significant degradation, noticeable impact on operations.
• Sev 2: Partial outage affecting customer-facing services, high user impact.
• Sev 1: Complete outage of critical services, extreme user impact.

For instance, in a manufacturing setting using PI System, an incident affecting data from critical equipment might quickly escalate to Sev 1 if it threatens production timelines.

Resolution

In the resolution phase, the team implements measures to address the root cause identified during diagnosis. This can involve actions such as:

• Rolling back changes to data models.
• Rerunning interfaces or calculations to recover lost data.
• Restarting tasks within the PI System.
• Backfilling missing data points to restore completeness.
• Setting up fallback mechanisms to ensure data availability.

Monitoring continues during this phase to verify that resolutions are effective and that critical business metrics are restored.

Closure

Once the incident is resolved, the team documents the entire incident response process and identifies areas for improvement. The focus should be on learning rather than placing blame. Conducting a post-incident analysis helps refine capabilities related to monitoring, documentation, and operational runbooks, reducing the likelihood of similar incidents in the future.

Real-World Example: Incident Response in an Industrial Setting

Consider a scenario where a manufacturing facility relies on real-time data from various sensors to monitor equipment health via PI Vision. One day, the operations team notices that critical temperature data from a major machine has not updated as expected.

• Detection: Automated alerts are set up in the PI System to notify the team if temperature data hasn’t been received within a specified time frame. Upon receiving the alert, the team begins the incident management process.
• Mobilization: The incident commander assembles a response team, including a data engineer familiar with the sensor data flow and a process engineer who understands the operational context.
• Diagnosis: The team confirms that the issue is localized to the temperature sensor data and checks the connection logs, identifying a communication failure with the sensor. They classify this as a Severity 2 incident due to its potential impact on production quality.
• Resolution: The data engineer re-establishes the connection and reruns the data extraction process from the PI System. They also set up a fallback to use historical data until the issue is resolved.
• Closure: Once the data is restored, the team documents the incident, detailing the steps taken and outcomes. They discuss potential enhancements to the monitoring system to prevent future occurrences.

Conclusion

Implementing an incident management process is essential for data teams working with the PI System. By following a structured approach to detection, mobilization, diagnosis, resolution, and closure, teams can efficiently manage incidents, minimize disruption, and enhance overall data governance. Adopting these practices enables organizations to learn from incidents, ultimately improving the reliability and integrity of their operational data. As data teams integrate these principles, the importance of incident management in ensuring operational continuity and data reliability cannot be overstated.