Governance of Unstructured Data: Managing Data Quality in Non-Traditional Data Sources

Abstract

In an era defined by exponential data growth, unstructured data now constitutes over 80% of all data generated globally, including diverse formats like text, video, audio, and social media posts. Despite its potential value, unstructured data presents unique governance challenges due to its complexity and lack of standardization. This study explores the importance of governing unstructured data, particularly from non-traditional sources like IoT devices and social media, emphasizing strategies for maintaining data quality, integrity, and security. Through an analysis of current frameworks and practices, this paper identifies gaps in traditional governance models and proposes a structured approach to address these challenges. Key findings highlight the importance of integrating AI and machine learning tools for data standardization and leveraging cross-departmental collaboration to manage data silos effectively.