Google is rebranding and expanding its data and analytics offerings into what it calls the Agentic Data Cloud. This new architecture aims to help large companies move their AI projects from simple tests to full-scale production. The goal is to turn scattered data into a unified, meaningful layer that AI agents can understand, reason over, and act on more reliably at scale.

Connecting Data and Enhancing AI Capabilities

The Agentic Data Cloud builds on Google’s existing data platform tools like BigQuery, Dataplex, and Vertex AI. It combines these services into a shared intelligence layer that improves metadata management, data governance, and interoperability across different cloud providers. This means data from multiple sources can be integrated more seamlessly, enabling smarter AI applications.

A key part of this new architecture is the Knowledge Catalog, an upgraded version of Dataplex Universal Catalog. It extends Google’s metadata foundation into a semantic layer that maps business meanings and relationships across data sources. This semantic layer helps AI systems understand not just the data itself but the context and connections that make it useful for decision-making.

Supporting Business Logic and Third-Party Data

Google is also adding new tools to make business logic more accessible inside Google Cloud. One preview feature is a LookML-based agent that can derive semantics from documentation, helping AI understand how data is used within the organization. Another preview feature in BigQuery allows companies to embed business logic directly into their data queries, speeding up analysis and decision-making.

The Knowledge Catalog doesn’t just collect data; it continuously enriches its understanding by analyzing how data is used across an enterprise. It can profile datasets, tag unstructured content stored in Google Cloud Storage, and even infer missing data structures. Using Google’s Gemini models, the catalog can generate schemas and identify relationships, making data more organized and easier for AI to work with.

This focus on turning raw data into meaningful business context is seen as a major challenge for enterprise AI. Experts highlight that inconsistent meanings across systems are a key barrier to deploying reliable AI solutions. A unified semantic layer can help CIOs establish consistent business language and reduce the manual effort needed to connect metadata and data lineage.

Industry Moves Toward Semantic Data Layers

Google’s approach aligns with broader trends among cloud giants. Microsoft with Fabric IQ and Work IQ, and Amazon Web Services with Nova Forge, are also building layers that add semantic understanding to enterprise data. These efforts aim to make AI more consistent, easier to operationalize, and scalable across large organizations.

While Microsoft’s strategy involves wrapping AI applications with business context, AWS focuses on integrating business logic directly into foundational data layers. Google’s Agentic Data Cloud reflects a similar push to embed semantic understanding into the core of enterprise data management, ultimately making AI systems more reliable and easier to use at scale.

Overall, Google’s new architecture signals a shift toward smarter, context-aware AI that can better serve business needs. By turning fragmented data into a cohesive semantic layer, companies are better positioned to unlock the full potential of AI across their operations.

Inspired by

• https://www.infoworld.com/article/4162737/google-pitches-agentic-data-cloud-to-help-enterprises-turn-data-into-context-for-ai-agents.html

Sources

• google.com
• google.com
• futurumgroup.com
• cio.com
• moorinsightsstrategy.com