Data Governance Practices in Traditional Enterprise Systems

Abstract

Data governance practices are important in traditional enterprise systems because organizations depend on controlled, accurate, secure, and well-managed data for daily operations and decision-making. Traditional enterprise systems often contain data across finance, human resources, procurement, inventory, sales, and customer service modules, making clear ownership and control essential. Existing literature highlights data stewardship, access control, data quality rules, metadata management, compliance monitoring, master data control, and policy-based data management as major elements of enterprise data governance. However, many organizations still face challenges such as unclear data ownership, inconsistent data definitions, duplicate records, weak validation rules, poor documentation, and limited monitoring across legacy applications. This research is important because weak data governance can reduce reporting accuracy, increase operational risk, affect regulatory compliance, and create inconsistent business decisions. This article discusses data governance practices in traditional enterprise systems, focusing on governance roles, data policies, quality standards, access management, audit controls, metadata documentation, and compliance procedures. The study concludes that effective data governance improves data reliability, strengthens accountability, reduces data-related risks, and supports consistent enterprise information management.