Implementing Data Governance Best Practices for PI System, PI Asset Framework (AF), and PI Vision

Implementing data governance best practices for PI System, PI Asset Framework (AF), and PI Vision requires a specialized approach, given these systems' role in handling operational data for industrial environments. Here’s an overview of how these systems can be effectively governed:

1. Define Clear Data Management Strategies Aligned with Operational Goals

In PI System environments, data must be managed with industrial and operational goals in mind. Creating a data management strategy should start by:

• Aligning data governance with the organization’s operational objectives, such as reducing downtime, optimizing processes, and ensuring compliance.
• Prioritizing critical data assets, such as time-series data from sensors and equipment metadata, which directly impact operational efficiency and decision-making.
• Developing a data inventory within the PI AF structure to classify and understand data assets and dependencies.

2. Establish Ownership and Responsibility

Defining clear roles for managing PI data ensures accountability and maintains data integrity:

• Data Stewards: Assign PI Data Stewards who are responsible for the data quality, security, and lifecycle within the PI environment. They should ensure adherence to standards and policies.
• Process Engineers as Business Data Owners: In many cases, process engineers are the best business owners for PI data, as they understand the operational context.
• Cross-Functional Teams: Include IT, OT (Operational Technology), and compliance representatives to promote collaboration and address diverse data requirements.

3. Implement Robust Data Quality, Access, and Security Policies

For PI System and PI Vision, data quality, access control, and security are paramount:

• Data Quality: Establish automated data quality checks and alerts for data irregularities. Tools such as PI Analytics can monitor for anomalies in time-series data and create alerts for potential issues.
• Access Control: PI System should enforce role-based access control (RBAC), ensuring only authorized personnel can access sensitive operational data. Configuring security groups in PI AF and PI Vision helps control access at the asset level.
• Data Security: Enforce security measures to protect data from unauthorized access. Integrate with Windows Identities (Active Directory) and limit edit access to both PI tag configuration changes and PI data changes.

4. Maintain Data Lifecycle and Version Control

Data lifecycle management is essential for PI System data, especially given the historical nature of operational data:

• Data Retention Policies: Define retention periods for time-series data, metadata, and event frames based on business and compliance requirements.
• Version Control: Maintain version control for key data structures in PI AF, including assets and templates, to ensure data consistency during changes.

5. Foster a Culture of Continuous Improvement and Adaptation

Data governance is not a static process. For PI System environments, continuous monitoring and adaptation are crucial:

• Regular Audits and Quality Checks: Perform regular data quality audits and security assessments to maintain data integrity and address emerging vulnerabilities.
• Adapt to Changes in Regulatory and Business Requirements: Compliance with regulations like GDPR or industry-specific standards may require adjustments to data governance practices, particularly regarding data storage and access.

Leveraging Tycho Data Osprey for Data Quality and Tag Usage in PI Systems

A tool like Osprey can provide added value by automating data quality monitoring and tracking usage within the PI ecosystem:

• Data Profiling and Quality Monitoring: Tools that integrate with PI can help by identifying anomalies, flagging quality issues, and supporting validation.
• Data Usage Tracking: Tracking usage is particularly useful when dealing with derived metrics or aggregated data in PI AF, allowing for more accurate impact analysis.
• Data Entry Validation: Validating logged data before being written to PI Data Archive helps maintain high-quality historian data.

Conclusion

Robust data governance practices enable PI System, PI AF, and PI Vision users to ensure data quality, security, and compliance while driving operational insights. With the right strategy, organizations can maximize the value of their PI data, contributing to improved performance, compliance, and informed decision-making.