Gartner Presents Data Fabric Framework for AI

At the Gartner Data & Analytics Summit in Orlando, Gartner Sr Principal Analyst Masud Miraz presented a 10-step framework for building data fabric architecture, as reported by IIoT-World. Per a 2024 Gartner survey of 247 data management leaders cited in that report, 77% of organizations list AI-ready data as their top investment priority for the next two to three years, while only 37% report upgrading their data management architecture, 40% are investing in active metadata tools, and 25% are pursuing lakehouse initiatives.

According to the presentation as reported, data fabric functions as a long-term orchestration engine and control plane spanning analytical and operational data stores, built on three inputs: system metadata, active metadata insights, and knowledge graphs for semantics. Miraz described three operational goals: flexible pipelines that catch schema and code drift automatically, augmented data engineering (he reportedly said 80% of current data-engineering work is repetitive and automatable), and workload management that balances cloud cost against performance.

The gap between stated AI-data priority (77%) and actual architecture investment (37%) mirrors a recurring pattern in enterprise analytics: teams fund visible AI use cases before the underlying metadata and orchestration layers that make those use cases reliable. Data and ML teams building production AI should treat the fabric's three inputs - metadata, active metadata, knowledge graphs - as a checklist for what is typically missing before models can use enterprise data safely.

Adoption rates for active metadata tooling and lakehouse initiatives in the next round of Gartner surveys, vendor roadmaps combining knowledge graphs with metadata layers, and published case studies quantifying reductions in schema-drift incidents or cloud-cost improvements from fabric-style control planes.