GL Journal Unposted Report

General Ledger

Every journal still unposted in a ledger — who entered it, its source, status, and amount — so the close team can clear the queue before period end and make sure nothing real is left out of the balances.

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 GL Journal Unposted Report — reconciled, and rendered tool-neutral so it runs in Power BI, ThoughtSpot, or Tableau.

Sample build · illustrative

Filters

P Ledger Id

1003

P Period Name

FEB-26

Unposted batches

$2.40M

Unposted value

Error status

Ledger	Journal Batch	Source	Category	Status	Entered Amount	Created By
US Primary	Sample	Sample	Computer-Hardware	Open	$1,240,500.00	Sample
EU Primary	—	—	Buildings	Posted	$842,150.75	—
US Primary	Sample	Sample	Vehicles	Validated	$96,400.00	Sample
UK Primary	—	—	Furniture-Fixtures	Open	$1,005,233.10	—
US Primary	Sample	Sample	Machinery	Paid	$58,720.40	Sample
US Primary	Sample	Sample	Computer-Hardware	Open	$1,240,500.00	Sample

AI Analyst · active

reading

The report reads GL_JE_HEADERS and GL_JE_BATCHES for anything not in Posted status, with the action log explaining why each is held.

flag

Six batches sit in error rather than simply unposted — they failed a funds or validation check, so they will not post on their own and the $2.4M includes amounts that cannot close as-is.

root cause & next step

Work the six errored batches first (the action log gives the reason), then post the rest; an errored batch left to the last day is the classic close-day surprise.

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

GL_LEDGERS
GL_JE_HEADERS
GL_JE_BATCHES
GL_JE_ACTION_LOG
GL_LOOKUPS
GL_JE_CATEGORIES_B
GL_JE_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.

SQL data set · genericized · parameterized · no hardcoded segments

Show / hide SQL

select 
gl.description  ledger
,gjb.name batch_name
,gjh.name journal_name
,gjh.currency_code currency
,gjh.running_total_dr debit_amount
,gjh.running_total_cr credit_amount
,src.user_je_source_name je_source
,gjc.je_category_key je_category
--,gll.description|| '-' ||GJB.ERROR_MESSAGE status
,(CASE WHEN gjb.ERROR_MESSAGE is null  THEN gll.description ELSE gjb.ERROR_MESSAGE END)status
,gjal.user_id journal_preparer
,gjh.period_name
,gl.ledger_id
from
 GL_LEDGERS gl
,GL_JE_HEADERS gjh
,gl_je_batches gjb
,gl_je_action_log gjal
,gl_lookups gll
,GL_JE_CATEGORIES_B gjc
,gl_je_sources src
where 1=1
and gl.ledger_id = gjh.ledger_id
and gjh.je_batch_id = gjb.je_batch_id
and gjal.action_code(+)='CREATED'
and gjh.je_batch_id = gjal.je_batch_id (+)
and gll.lookup_type ='MJE_BATCH_STATUS'
and gjb.status = gll.lookup_code(+)
and gjh.je_category =gjc.je_category_name (+)  
and gjh.je_source = src.je_source_name (+)
and gjb.status <>'P'
and (gjh.period_name  in  (:p_period_name) or 'All' IN (:p_period_name||'All'))
and (gl.ledger_id in (:p_ledger_id) or  'All' IN (:p_ledger_id||'All'))
--and gjb.je_batch_id=84203
order by gl.description,gjh.period_name,src.user_je_source_name,gjc.je_category_key,gjb.name,gjh.name

:p_ledger_id :p_period_name

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_BATCHES	dimension	dimension
GL_JE_ACTION_LOG	dimension	dimension
GL_LOOKUPS	dimension	dimension
Entered 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.

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_LEDGERS	10	104
GL_JE_HEADERS	34	2
GL_JE_BATCHES	14	2
GL_JE_ACTION_LOG	Setup / configuration table — joined for reference, not exposed for analytics
GL_LOOKUPS	3	1
GL_JE_CATEGORIES_B	Setup / configuration table — joined for reference, not exposed for analytics
GL_JE_SOURCES	Setup / configuration table — joined for reference, not exposed for analytics

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.