Industrial data quality is crucial for reliable operations. Proxus provides comprehensive data governance metrics to ensure your dashboards, alerts, and analytics are based on trustworthy data.
The 5 Dimensions of Data Quality
Proxus evaluates data quality using five orthogonal dimensions, each scored from 0-100%, providing a holistic view of data trustworthiness:
1. Data Accuracy
Percentage of successful complete cycles where operations succeed AND all expected data is returned. This measures the reliability of the data acquisition process.
2. Data Completeness
Percentage of expected data items that are actually received. This tracks whether all expected values are present in each data collection cycle.
3. Data Integrity
Percentage of received data that passes validation checks. This ensures the data content is valid and not corrupted during transmission.
4. Data Consistency
Rolling window consistency based on recent success/failure patterns. This measures the stability of data delivery over time.
5. Data Freshness
Time-based freshness score calculated from how recently data was received. This ensures you're working with current information.
Device Diagnostic Metrics
Every connected device (via Fieldbus, MQTT, or OPC-UA protocols) maintains detailed diagnostic metrics through the ProxusActorMetrics structure:
| Metric | Description | Purpose |
|---|---|---|
| Total Requests | Total number of communication attempts | Overall activity measurement |
| Successful Requests | Number of successful communications | Success rate indicator |
| Failed Requests | Number of failed communications | Error tracking |
| Last Communication Time | Timestamp of last communication attempt | Activity awareness |
| Last Data Received Time | Timestamp of last successful data reception | Freshness calculation basis |
| Total Data Received | Volume of data (bytes) received from device | Throughput measurement |
| Average Response Time | Average response time in milliseconds | Performance indicator |
| Connection Error Count | Number of connection-related errors | Network stability indicator |
| Read Error Count | Number of read operation errors | Data acquisition health |
Freshness & Stale Data Detection
Proxus automatically tracks data freshness using time-based interpolation against predefined thresholds:
- ≤ 30 seconds: 100% freshness
- ≤ 1 minute: 95% freshness
- ≤ 2 minutes: 90% freshness
- ≤ 5 minutes: 80% freshness
- ≤ 15 minutes: 60% freshness
- ≤ 1 hour: 40% freshness
- ≤ 12 hours: 10% freshness
- > 24 hours: 0% freshness (completely stale)
The system calculates freshness by comparing DateTime.UtcNow against the LastDataReceivedTime and LastCommunicationTime, with data older than 24 hours considered completely stale.
Quality Propagation & Offline Handling
When a device disconnects, Proxus automatically updates the metrics to reflect the disconnection. The DataFreshness score decreases over time based on the predefined thresholds, ensuring that disconnected devices are properly identified as providing stale data.
The system does not emit automatic "Bad" quality packets for tags when offline, but the freshness score provides clear visibility into data staleness.
Your Rule Engine logic can be configured to consider data quality metrics when triggering alerts. Rules can be designed to ignore low-quality data or adjust sensitivity based on quality scores to prevent false alarms from stale or inconsistent data.
System Health & Internal Metrics
The platform maintains comprehensive health metrics through the actor system:
- Actor Statistics: Each device actor maintains its own metrics instance
- Thread Safety: All metrics updates are synchronized using device-specific locks
- Accumulators: Internal tracking of rolling statistics
- Memory Management: Efficient pooling and management for performance
These metrics are available through the UI and API, providing real-time visibility into system health and data quality across your entire infrastructure. You can view per-device metrics in Device Details or troubleshoot missing data in Data Flow Problems.