Microsoft Fabric IQ Ontology - What It Is and Why It Matters for Australian Data Teams
Every data team I've worked with has the same recurring argument. What counts as an "active customer"? Is revenue net or gross? Does a "sale" include returns? The answers exist somewhere, often in a finance manager's head, sometimes in a Word document nobody reads, occasionally as a DAX measure in a Power BI model that only one person knows how to maintain. When a new analyst joins, they ask the question again. The answer they get depends on who they ask. This is the everyday cost of not having a shared way to describe what your business actually means by the words it uses.
Microsoft's pitch for the ontology feature in Fabric IQ is that this argument should not have to keep happening. Define what things mean once, in a place that both humans and AI agents can read, and let everything downstream point at the same definitions. The idea is not new. The interesting bit is what Microsoft has built and how it slots into the Fabric stack.
What an ontology actually is in this context
The word "ontology" sounds academic. In Fabric IQ, an ontology is a structured description of the concepts in your business and the relationships between them. A customer is a thing. An order is a thing. An order belongs to a customer. A product is a thing. An order contains line items, which reference products. Each concept has properties, definitions, and relationships to other concepts.
If you've built a semantic model in Power BI or Analysis Services, the idea will be familiar. Tables, columns, relationships, measures. The ontology is a layer above that. It is not about the physical data, it is about the meaning. A semantic model in Power BI tells you that the Sales table joins to the Customer table on CustomerID. An ontology tells you that a "sale" is a financial transaction with a customer, that it consists of one or more line items, that it has a status which transitions through a known set of values, that returns are sales with a negative quantity, and so on.
The value is in the layer of meaning being decoupled from any one technical implementation. The same ontology can be referenced by a Power BI report, a Fabric data agent, a custom application, or a chat experience in Microsoft 365 Copilot. Each of those consumers gets the same answer to "what is an active customer", because the definition lives in one place.
Why this matters now
The interesting thing about ontologies is that they have been a thing in enterprise data for decades. There are entire vendor categories built around them. Data catalogues, master data management, business glossaries. Most of them have struggled to get adopted. The reason is simple. They felt like documentation projects. You spent two years defining what everything means, you produced a glossary, and then it sat on a SharePoint nobody opened.
What is different now is AI. When you have a Copilot or an agent answering questions like "show me revenue from the top ten customers in NSW for the financial year", that agent has to know what "revenue", "top", "customers", "NSW", "financial year" all mean in your business. If it guesses, it might be right, it might be wrong, and you have no way of knowing which. If it points at an ontology, it can be deterministic about the meaning.
This is why Microsoft has put effort into the ontology feature in Fabric IQ. They want their AI products, from Copilot to data agents to whatever comes next, to be able to ask "what does this organisation mean by X" and get a real answer. The ontology is the substrate for that.
How it relates to a Power BI semantic model
Here is the question I get from technical clients. "We already have a Power BI semantic model. Isn't that the same thing?"
Not quite. A semantic model is technical. It describes how data is structured for analysis. It has tables, columns, relationships, measures. Measures are defined in DAX, which is a calculation language. The model is consumed by visuals.
An ontology is conceptual. It describes what things are and how they relate. It doesn't care about DAX or about the physical structure of your tables. A concept in the ontology might map to multiple tables in different semantic models, or to a column, or to a measure, or to a derivation across several sources. The same concept can be expressed in different physical models without losing its meaning.
The practical implication is that they are complementary, not competing. The ontology defines the language. The semantic model maps that language to a physical analytical structure. The Power BI report consumes the semantic model. An AI agent might consume the ontology directly, and rely on the semantic model behind it for the actual data fetch.
I expect the relationship between ontology and semantic model to keep evolving over the next year or two. There is talk of semantic models being able to reference ontology concepts natively. That would close the loop and make the two layers a lot easier to keep in sync.
What works well
I'll start with the positives, based on what we've seen so far.
The "shared meaning" benefit is real. When you have a finance team, a sales team, and a marketing team all using slightly different definitions of "customer", the ontology forces a conversation that has often been avoided for years. We had one client where the act of writing the ontology surfaced four different definitions of "active customer" across departments, each defensible, none reconciled. The ontology forced the question. The answer ended up being to have two formal concepts, "transactionally active customer" and "engagement active customer", with clear definitions for each, and to use them precisely from then on. That is genuine value before any AI ever touches it.
The AI agent story is also better with an ontology. When we build agentic automations for clients, the quality of the answers improves dramatically when the agent has access to a clear conceptual model. Without it, the agent guesses. With it, the agent can be deterministic about what a user is asking for.
The Fabric integration is decent. Because Fabric IQ sits inside the broader Fabric stack, the ontology can reference data that lives in OneLake, in lakehouses, in warehouses, in semantic models. The plumbing is mostly there.
What is still rough
Now the less flattering side.
The authoring experience is early. Defining concepts, properties, and relationships in the current tooling involves more clicking and form-filling than it should. For a small ontology, that is fine. For an enterprise with hundreds of concepts, it starts to feel slow. I expect this to improve quickly, but if you are evaluating it now, factor in some time for the tooling to mature.
The version control story is also young. If two people are working on the ontology simultaneously, the conflict resolution is not as developed as you'd want. We've taken to nominating one person as the ontology owner for any given engagement, mainly to dodge this.
The migration path from existing business glossaries is uncertain. If you have already invested in a data catalogue, a glossary in Purview, or any other documentation system, you don't get an automatic import. There is some integration with Purview at the metadata level, but anything richer requires manual work. For organisations with mature data governance, this is a real cost to weigh.
The benefits depend on adoption by downstream tools. The ontology is only useful if the things consuming it actually respect it. Today, Microsoft's AI products are leading the way. Third-party tools are not really there yet. If your stack is heavily mixed, you might find the ontology only pays off for the Microsoft-native parts of it.
When to actually start using this
If you are a small organisation with a clean stack, mostly Microsoft, and you are about to build out your data agents and Copilot integrations, start now. The cost of defining an ontology while the data estate is small is low, and the benefit grows fast.
If you are a large organisation with a complex existing data estate, start with a focused domain. Pick one business area, customer, finance, supply chain. Build an ontology for just that domain. Use it to power one or two AI use cases. Learn what works. Then expand.
If you don't yet have a clear use case beyond "we should have one", I'd wait. The tooling is getting better fast, and the patterns are still being worked out. There is no prize for being early if you don't have a use to put it to.
We've started weaving ontologies into our business AI strategy work where it pays off, particularly around customer analytics and around any agent that needs to answer business-language questions. It is one of those technologies where the value compounds slowly at first and then suddenly becomes obvious. The teams that have invested early are getting much better results from their Copilot rollouts than the teams that didn't.
Where to start practically
If you want to try this without a big commitment:
- Pick a single business domain you understand well
- Identify the five to ten core concepts in that domain
- Write plain English definitions for each concept that the business agrees with
- Map each concept to its current source data, even informally
- Stand up a small Fabric IQ ontology with just those concepts
- Connect one AI use case, such as a data agent or a Copilot extension, to it
- Compare the answers with and without the ontology in place
That is a small experiment that can be done in a few weeks. It will teach you more about whether this fits your organisation than reading any number of strategy documents.
If you want a hand thinking through where ontologies fit in your data and AI estate, we work with Australian businesses on Microsoft Fabric and on Microsoft AI implementations, and we are seeing this become a bigger part of the conversation each month.