Turnover Analytics De-Identification with anonym.plus

Strip identifiers from attrition data so trends can be shared without naming staff.

Turnover de-identification is the removal of personal identifiers from an attrition dataset. GDPR Recital 26 excludes data that no longer points to a person. anonym.plus marks names and IDs on your device, so the trend lines stay usable while the individuals behind each leaver are hidden.

When this applies

An attrition export ties leave dates and reasons to named former staff. You strip the identifiers so analysts see patterns without identifiable rows.

How anonym.plus handles it

  1. Open the dataset in anonym.plus on your device.
  2. The app flags named leavers and IDs.
  3. Local OCR reads a scanned chart export.
  4. Keep the leave-reason and date-band columns.
  5. Replace each identifier with a steady label.
  6. Save the clean dataset locally.

What you need to provide

PII entity types detected

Categoryanonym.plus entity typeExample
NamesPERSONleaver row → [LEAVER]
IdentifiersNATIONAL_IDstaff no. 51140 → [STAFF_ID]
NRPNRPjob title → [TITLE]
OrgORGANIZATIONdepartment → [DEPT]
DatesDATE_TIMEleft 2024-Q3 → [BAND]
ContactEMAIL_ADDRESSwork email → [EMAIL]

Compliance achieved

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Limitations & cautions

Trends are aggregate, yet a rare title plus a date band can re-identify one leaver. Where a slice holds a single person, that pattern may name them. Band such fields before you share the analysis.

Frequently asked questions

Can attrition trends still expose a person?

Yes, when a slice holds one leaver. Band dates and group rare titles so a pattern cannot single out a worker.

What stays after de-identification?

The reason and date-band columns. The app removes named rows and staff IDs.

Is the dataset uploaded?

No. The app is fully offline, so it stays on your machine.