1. Define the business-critical data domains
Do not clean everything at once. Start with the information that affects current operations, accounting, reporting and migration risk.
Assign an owner to each domain.
- Properties and units
- Residents and leases
- Vendors
- Accounting
- Maintenance
- Documents
2. Profile the current data
Measure duplicates, missing fields, inconsistent formats and invalid codes so the cleanup effort is based on evidence.
Create a baseline before changing records.
- Duplicate rate
- Missing required values
- Invalid statuses
- Inconsistent codes
- Unmatched references
3. Establish normalization rules
Agree on naming, formatting, coding and status standards before mass cleanup begins.
Document how exceptions should be handled.
- Naming conventions
- IDs
- Status values
- Date formats
- Accounting codes
- Property hierarchy
4. Clean active records first
Prioritize information used in current workflows and reporting. Historical data can be addressed separately based on value and requirements.
Validate changes with business users.
- Active leases
- Current residents
- Open vendor records
- Open accounting items
- Active work orders
5. Reconcile reports after cleanup
Cleaning data should improve the outputs the business depends on. Compare key reports before and after changes.
Investigate unexpected differences.
- Occupancy
- Receivables
- Payables
- Security deposits
- Vendor totals
- Operating counts
6. Prevent the problem from returning
Define ownership, validation rules and process controls so the same inconsistencies are not recreated by everyday work.
Data governance does not need to be complicated, but it needs an owner.
- Required fields
- Ownership
- Validation
- Exception handling
- Periodic review