The High Cost of Data Quality Issues
In the realm of operational technology, the integrity of data is paramount. Consider a scenario where a crucial calculation for a key performance metric, vital for monitoring and decision-making, was inadvertently deleted from their PI Asset Framework database. This oversight left operations teams scrambling for answers as the PI System data engineers and administrators embarked on a lengthy troubleshooting journey to identify the root cause of the discrepancy. The time spent on this investigation not only delayed operational insights but also jeopardized informed decision-making, illustrating the costly repercussions of data quality issues.
While such incidents can have devastating impacts, proactive monitoring can serve as a safeguard. In this article, we’ll explore how effective monitoring tools can help avoid disasters and ensure the reliability of critical data in operational environments.
Proactive Monitoring: A Key to Preventing Data Quality Issues
Not all stories of data downtime end in disaster. Some incidents can be avoided entirely with the right monitoring tools in place. For example, consider the case of a chemicals company that detected anomalies in the PI System data monitoring energy consumption. Without intervention, these irregularities could have led to significant equipment failures and operational disruptions. Fortunately, the PI Asset Framework data team identified issues with their latest changes in PI Asset Framework before those changes rolled out to production, affecting downstream business users.
Thanks to this coincidental discovery, the data quality issue—estimated to potentially cost up to $50,000—was resolved before it could disrupt operations further.
Mitigating the Long-Term Effects of Bad Data Through Data Observability
In operational technology, where equipment uptime and safety are critical, the stakes associated with poor data quality are exceptionally high. Bad data is not merely an inconvenience; it poses a significant risk that undermines trust, hampers productivity, and strains finances. This is why automated data observability has become indispensable in industrial environments.
By adopting a robust data observability framework, OT teams can proactively manage bad data before it escalates into larger problems. With capabilities such as alerting on data quality issues and tools for data usage tracking, engineers can trace the origins of data problems, understand their impact, and take swift corrective actions.
As one operations engineer stated, “Bad data can infiltrate every aspect of our operations, impacting reliability and safety. With Osprey, we can now detect and resolve data quality issues before they escalate into costly mistakes.”
Reliable data is the backbone of efficient operations. With a comprehensive data observability strategy, OT teams can keep poor-quality data at bay and safeguard the integrity of essential PI System assets, from the PI Data Archive to PI Vision.
