Tata Consultancy Services Limited

Abhay Sodani

Girish Keshav Palshikar

Rajiv Radheyshyam Srivastava

Mangesh Sharad Gharote

Sachin Sharad Pawar

Sangameshwar Suryakant Patil

Devraj Goulikar

Swapnil Vishveshwar Hingmire

Kamlesh Mhashilkar

Aparna Joshi

Saurabh Das

Divya Vasudevan

Savita Suhas Bhat

Ankita Jain

Rajdeep Sarkar

Vamsee Krishna Kodali

Pragati Hiralal Dungarwal

To evaluate performance of organizational units using Human Capital Value (HCV), the input data spread across an enterprise may be received from a user. The input data includes employee data, project related data, and organizational unit data for performance evaluation. The input data is analyzed for generating HCV variables. The HCV variables are stored in a repository (). Further, the HCV variables may be parsed to determine an optimal set of variables. Based on the parsing, an efficiency of each organizational unit is computed. The computing is based on a Multi Criteria Decision Analysis (MCDA) technique. Based on the computing, the organizational units are ranked in a decreasing order of efficiency.

Evaluating performance of organizational units using human capital values

With the scale of information available today along with the existing diverse channels of communication, manual processing of information is becoming a challenge and companies across industries are under tremendous pressure to lower transactional costs. Artificial Intelligence based automation of business transactions has seen regulatory hurdles due to probabilistic nature of the outcome. The main challenge lies in processing of transactions with unstructured information. Systems and methods of the present disclosure uses deterministic as well as probabilistic approaches to maximize accuracy. The larger use of deterministic approach with configurable components and ontologies helps to improvise accuracy, precision and reduce recall. The probabilistic approach is used when there is absence of quality information or less information for learning. Also, confidence indicators are provided at attribute level of data being processed and at each decision level.

Automated cognitive processing of source agnostic data

The present subject matter relates to method(s) and system(s) to rank human profiles based on selection criteria personalized to a selector. In an embodiment, the method includes obtaining querying criteria from the selector to query a database comprising a set of human profiles. Further, a subset of human profiles is determined from the set of human profiles based on the querying criteria and a default ranking mechanism. Furthermore, a selection based ranking is obtained for the subset of human profiles. Further, based on the selection based ranking, a ranking function is determined that is indicative of a relative inclination of the selector towards the one or more implicit attributes. Such a determination is by capturing at least one implicit attribute in the ranking function from the selection based ranking. Further, the ranking function is applied to rank a fresh set of human profiles based on the ranking function.

Methods and systems for ranking of human profiles

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Abhay Sodani