To replace manual reconciliation, you need a single model that understands both the structure of your business and the math that measures it. BigQuery measures builds directly on BigQuery Graph to deliver that unified foundation. By defining measures directly on the graph entities they describe, your BI dashboards and AI agents don’t just get a number they can trust — they get the relational context behind it. Finally, they can reliably answer both the what and the why.
“With BigQuery measures, we can now deliver self-documenting data products. Defining measures natively in BigQuery ensures Looker Dashboards, Conversational Agents and our Data team queries operate on identical logic, guaranteeing metric alignment across leadership and significantly reducing data team maintenance overhead.” – Amanda Reelick, Head of Data Enablement, Woolworths NZ
One model to answer both the what and the why
Let’s walk through an example. Suppose your retail bank’s risk analytics team spots a sudden spike in transaction activity in the Prague district. You need to know whether this represents healthy customer growth or concentrated risk exposure. Answering requires two pieces of evidence: how much money actually moved (the what), and which specific accounts it flowed through (the why). At most banks, getting that answer takes two teams, two tools, and two numbers that are rarely reconciled.
With BigQuery, your underlying data is already in place — whether in native tables or in a borderless Lakehouse. With a single schema, you can define a semantic model directly over your existing transaction, account, and district tables — with no data movement and no secondary system:
