“A data fabric can be thought of as the connective tissue that ensures consistent accessibility, availability, and understanding of data across an organization,” says Dominic Wellington, data and AI expert at SnapLogic. “Individual siloed platforms may have their own internal data transfer systems, and particular teams or departments may adopt interchanges that work for that domain, but a data fabric operates at a higher level, ensuring that unified data policies are applied end-to-end across the entire enterprise.”
Types of data fabrics
When reviewing data fabrics, it’s important to consider their primary use cases, supported data types, data processing capabilities, data management structures, and governance functions. Below are some considerations when reviewing data fabrics as features, platforms, and stand-alone products.
- Some data fabrics are optimized for analytics and machine learning use cases and may have limited support for unstructured data.
- Other data fabrics extend the functionality of data governance platforms beyond data cataloging and metadata management and now include persistent data management, data quality, and dataops capabilities.
- Many data integration and API connectivity platforms go beyond proxying, pipelining, and transforming data to include search, governance, and other capabilities from data centralization.
- Some SaaS platforms are extending their connectivity and data integration capabilities, enabling multicloud portability and persistent data.
- The more advanced data fabrics support features needed for AI agents and AI model training. These platforms create a semantic context layer for structured and unstructured data sources, support Model Context Protocol (MCP) integrations, have real-time query capabilities, centralize policy-driven governance, and track data lineage.
Why data fabrics are needed for AI
Data fabrics are not just for enterprises, and today, even smaller companies need them as part of their AI democratization programs. Here are a few reasons why:
- AI agents in enterprise SaaS solutions need access to broader data sets than those core to their workflows. Platforms such as Adobe, Appian, Oracle, Salesforce, ServiceNow, SAP, and Workday offer data fabric capabilities to bring data outside of the business processes they manage into scope for their AI agents.
- Unstructured data is important for setting the context for AI agents, and data fabrics are now used to provide access to documents, emails, transcripts, and other media formats.
- Data fabrics provide data access standards for the devops teams experimenting with AI code generators, vibe coding tools, and spec-driven development approaches to develop applications and AI agents.
- As companies use MCP servers to connect AI agents, data fabrics provide a standardized way for the agents to access governed, trusted data sources.
“As AI agents move from generating insights to taking action, the data fabric becomes foundational in the agentic era,” says Irfan Kahn, president and chief product officer of SAP Data & Analytics. “Most enterprises operate across scattered data sources and diverse data landscapes, and what’s needed is shared business context, governed access, and clear accountability for how data is used in decision-making. Without that context, agents can’t fully understand or coordinate across the enterprise to deliver meaningful value.”
