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

Editor’s Note: This post was originally published in November 2020. We’ve updated the content to reflect the latest information and best practices so you can stay up to date with the most relevant insights on the topic.

Master data management (MDM) is known for delivering trustworthy golden records—single, authoritative, accurate versions of a business entity’s data across multiple data sources and datasets. Yet in practice, silos persist, and data remains fragmented across systems and domains, leaving critical relationships disconnected. Without trusted, real-time views of key business entities, LLM-based AI agents struggle because they lack the connected, context-rich data needed to operate effectively and accurately. 

Data ontologies and enterprise knowledge graphs address this gap by connecting fragmented data and sources to identify cross-entity relationships. By uncovering meaningful connections and revealing valuable, actionable insights, organizations can establish a connected, trustworthy data foundation that more effectively supports analytics, operations, and AI. 

What Is an Enterprise Knowledge Graph?

Enterprise knowledge graphs are connected, contextualized views of an organization’s data that highlight the relationships among and between key business entities. Using enterprise knowledge graphs, organizations can surface complex, interconnected relationships within their data such as:

• Customers who are also suppliers or subsidiaries of suppliers
• Organizations and business contacts 
• 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
• Consumers who are part of the same household

What Is a Data Ontology? 

Data ontologies provide a way to standardize meaning within an enterprise knowledge graph by defining the concepts, relationships, and rules used to describe a domain. Ontologies provide a common vocabulary for human consumption, clarifying how the organization categorizes entities, how the entities relate to each other, and what those relationships mean. By mapping respective entities to the shared ontology, groups across the organization are able to share knowledge and data more efficiently, and AI systems can consistently interpret and comprehend information to generate more accurate insights. 

The Importance of Enterprise Knowledge Graphs

Enterprise knowledge graphs provide a critical foundation for AI. A well-structured graph for AI transforms data from isolated entities into a connected network that provides context and meaning. By surfacing relationships across entities—people, organizations, households, and more—they enable organizations to uncover insights that would otherwise remain obscured in siloed systems. 

This connected understanding is essential for AI and LLM-based AI agents. Not only do enterprise knowledge graphs provide the structured and connected data that AI applications need to thrive, but they also support reasoning by structuring relationships in a way that AI-based systems can traverse and interpret. 

When organizations use enterprise knowledge graphs, they benefit from trusted, 360-degree views of key business entities and their related connections; cross-entity resolution across disparate systems; reduced data team overhead; and better AI outcomes. 

Why Master Data is the Foundation for Enterprise Knowledge Graphs

When enterprise data is plagued by inaccuracies, duplicates, and missing fields, those deficiencies are inevitably reflected in enterprise knowledge graphs. Rooted in poor data quality, broken graphs undermine the integrity of relationships and context, leading to flawed recommendations, missed opportunities, and increased operational risk. Ultimately, users lose trust in the data foundation, which slows down decision-making and limits the value the organization receives from its data investments. 

However, organizations that follow the MDM Journey quickly discover a clear path to delivering the high-quality, trustworthy golden records needed to power context-rich, reliable enterprise knowledge graphs. The MDM Journey prioritizes data quality and trust. By assessing their data, improving its quality, and reviewing it with key stakeholders, organizations can confidently deliver the trustworthy data needed for analytics, operational systems, and AI applications. This high-quality master data provides the foundation that enterprise knowledge graphs rely on to accurately represent cross-entity relationships. When based on complete, consistent, reliable data, enterprise knowledge graphs deliver more precise context and richer insights. 

How Tamr Powers Enterprise Knowledge Graphs

Tamr’s AI-native MDM solution helps organizations to build trusted, connected enterprise knowledge graphs so they can uncover and understand complex relationships contained within their data. And when you connect these enterprise knowledge graphs to AI reasoning systems, these systems become even smarter and more “human” in their reasoning abilities.

With its powerful AI-powered entity resolution capabilities, Tamr enables organizations to identify and connect entities at scale, forming the foundation for modeling relationships across datasets and mapping connections that traditional, rules-based MDM solutions often overlook. Using persistent IDs, linked through these graphs, Tamr delivers the knowledge AI agents need to navigate the data in siloed systems and surface relationships hidden deep within them. 

Tamr also enriches key business entities with data from trusted, third-party sources, adding firmographic, hierarchical, and relationship details that provide greater context for these important records. And using ontologies, Tamr establishes a consistent semantic framework that helps to bridge the gap between unified, mastered data and industry-specific ontologies in order to address specific concerns such as regulatory compliance and interoperability. 

Finally, real-time insights and flexible models enable Tamr to deliver trusted relationship data directly into operational workflows. And it can do this even as needs change—without the need for re-tuning.

Turning Mastered Data Into Business Insights

Mastering data is a critical first step, but as organizations seek to derive more value from that data, the focus must shift from isolated golden records to connected, contextualized enterprise knowledge graphs. 

Here is an example of how enterprise knowledge graphs can help, illustrated through a real-world example.

A financial services company is introducing a new teen checking account product, and they want to cross-sell and upsell it to their existing customer base. By finding and connecting customer entities who live in the same household, it’s easier for the firm to accurately assess which customers could benefit from this new offering and balance this opportunity against any potential risk from a customer with a poor credit history. Instead of viewing each customer as an individual entity, the firm can use an enterprise knowledge graph to see a holistic view of the entire household, including both teens and adults. Now, the firm can target its financial advice based on the household’s combined situation, not just the status of one individual. As a result of these contextual insights, the firm is more likely to uncover new revenue opportunities and improve customer relationships, while also reducing Know Your Customer (KYC) compliance risk. 

From Trusted Master Data to Enterprise Knowledge Graphs

The path from trusted master data to enterprise knowledge graphs leads to richer context and deeper understanding of business data. With enterprise knowledge graphs, organizations can extend their foundation of clean, trustworthy golden records by connecting data, adding context, and exposing relationships, enabling them to move beyond simply managing data to realizing its full value.