Asset Register Report

Fixed Assets◆ Seeded · Asset detail

Account details for each asset with its financial data — cost, revaluation, depreciation, depreciation reserve, and net book value — plus the asset transactions for the fiscal year, ordered by transaction number.

Sample build of the Asset Register 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

Active assets

$62.00M

Net book value

240

Fully deprec., in use

Asset	Category	Cost	Accum Deprn	Net Book Value	In-service
Sample	Computer-Hardware	$1,240,500.00	Sample	CORP	Sample
—	Buildings	$842,150.75	—	TAX FEDERAL	—
Sample	Vehicles	$96,400.00	Sample	CORP	Sample
—	Furniture-Fixtures	$1,005,233.10	—	IFRS	—
Sample	Machinery	$58,720.40	Sample	TAX STATE	Sample
Sample	Computer-Hardware	$1,240,500.00	Sample	CORP	Sample

AI Analyst · active

reading

The report reads each asset's cost, accumulated depreciation, and net book value with its transactions.

flag

240 assets are fully depreciated but still in service — carried at zero net book value while still in use and often still incurring maintenance.

root cause & next step

Review them for revaluation or life extension; a fully depreciated active asset understates the asset base.

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_BOOKS	dimension	dimension
FA_DEPRN_SUMMARY	dimension	dimension
FA_CATEGORIES_B	dimension	dimension
FA_TRANSACTION_HEADERS	dimension	dimension
Cost	measure	measure
Net Book Value	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_ADDITIONS_B	15	9
FA_BOOKS	20	2
FA_DEPRN_SUMMARY	Setup / configuration table — joined for reference, not exposed for analytics
FA_CATEGORIES_B	5	10
FA_TRANSACTION_HEADERS	7	5

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 Ships as a fixed PDF per book; teams rebuild it as a roll-forward (beginning + additions − retirements ± adjustments = ending) with drill from the asset to its journals. Irvine rebuilds these on your data.