Asset Additions Report

Fixed Assets◆ Seeded · Additions

Assets added or capitalized in a period, with cost, asset type, category, and the cost and expense accounts, totaled by balancing segment, asset type, and account.

Sample build of the Asset Additions 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

320

Additions

$8.40M

Added cost

No in-service date

Asset	Category	Cost	Cost Account	In-service	Source
Sample	Computer-Hardware	$1,240,500.00	$1,240,500.00	Sample	Sample
—	Buildings	$842,150.75	$842,150.75	—	—
Sample	Vehicles	$96,400.00	$96,400.00	Sample	Sample
—	Furniture-Fixtures	$1,005,233.10	$1,005,233.10	—	—
Sample	Machinery	$58,720.40	$58,720.40	Sample	Sample
Sample	Computer-Hardware	$1,240,500.00	$1,240,500.00	Sample	Sample

AI Analyst · active

reading

The report lists assets added or capitalized in the period with cost and accounts.

flag

Five additions have no in-service date, so they won't start depreciating until one is set.

root cause & next step

Set the in-service dates; an addition with no in-service date sits as cost without depreciation.

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_CATEGORIES_B	dimension	dimension
FA_DISTRIBUTION_HISTORY	dimension	dimension
Cost	measure	measure
Cost Account	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_CATEGORIES_B	5	10
FA_DISTRIBUTION_HISTORY	6	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 Teams add a CIP-to-additions bridge so capitalizations from Projects and Payables trace back to their source. Irvine rebuilds these on your data.