Data Cataloging as a Catalyst for Effective Data Governance and Improved Decision-Making

Varun Garg

Vg751@nyu.edu

Abstract

Data governance is a foundational component for organizations managing data as a strategic asset in today’s data-driven environments. Effective data governance ensures data quality, security, accessibility, and regulatory compliance, all of which are crucial for maintaining organizational integrity and competitive advantage. This paper explores how data cataloging, as a core tool within data governance frameworks, facilitates metadata management, data lineage tracking, and role-based access control (RBAC). We analyze the technical mechanisms through which data cataloging contributes to governance objectives, such as maintaining data quality and enabling regulatory compliance. Additionally, this study examines the impact of data cataloging on decision-making within organizations, highlighting improvements in decision accuracy, speed, and accountability. By drawing from examples in sectors such as finance and healthcare, this paper outlines the challenges associated with data cataloging implementation, including integration complexity and scalability issues. The paper concludes with future directions, focusing on artificial intelligence (AI)-enabled metadata tagging and cloud-native cataloging solutions, which promise to enhance data cataloging capabilities and make governance frameworks more agile and scalable.

Keywords

Data Governance, Data Cataloging, Metadata Management, Data Quality, Data Lineage, Role-Based Access Control (RBAC), Compliance, AI-Driven Cataloging, Cloud-Native Solutions, Data-Driven Decision-Making.