Recognize Revenue Execution Report

Receivables◆ Seeded · Receivables balances

Reports the outcome of the revenue-recognition process for transactions that use invoicing and revenue-scheduling rules, listing the revenue distributions created and any unprocessed or partially processed transactions.

Sample build of the Recognize Revenue Execution Report — reconciled, and rendered tool-neutral so it runs in Power BI, ThoughtSpot, or Tableau.

Sample build · illustrative

Filters

Period

FEB-26

Ledger

US Primary

Currency

USD

2,400

Transactions

$14.00M

Recognized

Errored

Transaction	Rule	Scheduled	Recognized	Deferred	Status
Sample	Standard	Sample	Sample	Sample	Open
—	Corporate	—	—	—	Posted
Sample	Standard	Sample	Sample	Sample	Validated
—	Default	—	—	—	Open
Sample	Standard	Sample	Sample	Sample	Paid
Sample	Standard	Sample	Sample	Sample	Open

AI Analyst · active

reading

The report reports the revenue-recognition run for transactions using invoicing and revenue-scheduling rules.

flag

36 transactions errored in recognition — revenue that should have been recognized or deferred this period didn't post.

root cause & next step

Fix the revenue rule or schedule on the errored transactions; a recognition error is a period-end revenue misstatement waiting to be caught.

Illustrative data. The live interactive version — drill-through, filters, export, and the AI Analyst — runs on your warehouse. See it live →

This is the report's BI Publisher data model — the SQL data set BI Publisher runs against Oracle tables to produce the output. The same SQL becomes a dbt model in your warehouse, so one definition drives both the formatted report and the analytics layer.

Data sources

How it interconnects: this data set reads the physical tables above. Those same tables surface in OTBI as subject areas and in BICC as PVOs — three lenses on one source. Open any table to trace its subject areas and View Objects.

The SQL data set is authored to this report's exact spec during the build and ships as the BI Publisher data model plus a matching dbt model — one definition, both layers.

The data-warehouse model — one fact surrounded by conformed dimensions (what you slice by) and measures (what you aggregate), expressed as dbt so it migrates with you. Grain: one row per source transaction.

●— fact → dimension join

Element	Type	Definition
AR_DISTRIBUTIONS_ALL	dimension	dimension
RA_CUST_TRX_LINE_GL_DIST_ALL	dimension	dimension
Amount	measure	measure

Runs on your cloud warehouse — Snowflake, BigQuery, Redshift, or Synapse on AWS, Google Cloud, Azure, or any provider. Reconciled to the source control total — 0% variance by design. You own the code, the model, and the data.

How the data gets here: a BICC bulk extract of the source tables above, on the same pattern for every report. See the extraction pattern & data flow →

See the complete model

How this report's fact and dimensions fit the full picture, via conformed keys.

Receivables data model →Enterprise model →

Every source object behind this report. Each linked table has its own page with full column descriptions, drawn from the Oracle BICC lineage and articulated for practitioners.

Table	Reporting columns	Subject areas
RA_CUSTOMER_TRX_ALL	58	16
AR_DISTRIBUTIONS_ALL	12	6
RA_CUST_TRX_LINE_GL_DIST_ALL	11	2

Reporting columns = fields the report selects that are exposed as analytics attributes; subject areas = the OTBI subject areas the table appears in. Setup and configuration tables (master data, ledger and book setup, lookups) are referenced by the report's joins but aren't exposed as analytics columns or subject areas — that's expected, not a gap.

Customization note The exception list is the useful part; the build promotes unprocessed transactions into a monitored queue tied to the close calendar. Irvine rebuilds these on your data.