Production-shaped data. PII-safe by default.
Define a masking policy once and DataMaker anonymises every run against it, deterministically, so your data stays joinable across tables. GDPR-friendly. Audit-ready.
Brittle anonymisation slows everything down.
Hand-rolled scripts drift
Every team writes its own anonymiser. They miss new PII fields, fail audits, and break with each schema change.
Production copies are a liability
A snapshot of prod for testing is a GDPR incident waiting to happen, even with a 'safe' subset.
Masked data stops joining
Naive anonymisation renames the same customer differently in each table. Your foreign keys survive; your test scenarios don't.
A policy per field. Consistent across every table.
Write the policy once. Every run masks against it, and the result still behaves like your production data.
Pick the right one per field
Replace with a realistic fake, hash, tokenise, redact, or keep the format valid. 29 PII classes are recognised, from IBAN and VAT to VIN and IMEI.
One person, one masked identity
Masking keys on what a field is, not what the column is called. The same customer masks identically in NAME1, customer_name and full_name, so joins hold.
Same shape, different identity
A masked IBAN still passes checksum. Emails stay emails, dates stay dates, and date shifts keep the gaps between them intact.
Re-identify only on purpose
Tokenised values can be mapped back for reconciliation, behind a permission of its own and a one-time human approval. Every reversal is recorded.
Define the policy. Every run honours it.
DataMaker substitutes a realistic, locale-correct value, records what it did in a run report that contains no raw data, and keeps the mapping only where you asked for it.
- 29 PII classes recognised out of the box
- Replace, hash, tokenise, redact, preserve-format
- Run report as your compliance artefact
- Reversal behind a separate permission
Realistic test data without the GDPR risk.
Start with a pilot on your own infrastructure, against one real system.