Abstract: A device may process, using a first model and based on an entity-specific task description that is included in entity role data and that is associated with a role, the entity role data to identify a task associated with the role. The device may determine, using a second model and based on the entity-specific task description and standardized descriptions of automation-capable tasks, a task automation score associated with the task. The device may determine, using a third model and based on a characteristic of the task and mappings of standardized characteristics to a plurality of automation categories, a set of automation category scores for the task. The device may classify, based on the set of automation category scores, the task as being associated with a particular automation category, and may perform an action associated with the task automation score and the particular automation category.

Abstract: An analytics platform may generate one or more user interfaces based on the set of testing parameters. The analytics platform may provide the one or more user interfaces. The analytics platform may receive first test information corresponding to first respondents. The analytics platform may identify, based on the first test information and based on information identifying particular respondents of the first respondents, characteristic attributes of the particular respondents. The analytics platform may train, based on the values of the characteristic attributes and performance information associated with the first respondents, a predictive model. The analytics platform may obtain the second test information corresponding to the one or more second respondents. The analytics platform may determine the predicted performance information associated with the one or more second respondents using the predictive model. The analytics platform may perform an action based on the predicted performance information.

Abstract: A device may receive data regarding a set of entities. The device may pre-process the data regarding the set of entities to convert a portion of the data from an unstructured format to a structured format. The device may generate a network representation of the data. The network representation may include a plurality of nodes representing characteristics of the data and a plurality of edges connecting at least some of the plurality of nodes and representing a relationship between each pair of connected nodes of the plurality of nodes. The device may automatically evaluate the network representation of the data to determine a set of recommendations relating to the set of entities. The device may generate a user interface to provide information identifying the set of recommendations. The device may communicate with a client device to cause the user interface to be provided for display via the client device.

Abstract: An analytics platform may generate one or more user interfaces based on the set of testing parameters. The analytics platform may provide the one or more user interfaces. The analytics platform may receive first test information corresponding to first respondents. The analytics platform may identify, based on the first test information and based on information identifying particular respondents of the first respondents, characteristic attributes of the particular respondents. The analytics platform may train, based on the values of the characteristic attributes and performance information associated with the first respondents, a predictive model. The analytics platform may obtain the second test information corresponding to the one or more second respondents. The analytics platform may determine the predicted performance information associated with the one or more second respondents using the predictive model. The analytics platform may perform an action based on the predicted performance information.