Journals Batch Summary Report

General Ledger◆ Seeded · Journals

Lists posted journal batches for a ledger, balancing segment, currency, and date range, with source, batch and posting dates, total entered debits and credits, sorted by batch within category.

Run note · BIP run High-volume GL extracts can exceed BI Publisher's online output limit and time out. Run it as a scheduled process (ESS) with output bursted to file or email rather than online preview, and bound it by ledger and period.

Sample build of the Journals Batch Summary 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

640

Batches

Sources

Late posting

Batch	Source	Batch Date	Posting Date	Entered Debit	Entered Credit
Sample	Sample	2026-04-30	2026-04-30	$1,240,500.00	$1,240,500.00
—	—	2026-03-31	2026-03-31	$842,150.75	$842,150.75
Sample	Sample	2026-02-28	2026-02-28	$96,400.00	$96,400.00
—	—	2026-01-31	2026-01-31	$1,005,233.10	$1,005,233.10
Sample	Sample	2025-12-31	2025-12-31	$58,720.40	$58,720.40
Sample	Sample	2026-04-30	2026-04-30	$1,240,500.00	$1,240,500.00

AI Analyst · active

reading

The report lists posted journal batches with source and posting dates, by category.

flag

22 batches from one source posted after period close — late batches that may belong in the prior period.

root cause & next step

Tighten the close cutoff for that source; a batch posted after close distorts the period comparison.

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
GL_JE_HEADERS	dimension	dimension
GL_JE_LINES	dimension	dimension
Entered Debit	measure	measure
Entered Credit	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.

General Ledger 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
GL_JE_BATCHES	14	2
GL_JE_HEADERS	34	2
GL_JE_LINES	26	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 Customers usually extend it into a close-monitoring view that flags unposted or late batches by source. Irvine rebuilds these on your data.