Data assessment and selection for industrial automation environments

A system comprising:

• a memory that stores executable components;
• and
• a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising:
• a user interface component configured to display a data science environment for analyzing operational data from an industrial automation environment, wherein control data from a programming environment associated with the industrial automation environment is accessible from within the data science environment through a data pipeline;
• a machine learning-based context engine configured to process the control data from the programming environment to generate contextual data;
• and
• the user interface component further configured to surface a portion of the contextual data in the data science environment.

A method of proving contextual data in an industrial programming environment, the method comprising:

• displaying, by a system comprising a processor, a programming environment for editing control logic for an industrial automation environment, wherein operational data from the industrial automation environment is accessible from within the programming environment through a data pipeline;
• and
• processing, by a machine learning-based data science engine of the system, the operational data from the industrial automation environment to generate processed data for the industrial automation environment;
• and
• identifying, by the machine learning-based data science engine of the system, a portion of the processed data relevant to a component of the control logic;
• and
• surfacing the portion of the processed data in the programming environment.