Compliance Awareness Basics

Who this is for

BI Developers and Analytics Engineers who work with customer, employee, or financial data and need to ship dashboards, datasets, and reports that respect privacy and regulatory requirements.

Prerequisites

• Basic SQL and data modeling familiarity
• Experience publishing dashboards or datasets in a BI tool
• High-level understanding of your company's data stack

Why this matters

As a BI Developer, you will:

• Publish dashboards that may include personal data (PII/PHI)
• Share datasets with internal teams and external vendors
• Define row-level security (RLS) and column-level masking
• Retain and archive data according to policy
• Respond to data subject requests (export/delete) with Data Engineering

Doing the above without compliance awareness risks fines, brand damage, and loss of user trust. With a few practical habits, you can ship safely and confidently.

Concept explained simply

Compliance awareness means knowing what data is sensitive, why it is regulated, and which safe patterns to apply in your BI work.

• PII (Personally Identifiable Information): data that can identify a person (name, email, phone, device ID, IP when tied to a person).
• PHI (Protected Health Information): health-related data tied to an individual (e.g., diagnoses, lab results). Common in healthcare contexts.
• Lawful basis (GDPR): valid reason to process personal data (e.g., contract, consent, legitimate interests, legal obligation).
• Data minimization: collect/use only what is necessary for a specific purpose.
• Retention: keep data only as long as needed, then delete or anonymize.
• Subject rights: access, deletion, correction, portability (mostly GDPR-driven).
• Controls you apply: classification, masking, anonymization, aggregation, RLS/CLS, audit logs, encryption at rest/in transit (usually platform-level).

Worked examples

How to apply in your BI workflow

1. Step 1: Classify fields. Mark columns as PII/PHI/Confidential/Public in your semantic layer or dataset description.
2. Step 2: Minimize. Remove unnecessary columns; prefer aggregates; avoid stable user identifiers unless essential.
3. Step 3: Control access. Implement RLS/CLS policies; set workspace and object-level permissions on a least-privilege basis.
4. Step 4: Retain smartly. Apply retention windows to raw logs; preserve aggregates for trend analysis.
5. Step 5: Document. Add a brief privacy note: purpose, lawful basis (if applicable), controls, retention, owner.

Exercises (do these now)

Complete the exercise below. The Quick Test at the end checks your understanding. Note: The Quick Test is available to everyone; log in to save your progress.

Columns:
- ticket_id (string)
- customer_email (string)
- customer_region (string: EU, US, APAC)
- issue_summary (string)
- created_at (timestamp)
- agent_id (string)
- product_plan (string)
- churn_risk_score (float)
  

Common mistakes and how to self-check

• Overexposing identifiers: Leaving emails or device IDs visible when not needed. Self-check: Can the same analysis be done with aggregated metrics?
• Weak RLS: Building RLS but not testing with realistic personas. Self-check: Test as a user from another region—do you see anything you shouldn’t?
• Infinite retention: Keeping raw user data forever. Self-check: Do you have an end date or rotation for raw tables?
• Underdocumented purpose: Not stating why the data is processed. Self-check: Can a new teammate understand why each sensitive field exists?
• Assuming hashing = anonymization: Hashes can still be personal data. Self-check: Could the value link back to an individual with other data?

Practical projects

• Retrofit RLS on an existing dashboard: add region-based RLS and a masked email column; document the change.
• Build an aggregated export: replace user-level marketing extract with weekly cohort aggregates and compare utility vs risk.
• Retention refactor: set a 90-day retention for raw events and keep a 24-month aggregate table; verify reports still work.

Learning path

• Before this: Data classification basics and BI access controls.
• Now: Compliance awareness basics (this page).
• Next: Data retention and anonymization patterns; Vendor data sharing and DPAs; Auditing and change management.

Next steps

• Apply the checklist to one dashboard this week.
• Add a short privacy note to your team’s dashboard template.
• Take the Quick Test below to confirm understanding.

Mini challenge

Your sales team wants a public case-study dashboard with customer logos and NPS comments. In 5 lines, outline what you’ll include and what you’ll exclude, plus controls you’ll use.