Data Protection Strategy

Collibra and SAP – Your foundation for a business data fabric

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Key Takeaways

⇨ Managing data across complex multi-cloud and hybrid cloud infrastructures presents significant challenges, including locating data, addressing duplicate data, and ensuring data trustworthiness and relevant context.

⇨ Adopting a business data fabric offers an effective strategy for data management, moving beyond mere data transfer to preserve and enhance context.

⇨ Collibra stands out as a centralized platform to organize, document, and govern data and AI tailored to both technical and business users and features an enterprise-wide view of data assets that integrates business, technical, and operational contexts, enhancing stakeholders' understanding of data value across the ecosystem.

Clean and trusted data is essential for informed decision-making, regulatory compliance, and operational efficiency, enabling businesses to thrive, innovate, and remain competitive. High-quality data reduces operational risks, increases ROI, and facilitates collaboration across technical and business teams. However, managing data across multi-cloud and hybrid cloud infrastructures is challenging, often resulting in loss of business context during data extraction and replication. Centralized governance and decentralized access are crucial for maintaining data integrity and enabling real-time decisions. Adopting a business data fabric, such as SAP Datasphere within SAP BTP, provides a unified, semantically-rich data layer that ensures seamless, scalable access to data while preserving business context. Collibra complements this by offering a centralized platform for organizing, documenting, and governing data and AI, with features like cross-system lineage tools and business glossaries. The integration of Collibra with SAP Datasphere enhances data governance, quality, and observability, fostering collaboration and efficient data usage. Together, they offer a comprehensive business data fabric, ensuring data is discoverable, reliable, and understood, thus supporting AI models and streamlining approval processes for better transparency and visibility of data assets.

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