Data migration is often the riskiest part of a new software rollout because old spreadsheets contain years of exceptions, duplicates and inconsistent formats. Importing everything exactly as it exists may preserve the problems the new system was meant to solve. Migration should be treated as a controlled data-quality project.
What this workflow needs to solve
The new database usually has stronger rules than spreadsheets: unique IDs, required dates, valid course references and normalized statuses. Old files may contain multiple spellings for the same course, blank registration numbers, mixed date formats or formulas instead of fixed values. These differences need explicit decisions.
Recommended workflow
Inventory source files, decide the cutoff date, create a field mapping, standardize master values, remove or merge duplicates, validate required fields, import into a test environment and reconcile totals. Obtain sign-off on a sample before the final production migration.
Features that matter in day-to-day use
- Source-file inventory and ownership
- Field-by-field mapping document
- Master-data standardization
- Duplicate detection and merge rules
- Date, phone and identifier validation
- Test import with error report
- Financial/opening balance reconciliation
- Final backup and migration sign-off
Implementation approach
Keep original source files read-only as evidence. Perform migration with scripts that can be rerun rather than manual copy/paste where volume is significant. Produce counts before and after import by class, course, branch or status so missing records are visible.
What to measure after launch
- Rows rejected during test import
- Duplicate records resolved before go-live
- Difference in financial opening balances
- Records missing mandatory identifiers
- Post-launch corrections traced to migration
Common mistakes to avoid
Do not clean data directly in the only copy of the source file. Avoid importing unused columns simply because they exist. Never assume Excel display formatting reflects the stored value, especially for dates and long numeric identifiers.
Choosing the right solution
Migration quality matters more than migration speed. A smaller verified dataset can be expanded safely; a fast incorrect migration undermines user trust in every report produced by the new system.