In the era of digital transformation, enterprises increasingly rely on Enterprise Resource Systems (ERS) to enable seamless coordination across functions such as finance, operations, supply chain, and human resources. However, despite significant technological advancement, many organizations continue to experience data silos, inconsistent information flows, and limited collaboration across departments. This proposes a Framework for Enhancing Data Integration and Collaboration in Enterprise Resource Systems, designed to address these persistent challenges by fostering interoperability, transparency, and real-time decision-making. The framework integrates principles of data governance, systems interoperability, process standardization, and collaborative intelligence to establish a holistic approach for improving enterprise-wide information management and communication. The proposed framework comprises five interconnected components: (1) Strategic Data Alignment, ensuring that data architecture supports organizational objectives; (2) Interoperable System Design, enabling seamless data exchange across heterogeneous platforms; (3) Collaborative Workflows, promoting cross-functional engagement and process synchronization; (4) Data Governance and Quality Management, maintaining accuracy, security, and compliance; and (5) Performance Analytics, providing metrics for evaluating the impact of data integration on operational and financial efficiency. By embedding these components, enterprises can enhance process visibility, improve responsiveness, and support agile decision-making. This contributes to both theory and practice by linking enterprise systems integration with organizational collaboration dynamics. The framework advances the understanding of how unified data ecosystems enhance not only operational efficiency but also innovation and strategic alignment. It offers a practical roadmap for organizations transitioning toward intelligent enterprise architectures, leveraging cloud computing, artificial intelligence (AI), and data analytics to strengthen competitive advantage. Future research should empirically validate the model across industries and explore its scalability in multi-cloud and hybrid ERP environments.