Artificial intelligence is only as powerful as the data it’s built on. Today’s foundation models thrive on vast, diverse and interconnected datasets, mostly drawn from public domains. The real differentiator for organizations lies with their private, mission-specific data.

This is where a data fabric comes in. A modern data fabric stores and integrates information and serves as the connective tissue between raw enterprise data and AI systems that can reason, recommend and respond in real time.

Know your data

Before you can connect your data, you need to understand it. Knowing what data you have, where it resides, and how it flows is the foundation to every AI initiative. This understanding is built on four core pillars:

• Discoverability: Can your teams find the data they need without a manual search?
• Lineage: Do you know where your data comes from, and how it has been altered?
• Context: Can you interpret what the data means within your mission or business environment?
• Governance: Is it secure, compliant and trusted enough for decision-making?

At the center of an effective data fabric is a data catalog that takes an inventory of sources and continuously learns from them. It captures relationships, context and organizational knowledge across every corner of a digital ecosystem.

Anything can be a data source, including databases, spreadsheets, code repositories, sensor streams, ETL pipelines, documents and even collaborative discussions. When you start treating all of it as valuable data, the picture of your enterprise becomes complete.

Knowing your data is the foundation. Connecting your data is transformation.

Hidden value of knowledge

The true power of a data fabric lies in its ability to link seemingly unrelated data. When disparate systems — including operations, logistics, HR, supply chain and customer support — are connected through shared metadata and inferred relationships, new knowledge emerges.

Imagine correlating operational readiness data with HR analytics to anticipate staffing shortages or connecting structured metrics with unstructured logs to reveal previously invisible patterns. This derived knowledge is your competitive advantage.

A connected intelligence framework feeds directly into machine learning and generative AI systems, enriching them with contextually grounded, multi-source insights. The result: models that are powerful, explainable and mission aware.

Minimal requirements for an AI-ready data fabric

Building an AI-ready data fabric requires more than simple integration. It demands systems designed for adaptability, compliance and continuous intelligence.

A next-generation catalog goes far beyond simple source indexing. It must integrate lineage, governance and compliance requirements at every level, particularly in regulated or public-sector environments. This type of catalog evolves as data and processes evolve, maintaining a living view of the organization’s digital footprint. It serves as both a map and a memory of enterprise knowledge, ensuring AI models operate on trusted and the latest information.

Data is not enough. Domain expertise, often expressed in documents, code and daily workflows, represents a critical layer of organizational intelligence. Capturing that expertise transforms data fabric from a technical repository into a true knowledge ecosystem.

An AI-ready data fabric should continuously integrate new data, metadata and derived knowledge. Automation and feedback loops ensure that as systems operate, they learn. Relationships between data objects become richer over time, while metadata grows more contextual. This self-updating nature allows organizations to maintain a real-time, adaptive understanding of their operations and knowledge base.

The role of generative AI in the data fabric

Generative AI plays a pivotal role. Through conversational interfaces, an AI system can interactively capture rationales, design decisions and lessons learned directly from subject matter experts. These natural exchanges become structured insights that enrich the broader data landscape.

Generative AI fundamentally changes what is possible within a data ecosystem. It enables organizations to extract and encode knowledge that has traditionally remained locked in minds or informal workflows.

Imagine a conversational agent embedded within the data fabric that interacts with data scientists, engineers or analysts as they develop new models or pipelines. By asking context-aware questions, such as why a specific dataset was chosen or what assumptions guided a model’s design, the AI captures valuable reasoning in real time.

This process transforms static documentation into living knowledge that grows alongside the enterprise. It creates an ecosystem where data, context and human expertise continually reinforce one another, driving deeper intelligence and stronger decision-making.

The way forward

The next generation of enterprise intelligence depends on data systems built for agility and scale. Legacy architectures were never designed for the speed of today’s generative AI landscape. To move forward, organizations must embrace data fabrics that integrate governance, automation and knowledge capture at their core.

John Mark Suhy is the chief technology officer of Greystones Group.

The post The AI data fabric: Your blueprint for mission-ready intelligence first appeared on Federal News Network.