Presented by Google Cloud
For decades, enterprise data infrastructure focused on answering the question: “What happened in our business?” Business intelligence tools, data warehouses, and pipelines were built to surface historical trends and performance snapshots, revealing past sales figures, customer patterns, and operational metrics. These systems worked well when decisions were driven by dashboards and quarterly reports.
But artificial intelligence has changed the game. Today’s most powerful systems don’t just summarize the past; they make real-time decisions. They go beyond static observation to dynamic reasoning — not just answering what happened, but answering why they happened, what is likely to happen and, most critically, what action should be taken next.
Enterprises are realizing that traditional architectures, even in the cloud, are not enough. AI needs more than access to data. It requires access to meaning and it needs to drive business outcomes for decision makers.
That’s where knowledge graphs come in.
The hidden layer that makes AI work
There is a deeper “semantic” layer that is fundamental to AI success. How do enterprises take their data assets and expose the context, relationships, and metadata that allows AI models to infer deeper reasoning?
A knowledge graph represents real-world entities like people, places, and products, along with the relationships between them. Unlike traditional databases that store data in tables, knowledge graphs organize information as nodes and edges. This makes them better suited for AI systems that reason, infer, and act based on context.
Knowledge graphs helped solve key BI problems like brittle ETL and stale dashboards. Now, the same principles support AI. The demand for freshness and connected context is even more critical when algorithms must adapt and act in real time. Building this foundation requires understanding how knowledge graphs actually work in practice.
Designing data infrastructure that thinks
Once you recognize the need for a knowledge graph, your architecture must evolve. This is no mere modeling challenge. It is a shift in how data is ingested, connected, governed, and activated across the enterprise.
Think of the AI data lifecycle in four stages: capture, process, analyze, and activate, with governance embedded throughout.
Integration is the first priority. A useful knowledge graph spans structured, semi-structured, and unstructured sources. That includes transaction logs, PDFs, and sensor streams, all mapped to a shared context. Entity resolution becomes foundational: recognizing that “John Smith” in CRM, “J. Smith” in emails, and employee ID 12345 all refer to the same person. Relationship inference then uncovers hidden links, like customers with the same billing address or products frequently bought together.
Next, the infrastructure must support graph-native operations. Traditional query engines optimize for filtering and aggregation. Knowledge graphs support traversal — moving from a user to a product to a supplier to a document, following relationships to discover insights that weren’t explicitly programmed. These traversals must be fast, flexible, and semantically accurate.
Finally, freshness and observability are essential. A stale or opaque graph leads to poor decisions. Your system must support real-time updates, lineage tracking, access controls, and monitoring of both graph quality and performance.
What Google learned from a decade of knowledge graphs
Google has spent over a decade building and running one of the world’s most widely used knowledge graphs. It powers Search, YouTube, and Maps, delivering contextual results to billions of users every day.
When someone searches for “Jaguar,” the system doesn’t just return keyword matches — it infers whether they’re looking for the car, the animal, or the sports team. That shift from strings to entities is a defining feature of modern AI.
This “strings vs. things” mindset enables AI to reason over relationships, not just patterns. That ability to understand meaning is what separates truly intelligent systems.
But building the graph is only half the job. Running it at scale — keeping it fresh, evolving schemas, protecting privacy, and maintaining speed — is a continuous engineering challenge. You don’t just build a graph. You operate it like a core platform.
That’s why companies need partners with deep infrastructure and AI expertise. Knowledge graphs demand full-stack discipline across ingestion, modeling, governance, and delivery.
The intelligence layer for agentic AI
As AI shifts from summarizing the past to driving decisions, agentic AI pushes even further — pursuing business goals, invoking other tools, and chaining actions across systems. These agents need context, not just data, and knowledge graphs provide that context.
Knowledge graphs serve as the system of intelligence layer to build smarter, more accurate and grounded agents, turning data into actions that drive business outcomes in agentic AI workflows.
Just as knowledge graphs solved BI’s stale dashboards and brittle pipelines, they now power the real-time reasoning and coordination required for autonomous agents to act with intelligence and purpose.
Explore how agents can help across the data lifecycle. Learn more here.
Vinay Balasubramaniam is Director of Product at Google BigQuery.
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