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

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

  
  

At CLODAIN, we help organizations configure both validation approaches correctly so that data quality is built into your FDI operations — not bolted on after something breaks.

Set up a free introductory call to know more.