In this white paper, we outline how knowledge graphs are flexible, evolvable, semantic and intelligent. It is these characteristics that enable them to capture the description of data as an interconnected set of information that meaningfully bridges enterprise metadata silos.
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Data standardization transforms fragmented and inconsistent data into a trusted enterprise asset. By applying metadata, semantic models, ontologies, and governance, organizations ensure accuracy, interoperability, and compliance. This guide explains techniques, frameworks, and real-world examples to build consistent, AI-ready data across your enterprise.
Understand data compliance, why it matters for enterprises, and how to operationalize regulatory requirements using governance and metadata.
Learn what structured data is, why it matters for enterprises, and how it powers data governance, analytics, and AI initiatives at scale.
Discover what an AI data governance framework is and how enterprises use it to ensure responsible, compliant AI.
Discover how retrieval augmented generation (RAG) works, why it matters, and how enterprises use it to deliver trustworthy, context-aware AI outputs.
Learn how a robust data governance framework drives compliance, data quality, and AI readiness. Discover core components, benefits, and implementation best practices.
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