What-If Depreciation Analysis Report

Fixed Assets◆ Seeded · Depreciation

Projected depreciation under hypothetical changes to method, life, or cost — without changing the asset — so finance can model the expense impact before committing.

Sample build of the What-If Depreciation Analysis 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 modeled

$14.00M

Current annual deprn

-$1.80M

Scenario delta

Asset	Current Method	Scenario Method	Current Deprn	Scenario Deprn	Delta
Sample	Standard	Standard	Sample	Sample	Sample
—	Corporate	Corporate	—	—	—
Sample	Standard	Standard	Sample	Sample	Sample
—	Default	Default	—	—	—
Sample	Standard	Standard	Sample	Sample	Sample
Sample	Standard	Standard	Sample	Sample	Sample

AI Analyst · active

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The report projects depreciation under hypothetical method, life, or cost changes without touching the asset.

flag

The modeled scenario cuts annual depreciation by $1.8M — a material expense and tax impact before any decision is made.

root cause & next step

Model the full-year and tax effect before changing methods; a method change ripples through expense, net book value, and deferred tax.

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_METHODS	dimension	dimension
FA_CATEGORIES_B	dimension	dimension
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.

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_BOOKS	20	2
FA_METHODS	8	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 Modeling is more useful interactive; the build turns it into a scenario tool with side-by-side method comparison. Irvine rebuilds these on your data.