u/Leading-Cellist-5865

Do you deal with Historian data? How do you monitor the data quality for Historian data?

Since the process data from sites are vital to downstream analytics, the quality of the data in the historian for respective tags are important. Lile catching missing data, flatlined data, erroneous data, etc so that we can correct before using it for analytics or reporting.

I am curious, how do you do this?

So far, I deal with this using bespoke python code that I create and just monitor them daily, but one part of my brain says "there has to be a better way". Is there any best practices or industry standards? Any free python libraries that do this in a simple way?

Do you deal with Historian data? How do you monitor the data quality for Historian data?

Since the process data from sites are vital to downstream analytics, the quality of the data in the historian for respective tags are important. Lile catching missing data, flatlined data, erroneous data, etc so that we can correct before using it for analytics or reporting.

I am curious, how do you do this?

So far, I deal with this using bespoke python code that I create and just monitor them daily, but one part of my brain says "there has to be a better way". Is there any best practices or industry standards?

Do you deal with Historian data? How do you monitor the data quality for Historian data?

Since the process data from sites are vital to downstream analytics, the quality of the data in the historian for respective tags are important. Lile catching missing data, flatlined data, erroneous data, etc so that we can correct before using it for analytics or reporting.

I am curious, how do you do this?

So far, I deal with this using bespoke python code that I create and just monitor them daily, but one part of my brain says "there has to be a better way". Is there any best practices or industry standards?

Do you deal with Historian data? How do you monitor the data quality for Historian data?

Since the process data from sites are vital to downstream analytics, the quality of the data in the historian for respective tags are important. Lile catching missing data, flatlined data, erroneous data, etc so that we can correct before using it for analytics or reporting.

I am curious, how do you do this?

So far, I deal with this using bespoke python code that I create and just monitor them daily, but one part of my brain says "there has to be a better way". Is there any best practices or industry standards?