Microsoft Fabric Ontology: The missing layer between Data Governance and AI

The data industry has spent the last twenty years focused on one primary challenge: connecting data. We built and created increasingly sophisticated ways of moving information between systems and making it available for analytics. Many organisations today still struggle with a much simpler problem and that is clarity on terminology. Different department terms often mean different things when they use the same business terms.

A customer means one thing in CRM, another in finance and something slightly different again in marketing. Product definitions vary between commercial teams and operational systems. Employee records, supplier information, and assets frequently exist across multiple applications, each with its own interpretation and business rules.

Humans have become relatively adept at navigating these differences because they understand organisational context. AI does not understand this. As organisations increasingly adopt Copilot, AI agents and intelligent business applications, the challenge is no longer giving AI access to data but giving AI the meaning of terms.

This is where Microsoft Fabric Ontology, currently in preview as part of Fabric IQ, becomes particularly interesting. Microsoft describes Ontology as a machine understandable representation of enterprise vocabulary that defines business concepts through entity types, properties and relationships, creating a shared business context layer that can be used across teams, applications and AI agents. 

What makes this significant is not the technology itself but how important the need is for AI to have business understanding, just as much as it requires data access.

The problem we have been trying to solve for years

Anyone who has worked in data governance will recognise this challenge immediately. We have built business glossaries, data dictionaries, conceptual models and reference architectures in an attempt to create consistency across the organisation. Governance programmes have invested significant effort defining critical data elements, agreeing business terminology and establishing ownership for key information assets. The difficulty has always been turning those definitions into something operational. Many governance initiatives successfully define what a customer is, but those definitions often remain trapped in documents, spreadsheets or governance tools that sit separate from the systems actually using the data. The glossary becomes a reference point for people rather than an active component of the architecture. As a result, governance knowledge frequently exists in one location whilst operational data exists somewhere else. Both are valuable, but the connection between them is often weak.

Microsoft's vision for Ontology appears to be closing that gap. Rather than maintaining business definitions separately from data, Ontology allows organisations to define core business concepts and then bind those concepts directly to data residing within OneLake, Power BI semantic models, lakehouses and other Fabric data sources. The result is that the business definition and the physical data become connected through a common semantic layer. From a governance perspective, the business glossary stops being passive documentation and becomes a part of how information is understood and consumed throughout the platform.

Why AI changes everything

Inconsistencies in business terminology are often frustrating but manageable. Analysts learn the system nuances and data engineers write transformation logic to reconcile differences. Often reporting teams spend their time explaining why the numbers vary between departments.

AI fundamentally changes the scale of the problem. When an AI agent is asked a question such as Which customers are most at risk of churn? it needs more than access to customer records. It needs to understand what a customer is, which systems contain authoritative information, how related concepts connect to one another and which business rules should be applied during analysis. Without that context, even a highly capable model can produce inconsistent or misleading outcomes. 

Microsoft specifically highlights Ontology as a shared business context layer that can be consumed by Fabric agents and AI-driven workflows to support reasoning and actions across domains. Rather than asking questions against individual tables, users and AI agents can query business concepts that already carry organisational meaning.

As organisations move beyond simple AI assistants and towards agentic architectures where AI systems are expected to make decisions, execute processes and reason across multiple business domains having this tool is important. The quality of decisions will depend heavily on the quality of the organisational context provided to them.

More than another Semantic Model

Ontology is not simply another version of a semantic model. Semantic models primarily exist to simplify analytics and reporting. They provide a business friendly representation of data designed to support measures, calculations and reporting experiences.

Ontology aims to tackle a much broader challenge. It introduces concepts such as entity types, properties and relationships that represent how the organisation understands the world. Customer, Supplier, Product, Contract and Asset become business entities that exist independently of any particular source system. Relationships become explicit rather than buried inside data models and joins.

Microsoft also introduces a graph representation that allows relationships between entities to be stored and queried directly. In practical terms, this means understanding not just what something is, but how it connects to everything around it. Customers place orders. Suppliers provide products. Employees manage projects. Assets support services. These connections become part of the semantic model itself rather than logic recreated repeatedly by individual development teams. This kind of contextual understanding  for AI is enormously valuable because reasoning is often driven by relationships as much as by data values.

Is Ontology replacing Microsoft Purview?

One of the most common questions I have seen since the announcement is whether Ontology makes Microsoft Purview less relevant. Ontology and Purview address different layers of the same challenge.

Purview focuses primarily on understanding, governing and protecting information assets. It discovers data, provides lineage, manages classifications, supports compliance activities and enables organisations to establish trust in their information landscape.

Ontology focuses on meaning. 

Where Purview helps answer questions such as Where is this data?, Who owns it?, How sensitive is it? and Where did it come from?, Ontology helps answer questions such as What does this represent?, How does it relate to other business concepts? and How should AI reason about it?

The two capabilities compared.

Capability	Microsoft Fabric Ontology	Microsoft Purview
Primary objective	Create shared business meaning	Govern and manage enterprise data
Key focus	Business concepts and relationships	Data assets and metadata
Business glossary	Operational semantic layer	Governance glossary and terminology
AI support	Grounding and business reasoning	Trusted metadata and governance controls
Relationships	Business relationships between entities	Data lineage and technical relationships
Graph capabilities	Native graph-based business context	Metadata and lineage visualisation
Data discovery	Bound Fabric data sources	Enterprise-wide discovery
Classification	Limited focus	Core capability
Compliance	Not a primary objective	Core governance capability
Security and risk	Relies on platform controls	Governance, risk and compliance controls
Typical audience	AI teams, business architects, domain experts	Data governance, security and compliance teams

What this means for the Future of Governance

For me, the most significant aspect of Ontology is what it says about the future direction of governance. Historically, data governance was largely created for people. Policies were written for humans, glossaries were maintained for humans. with data standards interpreted by humans. Increasingly, we need governance artefacts that machines can understand directly. AI agents, Copilots and autonomous systems cannot read a governance policy and infer organisational meaning in the way people do. They need structured, machine readable context. They need agreed definitions and relationships. Also business vocabulary they can reason over consistently is required.

Ontology appears to be Microsoft's recognition that the next generation of governance must serve both humans and machines. As AI becomes embedded within business operations, organisations will increasingly discover that trusted data is only part of the equation. Equally important is ensuring that AI understands what that data actually means. Data governance professionals have argued for years that data without context has limited value. In the era of enterprise AI, that statement feels more true than ever. Trusted AI requires trusted data, but it also requires trusted meaning. Fabric Ontology is a significant step towards delivering that meaning at scale.

Reference

What is ontology (preview)?

Microsoft Tools for Making Data AI-Ready