Scheduled or Detailed? Choosing the Right Data Validation Approach in Oracle Fusion AI Data Platform (FDI)

Apr 22

2 min read

Updated: Oct 14

Oracle Fusion AI Data Platform gives you two ways to validate your data — but most teams either use only one or do not know when to use which.

Getting this right makes the difference between catching a data issue early and discovering it in a board meeting.

Scheduled Validation — your ongoing safety net

Automated. Set it up once, define your subject areas, metrics, and parameters — and the platform runs it weekly, monthly, or quarterly.

It compares your warehouse data against OTBI and stores results in the Data Validation Workbook in your OAC Catalog automatically.

Use this when you want to:

Run automated reconciliation for audit purposes on a regular cadence
Monitor data quality across HCM, ERP, SCM, and CX without manual effort
Build a paper trail of validation history for compliance or governance reviews

One important note:

Ensure the user assigned to run Scheduled Validation is part of the Integration Specialist Group in FDI and has access to the Common folder in Catalog. Without this, the output will not appear where your team expects it.

Detailed Validation — your investigation tool

On-demand. Pick a specific subject area, metric, and column set — and run it immediately.

The platform shows you:

A side-by-side comparison of warehouse values vs. OTBI
A Summary view of matched vs. mismatched records
A Details view that drills down to actual differences row by row

Use this when you want to:

Investigate a specific number that does not look right in a dashboard
Drill into why a metric is mismatching after a Scheduled Validation flags it
Validate a specific period or business unit before a leadership review

Important considerations for both

Both modes use the same predefined subject areas and metrics in the validation framework
Custom subject areas are not supported within the built-in validation feature
Differences may still occur due to variations in aggregation or data grain between OTBI and the warehouse

The decision — simplified

Regular ongoing data quality monitoring → Scheduled
Something looks wrong in a specific report → Detailed
Pre-go-live sign-off across all pillars → Scheduled
Post-pipeline investigation of a specific metric → Detailed
Audit trail for compliance → Scheduled
Quick ad-hoc check before a business review → Detailed

Want this working in your Oracle environment?

Data validation works best when the right checks are configured correctly and teams know how to act on the results.

CLODAIN's Oracle FDI Support Services help configure validation approaches and maintain reliable data quality checks as your environment evolves.

→ See where you stand: Take the free Oracle FDI Readiness Assessment.

→ Or talk it through: Book a free introductory call to know more.