Evaluating the Impact of AI on Performance Metrics in Automated Data Lake Management

Abstract

The proliferation of big data has necessitated the evolution of data management techniques, with data lakes emerging as a prominent solution. However, effectively managing these large repositories poses significant challenges, particularly in terms of data quality, accessibility, and processing efficiency. This paper evaluates the impact of artificial intelligence (AI) on performance metrics within automated data lake management. We analyze the key performance indicators (KPIs) influenced by AI techniques and provide empirical evidence from recent case studies demonstrating improved outcomes in data governance, retrieval speeds, and analytical efficiency. The findings suggest that AI-enhanced automation can significantly elevate the effectiveness of data lake operations, offering organizations a competitive edge in data-driven decision-making.