Ai-driven Knowledge Management for Adaptive Etl Workflows: Enhancing Data Profiling, Transformation, and Quality Assurance
Year: 2025
Type: article
Abstract: As data-driven enterprises continue to evolve, efficient Extract, Transform, Load (ETL) workflows becomecritical for ensuring data accuracy, consistency, and usability. Traditional ETL processes struggle with adaptingto dynamic data structures, heterogeneous sources, and governance requirements. This paper explores theintegration of AI-driven knowledge management with ETL workflows, focusing on adaptive data profiling, selfadjusting transformations, and AI-powered validation techniques. We analyze emerging frameworks that leveragemachine learning, rule-based knowledge systems, and automated governance models to enhance sustainability,compliance, and efficiency in modern data ecosystems.
Author: Varsha Kishin Premani
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6930847153
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