In today’s industrial environments, reliable data is the lifeblood of good decisions. From process engineers to data analysts to AI models, everyone depends on clean, trustworthy data coming from systems like PI Data Archive and PI Asset Framework (AF). But not all data is good data.
When bad data sneaks in, it leads to firefighting, lost production, and costly mistakes. That’s why understanding and managing data quality is mission-critical and it starts with these 6 core pillars of data quality for the PI System.
Accuracy
Is the data correct?
Accuracy is about making sure the data in your PI System reflects what’s actually happening in the real world. If a sensor reads 50 PSI, it should be because the actual pressure is 50 PSI, not 5 or 500.
Why it matters:
Inaccurate data leads to poor decisions, like operators responding to phantom problems or ignoring real ones.
Common PI examples:
- Sensor drift causing subtle inaccuracies.
- Incorrect scaling settings on tags.
- Manual data entry errors during overrides.
Completeness
Is all the data there?
Completeness means you’re not missing critical data points. Every data stream should consistently deliver values at expected intervals. Gaps, dropped connections, or bad timestamps degrade completeness.
Why it matters:
Gaps in data skew production totals, invalidate analytics, and create misleading reports.
Common PI examples:
- Dropped values during network outages.
- AF calculations not producing results.
- Tags with inconsistent scan rates leading to patchy trends.
Consistency
Does the data agree across systems and over time?
Consistency ensures that data behaves predictably. The same value should mean the same thing everywhere, whether in PI Vision, AF Analytics, or an exported report.
Why it matters:
Inconsistent data confuses teams, introduces errors in calculations, and breaks trust in reports.
Common PI examples:
- A tag renamed in PI System Management Tools or PI Tag Configurator but not updated in downstream tools.
- Conflicting production totals between PI Data Archives under high availability.
- Multiple tags with different scan rates or ranges representing the same thing.
Timeliness
Is the data arriving when it should?
Timeliness is about how up-to-date your data is. Real-time control decisions, alarms, and AI models depend on fresh, timely data streams.
Why it matters:
Stale data delays responses, hides problems, and creates operational blind spots.
Common PI examples:
- Slow data collection from remote assets.
- AF Analytics or Event Frames not triggering as expected.
- Late arriving data disrupting real-time dashboards.
Validity
Is the data in the right format and range?
Validity ensures that data values follow expected rules, correct units, ranges, and formats.
Why it matters:
Invalid data can cause calculation errors, false alarms, or safety risks.
Common PI examples:
- A temperature reading of -500°C.
- A Boolean tag reading a value of 3.
- Tag data in the wrong engineering units (e.g. gallons instead of liters).
Uniqueness
Is each data point distinct and properly identified?
Uniqueness means there are no duplicates, and every data stream is clearly and singularly defined.
Why it matters:
Duplicate or ambiguous data causes reporting errors, double-counting, and audit problems.
Common PI examples:
- Multiple tags measuring the same thing at different scan rates and no one knows which to use.
- Duplicate asset templates creating redundant AF structures.
- Copies of production totals being pulled from different points, leading to conflicting numbers.
Why These Pillars Matter
For industrial operations, bad data isn’t just a nuisance, it’s expensive. It drives:
- Firefighting when bad readings trigger false alarms.
- Lost production when gaps in data hide process issues.
- Missed insights when AI or analytics run on flawed inputs.
- Audit risks when reporting to regulators or management is inaccurate.
Strong data quality built on these 6 pillars is the foundation for better decisions, more reliable systems, and confident business outcomes.
Final Thoughts
If you rely on the PI System for operational data, now’s the time to get serious about these 6 data quality pillars. It’s not just about having data, it’s about having data you can trust.
Interested in how to monitor and improve data quality in your PI System?
Let’s talk. Data quality is the quickest way to reduce operational risk and unlock new opportunities.
Ready to See It in Action?
If your team depends on PI System data to keep your plant running safely and efficiently. Osprey is built for you.
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