In the era of data-driven decision-making, the effective governance of big data has become a strategic imperative for enterprises seeking to leverage analytics for competitive advantage. This study investigates the role of big data governance in enhancing enterprise analytics, focusing on the development and implementation of robust governance frameworks and best practices. Big data governance encompasses the policies, processes, standards, and roles required to manage the availability, usability, integrity, and security of data assets. As organizations increasingly rely on complex, high-volume, and diverse data sources, the absence of structured governance can lead to data quality issues, compliance risks, and analytics inefficiencies. This research presents a comprehensive review of existing big data governance models and proposes an integrated framework that aligns governance dimensions with enterprise analytics objectives. The framework emphasizes key pillars such as data ownership, metadata management, data stewardship, quality assurance, privacy, and ethical use of data. Using a qualitative approach supported by case studies across sectors including finance, healthcare, and retail, the study identifies common challenges and success factors in deploying governance structures. Findings highlight the critical role of executive sponsorship, cross-functional collaboration, regulatory alignment, and technology enablers such as data catalogs, AI-driven governance tools, and automated lineage tracking in ensuring governance effectiveness. Furthermore, the study outlines best practices for organizations to operationalize data governance in their analytics lifecycle, including the implementation of data governance councils, standardized data definitions, and continuous monitoring mechanisms. By embedding governance into enterprise analytics workflows, organizations can improve data trustworthiness, accelerate insights, and drive better strategic outcomes. The proposed framework also supports compliance with data protection regulations such as GDPR, HIPAA, and CCPA. This research contributes to both academic discourse and practical applications by bridging the gap between big data governance theory and enterprise analytics execution. It offers actionable guidance for data leaders, CIOs, and analytics teams aiming to enhance data governance maturity and maximize the value of their data assets.