Data Quality
Senzo applies a series of validation checks before writing any data to the database. Understanding these checks helps you prepare clean files and troubleshoot issues quickly.Validation layers
Data goes through three validation layers during upload:- Structural validation — file can be parsed, headers detected, encoding correct
- Field validation — required fields present, values in expected formats
- Business validation — values make operational sense, no conflicts with existing data
Required field checks
| Field | Check |
|---|---|
date | Present, parseable as a date, not null |
unit_name | Present, not empty |
provider_type_name | Present, not empty |
headcount | Present, non-negative integer |
Format checks
| Field | Check |
|---|---|
| All numeric fields | Non-negative values (headcount, FTE, hours cannot be negative) |
fte | Should not exceed positions by more than 10% |
vacant_fte | Should not exceed positions |
overtime_hours | Should not exceed worked_hours |
absence_hours | Should not exceed scheduled_hours |
Matching checks
| Field | Check |
|---|---|
unit_name | Must match a unit configured in Settings (case-sensitive) |
provider_type_name | Must match a provider type configured in Settings (case-sensitive) |
facility_name | If present, must match a facility configured in Settings |
Duplicate detection
Senzo detects duplicate rows — multiple rows with the same date, unit, and provider type combination. Duplicates are flagged in the Review stage. You must deduplicate before proceeding.Overlap detection
When uploading data for periods that already exist in the system, Senzo detects the overlap and presents options: Update, Skip, or Cancel. See Upload History for details.Best practices for clean data
Before exporting from your source system:- Confirm unit and provider type names match your Senzo Settings exactly
- Remove any summary, total, or blank rows
- Ensure all date fields are consistently formatted
- Exclude agency staff from headcount — track their hours separately in
agency_hours
- Open the file and do a quick visual scan for obvious issues
- Check the date range looks correct
- Verify row count is approximately what you expect
- Check the imported row count in the Review stage matches your expectation
- Navigate to Data Explorer after upload to verify data appears correctly
- Run Pulse Intelligence to confirm the new data generates expected insights
Data quality and metric reliability
Metrics are only as reliable as the underlying data. Common data quality issues and their effects:| Issue | Affected metrics |
|---|---|
| Positions not maintained | Vacancy rate unreliable |
| Agency hours included in worked hours | OT rate overstated |
| Scheduled hours missing | Absence rate unavailable |
| Inflow/outflow not tracked | All workforce flow metrics unavailable |
| Headcount includes agency staff | Vacancy rate understated |