Strategy Recognized among Notable Vendors in the 2026 Forrester Data Fabric Platforms Landscape

Quick Answer

• Strategy is recognized among Notable Vendors in The Data Fabric Platforms Landscape, Q3 2026. This is a market overview, not a product ranking.

• In our view, the important shift is from connecting enterprise systems to making data consistently understandable and governed for analytics, applications, and AI.

• Strategy Mosaic provides reusable business definitions and policies across existing data platforms, with governed access for BI and AI, MCP connectivity, semantic model portability, and operational visibility.

An AI agent can connect to multiple enterprise systems in seconds.

That does not mean it understands the business.

A CRM may define a customer one way. Finance may calculate revenue another. The warehouse may contain the right tables but not the relationships or policies explaining which definition applies to a decision.

The plumbing can be connected. The meaning can still be fragmented.

In our view, that gap helps explain why Strategy’s inclusion among Notable Vendors in The Data Fabric Platforms Landscape, Q3 2026, published by Forrester, is timely. The bigger question is how enterprises make business context reusable across their data environment.

For years, the data fabric discussion focused on connecting distributed systems, making data discoverable, and reducing the friction between clouds, databases, warehouses, and applications.

Agentic AI raises the stakes.

A person can review a questionable dashboard before acting. An AI agent may interpret the same information and initiate the next step without that pause.

It is not enough for an agent to find the right table. It also needs an approved metric, the relevant entity relationships, the user’s access rights, and the business rules needed to interpret the result.

Connectivity gets the agent to the data.

Context tells it what the data means.

What Strategy brings to the data fabric

Strategy selected business data context, real-time analytics, and security fabric as its three extended use-case focus areas in the Landscape. These are vendor-reported priorities, not independent functionality scores or rankings.

They reflect a practical combination: consistent business definitions, timely access to data, and governed permissions when dashboards, applications, and AI agents consume the same information.

Most enterprises already have platforms that store, move, and analyze data. The harder problem is ensuring that every consumer applies the same business meaning.

Strategy Mosaic provides that shared semantic layer. Teams can centrally define metrics, entity relationships, hierarchies, and business rules, then reuse them across BI tools, applications, APIs, and AI without replacing existing data platforms.

For example, a finance dashboard, an external BI report, and an AI agent should not each reconstruct a different net-revenue calculation for returns, discounts, and recognized sales. They should use the same approved business definition, even when the records reside in different systems.

Mosaic Studio supports semantic model development and Model Validation, allowing authors to inspect relationships, queries, and results before publishing. Mosaic Schema provides shared enterprise definitions and governance for reuse in self-service analytics.

Published semantic models can be consumed through supported BI and application interfaces, including SQL, DAX, APIs, and MCP. Access remains subject to the applicable model and user permissions. That lets agents work from governed business definitions rather than generating new logic from raw schemas.

Mosaic Sentinel provides visibility into model usage, query performance, and supported cost-monitoring scenarios. Together, these capabilities make business context easier to develop, share, and operate across analytics and AI.

Interoperability matters at more than one layer. Open table formats, lineage exchange, portable semantic definitions, and agent interfaces solve different problems, but all help reduce the friction between systems.

The standards in Figure 2 are complementary rather than interchangeable. Apache Iceberg addresses open table formats, OpenLineage addresses lineage exchange, Apache Ossie addresses semantic model interchange, and MCP connects AI clients with tools and governed context. The diagram illustrates an ecosystem architecture, not a claim that every standard is a native Mosaic capability.

For Mosaic, two connections are especially relevant: MCP can provide AI clients with access to approved semantic models and governed query tools; Apache Ossie (Incubating), formerly Open Semantic Interchange (OSI), supports exchange of semantic definitions through the supported YAML format.

An open data architecture can still leave business definitions trapped in individual tools. Open meaning makes those definitions more portable and useful beyond one application.

A practical test for AI-ready data fabric

“How did enterprise win rate change last quarter, and how does it relate to renewal risk for top accounts in regulated regions?”

The challenge is not just retrieving records. The agent needs consistent definitions of win rate and renewal risk, approved relationships between accounts and opportunities, and access rules for sensitive regions.

With Mosaic, an authorized AI client can use published, governed semantic models through MCP instead of inventing its own business logic from raw tables. Mosaic provides a consistent foundation for the underlying queries; the consuming AI application remains responsible for interpreting the results and presenting its final answer.

Enterprise data has become more distributed. AI is making it more important for systems to share a consistent understanding of the business.

For Strategy, that is the significance of inclusion in the Landscape. Governed business context is becoming an important part of modern data architecture, not something every dashboard, application, or agent should have to reconstruct independently.

Mosaic’s role is to make that context reusable, interoperable, and governed across the data platforms enterprises already operate.

The data does not need another place to live.

It needs a consistent way to mean the same thing everywhere it is used.

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