Data Term Definitions

Enterprise Knowledge Graphs

Enterprise knowledge graphs provide a contextualized view of the connections and relationships between business entities.

What Is an Enterprise Knowledge Graph?

An enterprise knowledge graph is a connected, contextualized view of an organization’s data that highlights the relationships among key business entities. Enterprise knowledge graphs connect fragmented data across systems, organizing it into clearly defined entities and relationships. As a core capability of AI-native master data management (MDM), they help organizations create a more complete, unified view of their data.

These graphs create multi-domain relationship maps that surface hidden relationships within the data. These maps show how records relate to others across domains, providing the context AI systems and AI agents need to deliver trustworthy insights.

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‍Resources

How Business Context Solves the Agentic AI Trust Gap

How Do Enterprise Knowledge Graphs Work?

Enterprise knowledge graphs use AI-powered entity resolution and persistent IDs to connect data across sources. By creating a unified view of data across systems, they enable AI agents to uncover hidden relationships among key business entities—such as people, organizations, and households—and associated items like products, orders, invoices, locations, and more.

‍Resources

How to Leverage Ontologies and Enterprise Knowledge Graphs With Your Mastered Data

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What Are the Benefits of Enterprise Knowledge Graphs?

Enterprise knowledge graphs connect a company’s most important data, enabling organizations to, for example, identify cross-entity relationships between contacts and companies, create a unified view of healthcare providers and organizations, identify 360-degree consumer views of households, and more. When you feed these knowledge graphs into AI, systems and agents gain real-world context and understanding of the business, allowing them to better process and problem-solve.

‍Resources

Enterprise Knowledge Graphs: How to Find Connections for Your Data

What Tools or Platforms Can Be Used to Create Enterprise Knowledge Graphs?

Enterprise knowledge graphs are a core platform capability of an AI-native MDM solution. Using AI-native MDM, organizations can create enterprise knowledge graphs that reveal complex connections—such as customers who are also suppliers or subsidiaries of suppliers; contacts who are also employees of the company; alumni who are also employees, grad students, or parents of a current, former, or prospective student; healthcare providers who are part of a practice that is affiliated with a healthcare system; and consumers who are part of the same household.

Key Takeaways: Enterprise Knowledge Graphs

Enterprise knowledge graphs connect fragmented data to reveal connections among enterprise data entities, giving AI systems and AI agents the context they need to work more effectively.
They reveal how different business entities—such as people, organizations, households, products, orders, invoices, and locations—relate to each other.
As a core platform capability of AI-native MDM, enterprise knowledge graphs enable solutions like Tamr to deliver trusted relationship data to AI systems and AI agents.

Enterprise Knowledge Graph FAQs

What business problems do enterprise knowledge graphs solve?

Enterprise knowledge graphs help organizations better understand the complex, interconnected relationships within their data, including how different entities relate to one another. Using this information, users can then ask deeper questions about their data and uncover meaningful connections that help them make better, more informed decisions.

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Can Tamr combine data from multiple systems into a single enterprise knowledge graph?

Yes, AI-native MDM solutions like Tamr enable organizations to connect fragmented data across applications—including CRMs, ERPs, CDPs, and other corporate systems—to create a single enterprise knowledge graph. This unified view helps organizations understand the relationships among people, organizations, products, locations, and other business entities.

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Do organizations need a Graph Database such as Neo4j before they can use Tamr?

No, Tamr does not require organizations to have a Graph Database in place. However, if an organization is using a complementary Graph Database solution like Neo4j, Tamr can integrate with it, ensuring it always has the most up-to-date and accurate entity data.

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Related Data Terms

AI-Native Master Data Management (MDM)

A modern data mastering approach built and centered around AI.

Agentic Data Curation

Approach to data curation where AI agents analyze, explain, and resolve data issues.

Golden Records

Unified, shared, trusted views of business entity data across systems and sources.

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Enterprise Knowledge Graph FAQs

AI-Native Master Data Management (MDM)

Agentic Data Curation

Golden Records

Entity Resolution

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