Trial Balance - Average Balances

General Ledger◆ Seeded · Trial balance

Provides ending and average balances for selected accounts based on a specified effective date, in ledger currency or foreign-entered, with period, quarter, and year average-to-date 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 Trial Balance - Average Balances — 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

1,420

Accounts

$1.20B

Avg balance

No daily balances

Account	As-of Date	Ending Balance	Period Avg	Qtd Avg	Ytd Avg
1000-2100-000	2026-04-30	$1,240,500.00	APR-26	Sample	Sample
1000-5400-000	2026-03-31	$842,150.75	MAR-26	—	—
1000-1410-000	2026-02-28	$96,400.00	FEB-26	Sample	Sample
2000-2100-000	2026-01-31	$1,005,233.10	JAN-26	—	—
1000-6300-000	2025-12-31	$58,720.40	DEC-25	Sample	Sample
1000-2100-000	2026-04-30	$1,240,500.00	APR-26	Sample	Sample

AI Analyst · active

reading

The report shows ending and average balances as of an effective date.

flag

12 accounts show no average because daily balances aren't tracked for their ledger.

root cause & next step

Enable average daily balances on those ledgers; average-balance reporting needs the daily balance store populated.

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_BALANCES
GL_DAILY_BALANCES
GL_CODE_COMBINATIONS

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_DAILY_BALANCES	dimension	dimension
GL_CODE_COMBINATIONS	dimension	dimension
Ending Balance	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_BALANCES	Setup / configuration table — joined for reference, not exposed for analytics
GL_DAILY_BALANCES	Setup / configuration table — joined for reference, not exposed for analytics
GL_CODE_COMBINATIONS	7	61

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 Requires average daily balances; usually re-cut as a trend rather than the seeded as-of listing. Irvine rebuilds these on your data.