System and method for artificial intelligence based data integration of entities post market consolidation

1. A processor implemented method, comprising:

extracting, using one or more text mining models, metadata associated with at least one category of each entity of a plurality of entities participating in a deal via one or more hardware processors, from a plurality of data sources associated with the plurality of entities, wherein the plurality of data sources accessible through a cloud infrastructure;

dynamically configuring, via the one or more hardware processors, an assessment for the at least one category based on the metadata by using a set of Natural Language Processing (NLP) rules, the assessment comprising a plurality of parameters associated with a data integration of the plurality of entities, wherein the assessment being administered to a plurality of users from the plurality of entities to obtain a plurality of responses to the assessment from the plurality of users;

assigning, by using an artificial intelligence (AI) based processing model, a similarity score to the plurality of responses, via the one or more hardware processors, wherein the similarity score is indicative of extent of match between distinct response obtained from the plurality of entities; and

recommending, by a recommendation engine, a data integration model from amongst a plurality of data integration models for the deal based on the similarity score, via the one or more hardware processors.

2. The method of claim 1 , wherein the deal comprises one of a merger and an acquisition between the plurality of entities participating in the deal.

3. The method of claim 1 , wherein a category of the at least one category comprises one of people, processes, technologies and software applications of the each entity.

4. The method of claim 3 , further comprising systematically storing the metadata in a database classified based on the people, the processes, the technologies and the software applications to be accessible based on function and access granted as a single version of truth.

5. The method of claim 1 , wherein the one or more text mining models comprises Named Entity Recognition, Entity Resolution, and Key Entity Detection.

6. The method of claim 1 , wherein the AI based processing model is pretrained using a supervised learning technique.

7. The method of claim 1 , wherein the AI based processing model is pretrained using a transfer learning technique.

8. The method of claim 1 , wherein the plurality of integration models comprises a clone model, a purge and merge model, a clone-acquire model, a develop new systems model, a develop interfaces model, a pick best of breed and purge model, and combinations thereof.

9. A system ( 300 ) for data integration, comprising:

one or more memories ( 315 ); and

one or more hardware processors ( 302 ), the one or more memories ( 315 ) coupled to the one or more hardware processors ( 302 ), wherein the one or more hardware processors ( 302 ) are configured to execute programmed instructions stored in the one or more memories ( 315 ), to:

extract, using one or more text mining models, metadata associated with at least one category of each entity of a plurality of entities participating in a deal, from a plurality of data sources associated with the plurality of entities, wherein the plurality of data sources accessible through a cloud infrastructure;

dynamically configure an assessment for the at least one category based on the metadata by using a set of Natural Language Processing (NLP) rules, the assessment comprising a plurality of parameters associated with the data integration of the plurality of entities, wherein the assessment being administered to a plurality of users from the plurality of entities to obtain a plurality of responses to the assessment from the plurality of users;

assign, by using an artificial intelligence (AI) based processing model, a similarity score to the plurality of responses, wherein the similarity score is indicative of extent of match between distinct responses obtained from the plurality of entities; and

recommend, by a recommendation engine, a data integration model from amongst a plurality of data integration models for the deal based on the similarity score.

10. The system of claim 9 , wherein the deal comprises one of a merger and acquisition between the plurality of entities participating in the deal.

11. The system of claim 9 , wherein a category of the at least one category comprises one of people, processes, technologies and software applications of the each entity.

12. The system of claim 11 , wherein the one or more hardware processors are further configured by the instructions to systematically store the metadata in a database classified based on the people, the processes, the technologies and the software applications to be accessible based on function, and wherein the one or more hardware processors are further configured by the instructions to grant the access as a single version of truth.

13. The system of claim 9 , wherein the one or more text mining models comprises Named Entity Recognition, Entity Resolution, and Key Entity Detection.

14. The system of claim 9 , wherein the AI based processing model is pretrained using a supervised learning technique.

15. The system of claim 9 , wherein the AI based processing model is pretrained using a transfer learning technique.

16. The system of claim 9 , wherein the plurality of integration models comprises a clone model, a purge and merge model, a clone-acquire model, a develop new systems model, a develop interfaces model, a pick best of breed and purge model, and combinations thereof.

17. One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

extracting, using one or more text mining models, metadata associated with at least one category of each entity of a plurality of entities participating in a deal via one or more hardware processors, from a plurality of data sources associated with the plurality of entities, wherein the plurality of data sources accessible through a cloud infrastructure;

dynamically configuring, via the one or more hardware processors, an assessment for the at least one category based on the metadata by using a set of Natural Language Processing (NLP) rules, the assessment comprising a plurality of parameters associated with a data integration of the plurality of entities, wherein the assessment being administered to a plurality of users from the plurality of entities to obtain a plurality of responses to the assessment from the plurality of users;

assigning, by using an artificial intelligence (AI) based processing model, a similarity score to the plurality of responses, via the one or more hardware processors, wherein the similarity score is indicative of extent of match between distinct response obtained from the plurality of entities; and

recommending, by a recommendation engine, a data integration model from amongst a plurality of data integration models for the deal based on the similarity score, via the one or more hardware processors.