Natural Language Processing in Data Governance: Enhancing Metadata Management and Data Catalogs
Abstract
Natural Language Processing has become one of the revolutionary technologies in data governance, particularly in enhancing metadata management and data catalogues. The explosive growth of data brings forth several issues for an organization to manage, discover, and utilize metadata correctly. Metadata, popularly called "data about data," is significant in data quality, discoverability, and regulatory compliance. The traditional method of managing metadata is labour-intensive and error-prone, thereby not scalable and inefficient. This paper explores applying NLP techniques in data governance to automate metadata generation, improve search and discovery within data catalogues, and enable compliance with regulatory standards. By leveraging advanced NLP models, organizations can significantly reduce manual efforts, streamline metadata processes, and ensure accurate and consistent metadata. Specific NLP techniques such as NER, topic modelling, and semantic search, which aid metadata functionalities by supporting better handling, organization, and classification of metadata, have also been presented in the work. Integrating NLP contributes significantly to fastening data discovery with more intelligent classifying and organizational processes to arrive at improved decisions.
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