Performance Analytics Export De-Identification with anonym.plus

Strip the names from an analytics export before it reaches a BI tool.

This task is the removal of identifiers from a people-data extract of appraisal metrics, so no one is named. GDPR Recital 26 places anonymous data outside the rules. anonym.plus marks each name on your device, so the numbers stay rich while the staff stay private.

When this applies

An analyst loads an HR extract into a BI dashboard. You strip the identifiers first, so the feed meets Recital 26 before it leaves the HR system.

How anonym.plus handles it

  1. Open the data extract in anonym.plus on your device.
  2. Local OCR reads a scanned report page.
  3. The tool flags names, emails, and IDs.
  4. Confirm the flags and keep the metric columns.
  5. Replace each identifier with a code.
  6. Save the clean feed locally.

What you need to provide

PII entity types detected

Categoryanonym.plus entity typeExample
NamesPERSONPavel Novak → ID_318
ContactEMAIL_ADDRESSp.novak@firm.com → [EMAIL]
OrgORGANIZATIONRetail Region 4 → [UNIT]
DatesDATE_TIMEhire 2017 → [TENURE]
LocationLOCATIONPrague store → [SITE]
DemographicNRPshift group B → [GROUP]

Compliance achieved

Anonymize performance analytics exports offline — see plans & start free →

Limitations & cautions

Quasi-identifiers can re-link a row. A rare mix of tenure, site, and role may match one person even with no name. Bucket such fields before you publish the dashboard.

Frequently asked questions

What is a quasi-identifier risk here?

A combination like tenure plus site plus role can match one person. Recital 26 treats that as non-anonymous, so bucket or coarsen those fields.

Does it keep the metric values?

Yes. Only names and contacts are coded, so the metrics stay rich for analysis.

Is the extract uploaded?

No. The tool works offline, so it stays on your device.