Your team did the work correctly. You modeled the entities in Microsoft Fabric IQ, defined the relationships, bound them to your OneLake tables and watched the graph come together. The ontology is live. Then in a review meeting, someone points at the customer count and says the number is too high.

They're correct. It is too high. The modeling was right, but the number skewed because the same customer is sitting in your source data three or four times under slightly different names.

This caused Fabric IQ to turn each of those rows into its own node. The ontology did not create the problem, but instead it exposed it.

This is the wall that many analytics teams are hitting. The platform is ready, yet the data underneath is not.

The identity gap does not reveal until the build is already done.

Fabric IQ binds entity definitions to rows in your OneLake Delta tables. That's the right conventional design choice. Fabric IQ's job is to govern meaning and relationships, not to clean your data. It does mean the binding step acts in good faith and proceeds deterministically without question. One row becomes one node, but no resolution happens in between.

This is fine, but what if your customer table holds six rows for the same company under six name variants? Fabric IQ creates six customer nodes. Your Data Agents, your graph traversals and your Power BI reports all start from those six nodes. Every downstream pipeline is now subtly, confidently wrong, so counts inflate, features split, and agents reason from partial views.

The binding step is deterministic. Resolution needs to happen before binding, not after.

An entity resolution gap does not show up as a red flag on a dashboard. It shows up as numbers that are generated deterministically, and almost right. A customer count is slightly inflated. A top-accounts list splits one real account into three smaller ones without realizing they are the same, so that account does not crack the top ten. No alerts are created. A Data Agent answers a question about a supplier using one of its duplicate records carrying missing records and context, quietly omitting the important bits about the supplier's checkered past.

Reviews continue unquestioned until someone who knows the business reads the number and says "that's not right." By then the ontology, and the graph underneath, has run for weeks, with models trained on the split features. Reports have shipped across the business, leading to new ill-formulated sales targets, a service purchased from a risky supplier, and an incorrect customer count in the board deck.

The correct sequence is simple. Resolve entities first, then bind the ontology to resolved, canonical records. Downstream, you can now build agents and reports on a real-world foundation that doesn't need to be rebuilt.

Quantexa Unify is the workload that goes first. It runs natively inside your Fabric tenant, reads the raw Delta tables from OneLake, resolves fragmented records into canonical confidence-scored entities, and writes them back to OneLake, before Fabric IQ ever binds. Your data never leaves the tenant. When your team then defines entity types and creates bindings, Fabric IQ materializes every node as a single, resolved, stable-ID entity.