Improving Data Quality in the PI System: Common Issues and Solutions
Your data doesn’t need to be perfect to make an impact, but there’s definitely room for improvement.
After extensive discussions with teams facing challenges related to data quality, it’s clear that these issues are widespread and costly. According to Gartner, businesses lose an average of $12.9 million annually due to data problems. With the growing reliance on data-driven decision-making, the integrity of data is more critical than ever—especially with the advent of generative AI. The takeaway is straightforward: poor data leads to poor results.
Let’s examine some common data quality issues that can hinder your PI System data, their underlying causes, and strategies for detection and resolution. We’ll explore:
- Bad Values
- Poor PercentGood
- Referential Integrity Issues
- Freshness Issues
- Duplicate Tags
- Metadata Inconsistencies
- Calculations Writing to the Same Tag
Understanding Data Quality Issues in PI System Data
Data quality issues are a fact of life, whether they stem from human mistakes, system quirks, or unexpected anomalies. As data travels through your pipelines, it faces multiple opportunities for compromise. Problems arise when data is inaccurate, incomplete, duplicated, or does not accurately reflect the real-world scenario. These issues can occur at any stage—be it during ingestion, transformation, or elsewhere in the process.
Some prevalent data quality challenges include:
- Bad Values: Incorrect values can lead to misleading reports and insights. Regular validation checks can help identify and rectify these anomalies.
- Poor PercentGood: A PercentGood score below 100 indicates that some data points are unreliable. Monitoring and addressing the sources of these bad values can improve overall data quality.
- Referential Integrity Issues: Problems with enumeration and table references can disrupt data relationships, leading to inaccurate analytics. Implementing integrity checks can ensure that relationships between tables are maintained.
- Freshness Issues: If the latest information isn’t consistently written to the tags, it can cause decisions to be based on outdated data. Setting up freshness checks can help verify that data is up-to-date.
- Duplicate Tags for the Same Sensor: Having multiple tags for the same sensor can inflate data storage and complicate analysis. Regular audits can help identify and eliminate duplicates.
- Metadata Inconsistencies: Inconsistent engineering units and other metadata can lead to confusion and inaccuracies. Establishing standard metadata practices can help maintain consistency across data sets.
- Calculations Writing to the Same Tag: When multiple calculations write to the same tag, it can create conflicts and distort results. Implementing a clear strategy for tag management can mitigate this issue.
Prioritizing Data Quality Issues
As data quality issues accumulate, it’s vital to prioritize them effectively. Consider the following factors:
- Affected displays
- Affected AF calculations
- Number of users impacted
- Importance to stakeholders
Understanding tag tracking—how time-series data flows and where it originates—enables faster root cause analysis and targeted remediation. This is where data observability comes into play, offering teams scalable monitoring solutions.
How Data Observability Enhances Data Quality
While manual testing may suffice at smaller scales, it becomes increasingly inadequate as data volumes rise. Data observability automates the monitoring process, providing comprehensive oversight across the entire data pipeline. With machine learning-powered quality checks, issues related to freshness, volume, and configuration changes can be identified and addressed promptly.
Data observability fosters trust by ensuring that your data is accurate, timely, and ready for stakeholders at all times. By addressing these common data quality issues in the PI System, you can significantly enhance the reliability and effectiveness of your operational data.
