Do we need a full data governance platform before we start?

No. Start with tags in your warehouse (BigQuery policy tags, Snowflake masking policies, Unity Catalog) and wire them via Terraform. Add a lightweight catalog like DataHub or OpenMetadata for lineage and tagging. You can layer Collibra/Alation later if needed.

Will masking kill analyst productivity?

Not if you design for it. Use role-based dynamic masking so analysts see what they need (e.g., hashed email for joins) and only privileged roles can detokenize. Provide privacy-safe views and materialized aggregates for common workflows.

How do we handle DSARs without breaking referential integrity?

Use stable, privacy-safe identifiers (tokenized keys) to join, and implement deletion pipelines that remove or redact raw PII while re-materializing aggregates. Test DSAR pipelines in CI with seeded data to avoid surprises.

What about re-identification risk in aggregates?

Set minimum group sizes (k-anonymity thresholds), drop high-cardinality quasi-identifiers, and consider noise injection for sensitive metrics. Most analytics don’t need individual-level data—enforce purpose limitation via views.

We’re multi-cloud. Is this portable?

Yes. Tag-test-enforce-audit works across AWS/GCP/Azure. Use cloud-native primitives where possible and centralize policy-as-code (OPA/Rego, Terraform) and lineage (OpenLineage/DataHub) for portability.

Data-engineering · Oct 14, 2025 · 9 minute read

The Privacy Controls That Won’t Break Your Dashboards (Or Your Audit)

Stop treating privacy as a bolt-on. Build tag-test-enforce pipelines that ship reliable analytics and pass GDPR/CCPA/HIPAA without heroics.

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The Privacy Controls That Won’t Break Your Dashboards (Or Your Audit)

Key takeaways

Implementation checklist