Introducing the TQ Data Foundation–the Context Layer for trusted, autonomous agents. [LEARN MORE]
The video highlights the Git integration feature in TopBraid EDG, demonstrating seamless incorporation of EDG into existing workflows. Laurie Nelsen, Semantic Solution Engineer, shows how to connect an asset collection in EDG to GitHub and how to push and pull changes between EDG and GitHub.
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.
An enterprise data catalog is more than a list of datasets. It organizes technical, business, and operational metadata, provides semantic context, and enables discovery, compliance, and analytics across the enterprise. This guide explains key features, implementation best practices, and real-world use cases for regulated industries.
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.
Use Cases
Industries
Initiatives
Controlled Vocabularies
