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1、EY and Elastic CollaborationEnhancedEnhancedData Extraction Data Extraction using Gen AIusing Gen AI2 2EY and Elastic CollaborationAbstractAbstractThe growing accessibility of diverse types of data including structured databases,unstructured text,and multimedia,pose significant challenges for organi

2、zations that want to derive meaningful insights from complex data.Conventional search and retrieval methods are increasingly inadequate for managing the complexity and immense volume of data today.Lets take a look at how generative AI(gen AI)can enhance retrieval strategies through language embeddin

3、gs and source grounding,focusing on optimizing performance,speed,and scalability to effectively address these challenges.To assess the effectiveness of these gen AI-driven strategies,well explore a critical intersection between financial services and environmental,social,and governance(ESG).Well spe

4、cifically focus on extracting data from unstructured documents,such as banks emissions reports and quarterly reports,and constructing a database from these data points that were previously difficult to access,demonstrating the practical applications and benefits of advanced data retrieval in the fin

5、ancial services sector.3 3EY and Elastic CollaborationIntroductionIntroductionData extraction has always been challenging,particularly when dealing with unstructured,inconsistent,and notably large amounts of data.Organizations have often relied on external data providers,which was not only costly bu

6、t also not always up-to-date or live.Alternatively,organizations had to build their own extraction pipelines,an endeavour that came with its own challenges.But with the advent of gen AI,the entire financial services industry has been disrupted,resulting in a lasting change in the field of data extra