Data Quality is rapidly becoming one of the top areas of interest for organizations looking to extract the most benefit from their data infrastructure. As the number of connected devices continue to grow, having visibility into data quality becomes an increasing challenge.
This presentation will demonstrate how to utilize the functionality and scalability of AF to identify conditions where assets' data flow has stopped. It will highlight different logic that can be used to account for a variety of data flow regimes, and non-ideal network conditions. These strategies are also designed to minimize load on PI Analysis Service and conserve computational resources.
The presentation will also cover reporting on data quality issues and creating parameters that track long term performance. This allows users to quickly resolve new issues and prevent future ones.
David works for EDF Renewables as an Analytics and Intelligence Engineer. One of his primary focuses has been using AF to build custom monitoring of EDF's data infrastructure and track data quality of over 3000 assets across North America.
As a remote worker, David enjoys traveling and working from many locations around the world.