Leveraging Large Language Models (LLMs) to Enhance Data Governance in Digital Products

Abstract

The rise of digital products has significantly increased the volume and complexity of data generated, making data governance a critical challenge for organizations. Traditional data governance frameworks struggle to keep pace with the evolving needs of digital products, leading to gaps in data quality, privacy, and compliance. In this paper, we explore the adoption of Large Language Models (LLMs) for enhancing data governance in digital products. By leveraging the natural language processing (NLP) capabilities of LLMs, organizations can automate key aspects of data governance, such as data classification, privacy compliance, and anomaly detection. We investigate the potential benefits, challenges, and methodologies for integrating LLMs into existing data governance systems, drawing on a range of case studies and recent developments in artificial intelligence. Statistical analysis is performed to evaluate the effectiveness of LLM-based approaches compared to traditional methods. Our findings suggest that LLMs can provide substantial improvements in data quality management, legal compliance, and operational efficiency, marking a significant shift in the governance of digital products.