Objective: Ensure the number of records in the old and new systems match after migration.
How to do it:
Compare the total record count between the source and target systems for each table or dataset.
Example: If a table in the old system has 100,000 records, the same table in the new system should also have 100,000 records.
2. Field-Level Comparison :
Objective: Verify that data in individual fields (e.g., dates, numbers, strings) matches between the old and new systems.
How to do it:
Row-by-row comparison or use automated scripts/tools to compare specific fields (e.g., customer names, order amounts).
This process ensures that there are no discrepancies in field values or data types.
3. Data Summarization & Aggregation :
Objective: Perform data aggregation (e.g., sums, averages) and verify that results match between systems.
How to do it:
For tables with numeric data (like sales totals), compare aggregated sums or averages between the old and new systems.
Example: Total sales for a given period in the old system should match the total in the new system.
4. Record-Level Data Validation :
Objective: Validate that every individual record is correct and accurately transferred.
How to do it:
Compare specific records based on a unique identifier (e.g., customer ID, order ID).
Ensure there is no data loss and that each record in the source has a corresponding match in the target.
5. Referential Integrity Checks :
Objective: Ensure that relationships between records (e.g., foreign keys, references) are preserved after migration.
How to do it:
Verify that foreign key relationships in the old system remain valid in the new system (e.g., customer data linked to orders, employees linked to departments).
This process ensures data relationships are maintained correctly in the new system.
