APERIO has succeeded where others have failed. It can proactively fix PI data problems at scale, automate anomaly detection, and interactively perform root cause analysis. What’s different about APERIO’s approach is its reliability to produce accurate PI data 100% of the time by using rigorous AI machine learning across millions of live data streams (greater than 2M tags, for example). This machine learning technology measures data quality by ensuring accuracy, consistency, completeness, validity, integrity, and timeliness of all operational data. It can alert at the highest possible level of the asset framework with insight and recommended actions to resolve poor PI data issues.
Not only do companies spend a ton of tremendous time and resources cleaning the data, but their solutions are not nearly as precise as APERIO’s. It is hard to find a one-size-fits-all solution because each data set is different. For example, linear interpolation only works for some correlations, and maximum likelihood estimations can be biased with a small sample size. With APERIO DataWise’s reliance on unsupervised, automated AI machine learning, however, working with missing data gets more and more accurate, making it unnecessary to spend time differentiating different data sets. With quantifiable data quality and trending, APERIO DataWise for PI can contextualize alerts and integrate with PI notifications.
If the quality of your PI data putting your value-added initiatives—i.e., analytics, APC, process optimization, predictive models, AI, etc.—at risk, learn how you can easily deploy APERIO DataWise for PI to monitor, track, and instantly improve the quality of your PI data.