Seventy-seven percent of organizations list AI-ready data as their number one investment priority for the next two to three years. But according to a 2024 Gartner survey of 247 data management leaders, the spending tells a different story. Only 37% are upgrading their data management architecture, 40% are investing in active metadata tools, and just 25% are pursuing lakehouse initiatives. The ambition for artificial intelligence far outpaces the foundational work required to make it function.
At the Gartner Data & Analytics Summit in Orlando, Gartner analyst Masud Miraz presented a 10-step framework for building data fabric architecture, the data management layer that organizations need before AI use cases can deliver reliable, contextual results.
What Is Data Fabric Architecture?
Data fabric is a foundational, long-term data management architecture that acts as an intelligent orchestration engine for enterprise-wide data stores and a control plane for all data assets. Miraz described it as the layer that creates flexible, augmented, and optimal data pipelines across both analytical and operational databases.
It achieves three things. First, flexible pipelines that catch schema drift and code drift automatically, so downstream applications are not disrupted. Second, augmented data engineering: 80% of the work data engineers perform today is repetitive and can be automated through fabric recommendations. Third, optimal workload management that balances cloud costs against performance requirements, a growing concern as cloud budgets continue to rise.
The architecture runs on three inputs: metadata from systems, active metadata insights, and knowledge graphs for semantics. It works across both analytical and operational databases, which distinguishes it from earlier data warehouse or data lake approaches that handled analytics alone.
Why Are Organizations Not Investing in AI-Ready Data Infrastructure?
According to a 2024 Gartner survey of data management leaders presented at the summit, 77% of organizations list AI-ready data as their top investment priority, yet only 37% are upgrading to data fabric architecture and just 25% are pursuing lakehouse initiatives. Data quality and governance ranked second at 64%, but the foundational architecture work required to deliver on AI ambitions sits near the bottom of actual spending.
Organizations want to deploy AI in manufacturing operations and other sectors, but the data foundation underneath those operations is not ready. The gap between stated priorities and actual architecture spending is the primary reason AI use cases stall after initial experimentation.
How Do Knowledge Graphs Reduce AI Hallucination?
Knowledge graphs are machine-readable structures of nodes and edges that describe relationships between data entities, enriched with semantics that subject matter experts build on top. They fix the hallucination problem that occurs when enterprises query data using natural language, SQL, or retrieval-augmented generation (RAG).
Traditional relational modeling fails when the number of relationships between data entities grows. Performance degrades as more joins are added. Knowledge graphs handle this by creating a semantic model that scales with relationship complexity, and they allow subject matter experts to layer business meaning on top of the raw structure.
Combined with techniques like GraphRAG and GraphQL, knowledge graphs produce more accurate and contextual answers from AI systems. This is particularly relevant for agentic AI, where agents need grounded context to operate autonomously without generating unreliable outputs.
What Is Active Metadata and Why Does It Matter?
Active metadata continuously reads system conditions and adjusts behavior, like a thermostat, compared to passive metadata that only monitors without acting, like a thermometer. Data fabric shifts metadata from a passive to an active model.
Active metadata works by comparing design-time metadata (how a system is supposed to behave) against runtime metadata (how it actually behaves). When the two diverge, the system identifies outliers and errors. Data engineers resolve those outliers, and the fabric learns from their actions. Over time, it automates responses to similar deviations.
Four types of metadata matter: technical (schemas, data types), operational (lineage, performance), business (classifications, tagging), and social (user feedback, content ratings). The business value increases from technical to social, but the requirement for organization-wide participation increases in parallel. Metadata management requires participation from every department, not only IT. According to Gartner’s conversations with data and analytics leaders, most organizations currently capture less than 10% of the metadata they should be collecting.
How Do AI Agents and Data Fabric Work Together?
AI agents require metadata for context and grounding, and data fabric requires agents to generate metadata at scale, creating a symbiotic feedback loop. Neither can function at enterprise scale without the other.
Agents need a semantic layer to navigate enterprise data. Without one, they produce hallucinated or non-contextual results. At the same time, manually tagging and annotating all enterprise metadata is impossible at scale. Organizations need agents, powered by large language models, to automate the generation of metadata itself.
The result is a feedback loop: data fabric provides active metadata that gives agents context, and agents generate new metadata that updates the fabric. This mutual dependency is why Miraz positioned agentic AI as the final step of the Gartner data fabric framework.
Should You Build or Buy a Data Fabric?
Converged data management platforms offer packaged, single-vendor implementations of data fabric architecture for organizations that want to avoid the integration work. Gartner client inquiries found that most data and analytics leaders supported the data fabric vision but wanted to avoid building it themselves.
The market now offers three categories of converged platforms:
- Application providers: Salesforce, SAP, ServiceNow, Infor
- Cloud service providers: AWS, Google, Microsoft, Oracle, IBM
- Independent software vendors: Informatica, Snowflake, Databricks, Cloudera, Palantir, Qlik, Denodo, among others
Organizations that rely on commercial off-the-shelf applications and want faster AI adoption tend toward buying. Those that depend on custom-developed applications, require full control over their technology stack, and are willing to integrate best-of-breed solutions choose to build. Single-vendor solutions may have weaker capabilities in specific areas, and vendor lock-in remains a concern.
Miraz outlined a three-phase adoption path for either approach: start with a data catalog, data integration, and data preparation as foundational elements. Then add knowledge graphs, DataOps, FinOps, and data product marketplaces. Finally, activate metadata for automated recommendations and continuous optimization.
FAQ
1. What is data fabric architecture and why does it matter for AI?
Data fabric is a foundational, long-term data management architecture that acts as an intelligent orchestration engine for enterprise-wide data stores. It uses metadata analysis, active metadata insights, and knowledge graphs for semantics to create flexible, augmented, and optimal data pipelines. For AI use cases, data fabric provides the data foundation that models require. A 2024 Gartner survey found that 77% of organizations prioritize AI-ready data, but only 37% are investing in the architectural upgrades required to deliver it.
2. How do knowledge graphs reduce AI hallucination in enterprise settings?
Knowledge graphs build machine-readable relationships between data entities using nodes and edges, enriched with semantics built by subject matter experts. When enterprises use natural language querying or retrieval-augmented generation against their data, results are often non-contextual or hallucinated. Knowledge graphs combined with GraphRAG and GraphQL provide grounded context, producing more accurate results. This is critical for agentic AI systems, where agents need reliable context to operate autonomously.
3. What is the difference between active and passive metadata?
Passive metadata monitors data like a thermometer: it reads information but takes no action. Active metadata works like a thermostat: it continuously reads conditions and adjusts the system. Active metadata compares design-time metadata against runtime metadata, identifies deviations, and learns from the corrective actions data engineers take. Most organizations currently collect less than 10% of the metadata they should be capturing across four types: technical, operational, business, and social.
4. How do AI agents and data fabric architecture work together?
AI agents need metadata for context and grounding; without a semantic layer, they produce hallucinated or non-contextual results. Data fabric needs agents because manually tagging and annotating all enterprise metadata is impossible at scale. The relationship is symbiotic: data fabric provides active metadata that gives agents context, while agents generate new metadata that updates the fabric. Neither can function at enterprise scale without the other.
This article is based on a presentation by Masud Miraz, Gartner analyst, at the Gartner Data & Analytics Summit in Orlando (2026). Lucian Fogoros of IIoT World attended the event. AI tools were used to help summarize and organize the content. Reviewed and edited by the IIoT World editorial team.
