Key takeaways
- Clean data before migration, not after import fails.
- Remove duplicates and standardise names, dates, and codes.
- Fill missing fields and archive records outside the active year.
- Treat field mapping as a migration risk, especially for rooms, staff codes, and curriculum entries.
What should be cleaned first?
Start with duplicates, inconsistent formats, and missing fields. Duplicate records can make one teacher, room, or curriculum entry appear as more than one object. Inconsistent names, dates, and codes make matching harder during migration.
Do this before migration so the mapping step works from cleaner source data.
What cleanup tasks are grounded for this topic?
Keep the cleanup list close to the verified migration guidance.
- Remove duplicate records
- Standardise names
- Standardise dates
- Standardise codes
- Fill missing fields
- Archive records not relevant to the active year
How should planners prepare data for migration?
The aim is to reduce avoidable mapping and validation failures.
- Identify the current records needed for the active year.
- Archive records that are not relevant to that year.
- Remove duplicates from the active data set.
- Standardise names, dates, and codes.
- Fill required missing fields where the source data supports it.
- Review field mappings before running the migration test.
Why does field mapping need extra attention?
Field mapping is a common break point because old and new structures do not always mean the same thing. The verified risks include supplier-specific structures, inconsistent field definitions, room structures that do not map, differing staff codes, and duplicate curriculum entries.
Imports can also fail validation. That failure should send the team back to the mapping and cleanup work, not into guessing.
Why migrate current records first?
Migrating all legacy data increases the amount of old structure that must be cleaned and mapped. The verified guidance is to migrate current records and archive what is not relevant to the active year.
This keeps the migration focused on the timetable data planners need to validate for the live process.
What question does this guide answer?
This guide answers one question: what should planners clean before timetable migration? It does not define every possible data model.
The grounded scope is duplicates, standard formats, missing fields, active-year records, and field mappings that can break validation.
Questions planners ask about data cleanup
Should we migrate all legacy records?
The verified guidance says to migrate current records and archive anything not relevant to the active year.
Which formats need standardising?
The sourced cleanup guidance names names, dates, and codes as formats that should be standardised before migration.
Where do timetable migrations break?
They often break at field mapping: room structures, staff codes, curriculum entries, field definitions, and import validation.