Artificial intelligence and machine learning based incident management

A method for artificial intelligence and machine learning based incident management, the method comprising:

• ascertaining, by at least one hardware processor and for a specified time duration, incident data related to a plurality of incidents associated with organizational operations of an organization;
• preprocessing, by the at least one hardware processor, the incident data to remove specified features of the incident data;
• analyzing, by the at least one hardware processor, the preprocessed incident data to determine the organization associated with the preprocessed incident data;
• separating, by the at least one hardware processor, the analyzed preprocessed incident data into a first part that is to be used to train a machine learning classification model, and a second part that is to be used to test the trained machine learning classification model;
• training, by the at least one hardware processor and based on the analyzed preprocessed incident data from the first part, the machine learning classification model;
• testing, by the at least one hardware processor and based on the analyzed preprocessed incident data from the second part, the trained machine learning classification model by
• determining, for the trained machine learning classification model, a precision score, and
• based on a determination that the precision score is less than a specified precision score threshold, iteratively training the machine learning model until the precision score is greater than or equal to the specified precision score threshold;
• ascertaining, by the at least one hardware processor, for another specified time duration, new incident data related to a further plurality of incidents associated with further organizational operations of the organization;
• determining, by the at least one hardware processor, specified organizational key performance indicators associated with the further organizational operations of the organization;
• determining, by the at least one hardware processor, based on the trained machine learning classification model, and from the further organizational operations of the organization, an output that includes a specified organizational key performance indicator, from the specified organization key performance indicators, impacted by an incident of the further plurality of incidents;
• and
• controlling, by the at least one hardware processor and based on the output, an operation of a hardware system associated with the identified organization.

A non-transitory computer readable medium having stored thereon machine readable instructions, the machine readable instructions, when executed by at least one hardware processor, cause the at least one hardware processor to:

• ascertain, for a specified time duration, incident data related to a plurality of incidents associated with organizational operations of an organization;
• preprocess the incident data to remove specified features of the incident data;
• analyze the preprocessed incident data to determine the organization associated with the preprocessed incident data;
• separate the analyzed preprocessed incident data into a first part that is to be used to train a machine learning classification model, and a second part that is to be used to test the trained machine learning classification model;
• train, based on the analyzed preprocessed incident data from the first part, the machine learning classification model;
• test, based on the analyzed preprocessed incident data from the second part, the trained machine learning classification model by
• determining, for the trained machine learning classification model, a precision score, and
• based on a determination that the precision score is less than a specified precision score threshold, iteratively training the machine learning model until the precision score is greater than or equal to the specified precision score threshold;
• generate, based on mapping of the organizational operations of the organization to associated organizational key performance indicators, a corpus;
• for each incident of the plurality of incidents, determine, based on the corpus, an organizational key performance indicator that is impacted by the incident;
• ascertain, for another specified time duration, new incident data related to a further plurality of incidents associated with further organizational operations of the organization or another organization;
• determine, specified organizational key performance indicators associated with the further organizational operations of the organization or the another organization;
• determine, based on the corpus and the trained machine learning classification model, and from the further organizational operations of the organization or the another organization, an output that includes:
• an organizational operation impacted by an incident of the further plurality of incidents associated with the further organizational operations of the organization or the another organization, and a specified organizational key performance indicator, from the specified organization key performance indicators, associated with the organizational operation;
• or
• the specified organizational key performance indicator, from the specified organization key performance indicators impacted by the incident of the further plurality of incidents;
• and
• control, based on the output, an operation of a hardware system associated with the identified organization or the another organization.