Accenture Global Solutions Limited

Sarvesh Madhusudan Damle

Rajendra Tanniru Prasad

Niju Prabha

Shankaranand Mallapur

Bhaskar Ghosh

Koushik M Vijayaraghavan

Aditi Kulkarni

Mohan Sekhar

Vijayaraghavan Koushik

Rajesh Nagarajan

Jayashri Sridevi

Dhiviya Dhanasekaran

Soumya Swarup Jena

Meiyarasu Selvam

Girish Hulkoti

Srikanth Nr

Prashant Bhadre

Anandakrishnan Rajaram

Rashmi Jhawar

Prajwal Patrick Dsilva

A device may receive work item data identifying work items associated with requirements from different tools of a project and may perform data cleansing to remove and/or modify particular words from the work item data and to generate cleansed work item data. The device may perform natural language processing on the cleansed work item data to identify synonyms for words in the cleansed work item data and may replace abbreviations in the cleansed work item data with full form text to generate final work item data. The device may identify keywords in the final work item data and may process the final work item data, the synonyms, and the keywords, with a machine learning model, to identify mappings between work items of the final work item data and to determine a confidence score for the mappings. The device may perform actions based on the mappings and the confidence score.

Utilizing machine learning and natural language processing to determine mappings between work items of various tools

A device receives a command to identify an automation evaluation for a role, determines tasks of the role based on data relating to the role, and determines activities for the tasks based on the data relating to the role. The device determines one or more automation scores, which correspond to a suitability for automation of the activities, based on a set of characteristics of the activities and based on the data relating to the role. The automation scores are determined using a machine learning model to parse natural language descriptions of the activities and score parsed portions of the natural language descriptions. The device generates, for the role, an aggregate automation score based on the automation scores, determines the automation evaluation for the role based on the aggregate automation score and characteristics of an entity associated with the role, and performs an action relating to the automation evaluation.

Data processing for role assessment and course recommendation

A device may predict, based on historical data relating to a plurality of past projects, a trigger to perform a project health check for a project. The device may process project data relating to the project to determine a health check status of the project based on predicting the trigger to perform the project health check for the project. The device may generate a recommendation relating to altering completion of the project based on the health check status of the project. The device may communicate with one or more devices to provide information identifying the recommendation. The device may receive, from the one or more devices, response information relating to the recommendation. The device may perform a response action relating to the recommendation based on receiving the response information.

Data processing platform for project health checks and recommendation determination

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Thane, India

Sarvesh Madhusudan Damle