How to Transform Raw Data for Oracle Cloud ERP Migration (A Complete Guide)
Migrating to Oracle Cloud ERP is one of the most strategic steps for any organization. But the success of your implementation heavily depends on one factor:
👉 How well you transform raw legacy data into Fusion-compatible, business-ready data.
In this blog, we’ll break down the complete transformation process, best practices, and the exact steps teams follow during Oracle Cloud data migration.
1. Understand Your Source Data
Every migration begins with knowing what you currently have.
Key activities:
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Identify all legacy systems (EBS, SAP, Tally, custom apps, spreadsheets).
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Conduct data profiling to check:
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Missing fields
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Duplicate records
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Invalid formats
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Data inconsistencies
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Determine who owns which data.
Why this matters:
You can’t clean or transform what you don’t understand.
2. Create Source-to-Target Mapping (S2T)
This is the backbone of the migration.
Build a smart mapping sheet to define:
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Legacy Field → Oracle Cloud Field
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Data type & length
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Transformation logic (UPPERCASE, CONCAT, SPLIT)
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Default values
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Lookup mappings (Payment Terms, UOM, Tax Regimes)
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Business rules
Examples:
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Legacy Customer Category → Oracle Customer Class
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Old GL Code → Fusion CoA Segment Values
This ensures the team transforms data exactly the way Oracle Cloud expects.
3. Apply Business & Functional Validation Rules
Oracle Cloud has strict validations and dependencies.
Common rules include:
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Mandatory fields (Supplier Name, Payment Term, Legal Entity)
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Valid lookup values
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Accounting date rules
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Parent–child relationships (Customer Account ↔ Sites ↔ Site Uses)
Run multiple validation passes before moving ahead.
4. Data Cleansing
Raw data is almost always messy.
Clean it using:
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Duplicate removal
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Standardizing date formats
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Removing unwanted characters
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Splitting merged fields
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Normalizing text values (UOM, State, Country)
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Merging records referring to the same entity
Tools that help: Excel, SQL, Python, Oracle Integration Cloud ETL pipelines.
5. Data Enrichment
Legacy systems often lack fields required by Fusion.
Common enrichment examples:
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CoA segments (BU, Department, Natural Account)
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Supplier Payment Methods
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Customer Account Relationships
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Item Templates & Categories
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GST/VAT/TAX details
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Subinventory & Locator information
You can derive values, use defaults, or conduct business workshops to collect missing information.
6. Apply Transformation Rules
This is where the actual conversion happens.
Typical transformation operations:
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Convert dates →
YYYY-MM-DD -
Convert text → uppercase
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Build address lines with concatenation
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Split legacy GL code → segment-wise Fusion CoA
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Map legacy lookup → Oracle lookup
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Conditional logic
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If Country = “IN”, populate GST fields
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If Item Type = “SERV”, exclude UOM conversions
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The output is your final staging dataset, ready for Oracle templates.
7. Load Data into Oracle FBDI Templates
Fill Oracle’s official import templates for:
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Customers
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Suppliers
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Items & Item Category Assignments
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Chart of Accounts
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Subledger Transactions
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Open AP/AR balances
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Bank & Branch
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On-hand Inventory
Important tips:
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Keep column order unchanged
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Remove formulas
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Use correct date formats
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Avoid blank columns not supported by template
8. Pre-Load Validations
Before loading:
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Ensure lookup values match Oracle Cloud
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Validate CoA combinations
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Check duplicates
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Validate parent-child dependencies
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Run template-specific checks
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Verify CSV structure
Good tools include BI Publisher reports, SQL scripts, and automated Python validators.
9. Load Data to Oracle Cloud
Choose the correct loading method:
▶ FBDI (File-Based Data Import) — Most Common
Upload ZIP → Run Import ESS Jobs
Best for large migration data volumes.
▶ ADFDI
Used for small master data, quick loads, and corrections.
▶ REST/SOAP APIs
Great for incremental or integration-style loads.
▶ OIC or ODI
For automated ETL-style migrations with transformations.
10. Post-Load Reconciliation
This step ensures everything is correctly migrated.
Perform:
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Record count validation (Source vs Loaded)
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Financial balancing (Opening balances, subledgers)
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Master data sample checks
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Error correction & re-loads
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Business sign-off
Use:
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OTBI reports
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Data import audit logs
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BI Publisher reconciliation reports
End-to-End Data Transformation Flow
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Extract raw data
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Profile & analyze
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Map fields to Fusion
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Clean & enrich
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Transform into Fusion-ready format
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Load via FBDI/ADFDI/API
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Validate & reconcile
Conclusion
Data transformation is not just an ETL activity—it's a strategic process that ensures clean, compliant, and business-ready data enters Oracle Cloud ERP. A strong transformation framework directly impacts the success of the implementation, user adoption, and system stability.
