Journal Entry Reserve Ledger Report

Fixed Assets◆ Seeded · Reserve

The depreciation Assets calculated for the period — debiting depreciation expense and crediting accumulated depreciation — so finance can verify the depreciation run before it posts to the ledger.

Sample build of the Journal Entry Reserve Ledger 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

4,820

Assets depreciated

$1.20M

Period depreciation

Zero-deprn active

Asset	Category	Cost	Period Depreciation	Accum Deprn	Nbv
Sample	Computer-Hardware	$1,240,500.00	APR-26	Sample	Sample
—	Buildings	$842,150.75	MAR-26	—	—
Sample	Vehicles	$96,400.00	FEB-26	Sample	Sample
—	Furniture-Fixtures	$1,005,233.10	JAN-26	—	—
Sample	Machinery	$58,720.40	DEC-25	Sample	Sample
Sample	Computer-Hardware	$1,240,500.00	APR-26	Sample	Sample

AI Analyst · active

reading

The report lists the depreciation Assets calculated for the period — expense debited, reserve credited.

flag

36 active assets calculated zero depreciation this period — either fully depreciated or a method or life setup that stopped depreciation early.

root cause & next step

Check the method and life on those assets; an active asset that quietly stops depreciating understates expense.

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
FA_DEPRN_SUMMARY	dimension	dimension
FA_BOOKS	dimension	dimension
FA_CATEGORIES_B	dimension	dimension
Cost	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.

Fixed Assets 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
FA_DEPRN_DETAIL	16	2
FA_DEPRN_SUMMARY	Setup / configuration table — joined for reference, not exposed for analytics
FA_BOOKS	20	2
FA_CATEGORIES_B	5	10

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 Runs automatically after Calculate Depreciation; the common build turns it into a pre-post review with variance versus prior period and projected versus actual. Irvine rebuilds these on your data.