Securing Sustainable Value from AI with Collibra
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Key Takeaways
⇨ Effective AI integration depends on addressing the critical data challenges like data quality and lineage, privacy, scalability, integration, and bias.
⇨ However, to address such challenges, enterprises need to establish AI governance frameworks, invest in data cleansing and normalization, and ensure quality assurance throughout the AI model lifecycle.
⇨ SAP Datasphere and Collibra offer a business data fabric architecture that governs both SAP and non-SAP data, ensuring trusted data is accessible to all users regardless of its location.
AI’s effectiveness in enterprises hinges on robust data management, as high-quality, consistent data is essential for reliable AI model performance. Organizations face significant challenges such as siloed data sources, inconsistent formats, and outdated or incomplete data, which impede AI’s ability to generate meaningful insights. Addressing these challenges requires establishing AI governance frameworks, investing in data cleansing, and ensuring quality assurance throughout the AI lifecycle. SAP Datasphere and Collibra’s collaboration provides a comprehensive data fabric architecture that governs both SAP and non-SAP data, enhancing transparency and governance of AI models. Collibra’s AI governance solutions enable organizations to track data lineage, ensure compliance, and foster collaboration between technical and business stakeholders. This integrated approach supports AI initiatives by maintaining high data quality, mitigating risks, and promoting data-driven decision-making, thereby driving digital transformation and competitive advantage.
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