Federated Knowledge Graph: The Missing Link in Your Data Strategy

When it comes to using data, every organization is striving to achieve the same goal—find and access the right data quickly, and trust that it’s accurate. It sounds like a process that should be simple, but too often, it’s not.

A federated knowledge graph offers value for every data user, making data readily accessible, trusted, and usable. It gives each business domain its own view of your organization’s overall data universe, without sacrificing consistency, security, or collaboration. It’s powerful. It’s flexible. And it enables smarter, safer, faster decision-making across your enterprise by making the right data easy to access.

What is a Federated Knowledge Graph?

In the “Hype Cycle for Artificial Intelligence, 2024,” Gartner notes that the two biggest movers on the cycle are AI engineering and knowledge graphs. “Knowledge graphs are machine-readable representations of the physical and digital worlds,” according to Gartner. “They capture information in a visually intuitive format, yet are still able to represent complex relationships. More importantly, they provide dependable logic and explainable reasoning (as opposed to GenAI’s fallible but powerful predictive capabilities).”

You can think of a federated knowledge graph as your company’s internal Google search engine for data, only smarter. At Actian, we define the knowledge graph as a data structure representing a universe of knowledge through a collection of interlinked concepts, entities, relationships, and events. By linking semantic metadata, a federated knowledge graph provides context to data and offers a trusted framework for data integration, data discovery, and governance.

By using a federated knowledge graph, you benefit from a dynamic, interconnected view of your metadata that reflects how your business actually works. Unlike traditional data catalogs that impose a rigid, static metamodel, which is essentially a preconceived idea of how your organization should be structured, a federated knowledge graph evolves with your business and can be designed to meet your organization’s unique and real-life structure.

Each domain within your organization, such as marketing, finance, and product development, can maintain its own localized knowledge graph that reflects its specific language, workflows, and standards. These “sub-graphs” are interconnected, allowing users to discover and use data across your organization while also having domain autonomy.

In other words, it’s a modern system that respects how your teams operate while ensuring your organization’s data products are discoverable, accessible, and usable at scale.

Why Traditional Data Catalogs are not Effective

Many legacy data platforms use centralized governance models and monolithic architectures that limit flexibility and scalability. These approaches require rigid structures, strict and sometimes lengthy approval processes, and central data ownership.

These models typically require companies to restructure their data to fit the technology, instead of the other way around. That leads to many drawbacks, such as:

• Low adoption rates. Users don’t understand or trust a system that doesn’t reflect their day-to-day reality, so they don’t use or fully optimize the platform.
• Data disconnects. Business domains are forced to speak a data language that doesn’t align with their own, creating confusion and limiting collaboration.
• Costly delays. Making even minor changes, such as updating data definitions, adding new sources, or adjusting to evolving business needs, is time-consuming and expensive.

And worst of all? These static systems often describe a version of your business that doesn’t actually exist in reality, leaving teams to make decisions based on outdated or irrelevant representations of how your organization truly operates.

Why a Federated Approach Works Better

A federated knowledge graph flips the traditional, static model on its head. It enables a more natural, human-centric way of working with data. Instead of forcing teams into a one-size-fits-all structure, it lets each domain:

• Define its own terminology, KPIs, and governance policies.
• Maintain its own data catalog and metadata.
• Build data products using their own standards.
• Share data seamlessly through governed data contracts.

For example, the federated knowledge graph that powers the Actian Data Intelligence Platform delivers rich, in-depth, contextual search results. It gives you visibility along with a detailed understanding of your enterprise data landscape. In addition, it automatically identifies, classifies, and tracks data based on contextual factors, mapping your data products to key concepts to make them easily discoverable and accessible.

So, what does a federated knowledge graph look like in action? Here are a few examples:

• Compliance teams define sensitive data policies in their knowledge graph, while clinical teams curate their own operational data products. The two can share information securely through governed contracts without compromising privacy or integrity.
• The marketing team defines terms and customer segments in its business glossary for a new campaign, while the finance team defines revenue and margin metrics. Each domain maintains its own universe of meaning for data, yet the federated graph connects the dots for executive reporting and recommendations.
• Various departments such as risk, audit, and sales each maintain their own views of critical data. The federated knowledge graph ensures traceability and data lineage for compliance, while enabling agile data exploration and product recommendations for sales teams.

Decentralized data management can still result in enterprise-wide harmony when it’s powered by the right technology. A modern federated knowledge graph offers a smarter way to align teams, delivers trusted data products faster, and drives tangible business outcomes.

Supporting Data Intelligence in the AI Era

The federated knowledge graph is more than a tool for today’s data needs. As our Chief Evangelist Ole Olesen-Bagneux explains in a short video, our federated knowledge graph is an ontological treasury for generative and agentic AI.

Because it captures rich metadata and relationships across domains, the knowledge graph provides the perfect foundation for:

• Intelligent, context-aware recommendations.
• Enhanced semantic search across business glossaries.
• Greater explainability and transparency in outputs.
• Enabling modern data governance.
• A holistic view of data for a better understanding and optimization.

In a world where AI readiness depends on the quality of your metadata, a federated knowledge graph is your best strategic asset. And it’s an area where Actian can help. The Actian Data Intelligence Platform is built on a federated knowledge graph that makes scaling enterprise-wide governance and empowering business users simple and intuitive.

If you’re looking to modernize your data strategy and accelerate your journey to AI-readiness, don’t just implement another data catalog. Federate your knowledge. Take a product tour today to experience data intelligence powered by a federated knowledge graph.

About Dee Radh

As Senior Director of Product Marketing, Dee Radh heads product marketing for Actian. Prior to that, she held senior PMM roles at Talend and Formstack. Dee has spent 100% of her career bringing technology products to market. Her expertise lies in developing strategic narratives and differentiated positioning for GTM effectiveness. In addition to a post-graduate diploma from the University of Toronto, Dee has obtained certifications from Pragmatic Institute, Product Marketing Alliance, and Reforge. Dee is based out of Toronto, Canada.