The scope of a typical HR data migration includes: active employee records (personal details, job information, employment history, compensation history), historical records (leavers from the past two to three years for reporting purposes), absence records (current year and, ideally, two prior years for absence pattern tracking), performance review history (at least the two most recent cycles), compensation records (salary history, bonus history), and organisational structure (job levels, departments, cost centres, reporting lines). Benefits and payroll history may also be in scope depending on whether the new system includes these modules.
The data quality work required before migration is the most time-consuming part of most migrations. Common data quality issues in HR source systems include: employees with missing or inconsistent job titles (the same role called different things by different managers); absence records with gaps or errors (periods of leave not recorded, dates that overlap or are inconsistent); employees on the wrong grade or job family because the classification system was never consistently applied; missing date of birth or right-to-work information; and contractor records mixed in with employee records without clear differentiation. These issues must be resolved before migration, not during or after - migrating dirty data into a new system simply recreates the problems in a new location.
A staged migration approach reduces risk. The first stage is a data extract and profiling exercise: pull all data from the source, run automated validation checks to identify missing fields, format inconsistencies and logical errors, and produce a data quality report. The second stage is remediation: working through the data quality report, correcting errors, and filling gaps (which often requires conversations with managers or employees to verify correct information). The third stage is the migration rehearsal: loading cleaned data into a test environment and validating the result against the source. Two or three rehearsals are standard, with each revealing additional issues. The final stage is the cutover migration: the production go-live load, validated against the rehearsal results and the source system.
Data governance after migration is as important as the migration itself. A clean dataset at go-live will deteriorate if there are no processes to maintain its quality. This requires: mandatory system updates at each employment lifecycle event (hire, role change, leave, termination); data validation rules in the HRIS that prevent records being saved without required fields; regular data quality reports that flag records with anomalies; and a named data stewardship responsibility within HR.
Key Points: HR Data Migration
- Scope: Active and historical employees, absence records, performance history, compensation data, organisational structure.
- Data quality: Source data quality is almost always lower than expected; remediation must happen before migration, not after.
- Staged approach: Profile, remediate, rehearse (2-3 times), then cutover - each rehearsal reveals additional issues.
- Common issues: Inconsistent job titles, absence gaps, wrong classifications, missing dates, contractor/employee mixing.
- Post-migration governance: Clean data degrades without process discipline; mandatory updates and data quality reports maintain quality over time.
How HR Data Migration Works in Treegarden
HR Data Migration in Treegarden
Treegarden provides structured data migration support as part of every implementation. Standard import templates cover all core data objects. The data validation engine checks uploaded files against over 50 rules before loading, returning a detailed exception report identifying issues that need remediation. A sandbox environment allows multiple migration rehearsals before go-live. Post-migration, automated data quality checks flag records with anomalies on an ongoing basis.
Related HR Glossary Terms
Frequently Asked Questions About HR Data Migration
At minimum, migrate all active employee records with their current job and personal details, current compensation, current leave balances, and current reporting line. For historical reporting continuity, include leavers from the past 12-24 months, absence records for the current and prior two years, and performance review records for the most recent two cycles. Payroll history and historical compensation detail are higher effort to migrate but valuable if the new system includes analytics - otherwise they can be archived in the old system or in a spreadsheet for the rare occasions historical data is needed.
Data extraction and initial profiling can be done in one to two weeks for most organisations. Data remediation is the variable element - a small organisation with relatively clean data may complete remediation in two to four weeks; a large organisation with data distributed across multiple legacy systems, years of inconsistent management and minimal historical data governance may take two to three months of intensive clean-up work. Migration rehearsals add two to four weeks. Total elapsed time from start of data work to successful cutover is typically six to twelve weeks for a mid-sized organisation, assuming dedicated resource is available for the data quality work.
Yes, and this is the most common migration source for small and growing organisations that have not previously used a dedicated HRIS. Spreadsheet-sourced migrations present specific challenges: data that was captured informally over time in inconsistent formats, calculations that lived in spreadsheet formulas rather than being stored as values, absence of historical change records, and data spread across multiple files with no single source of truth. The process is the same as for system-to-system migration (extract, profile, remediate, rehearse, load) but the remediation step typically requires more manual intervention to standardise formats and resolve inconsistencies.