Data Governance: Managing Data as an Asset

# Data Governance: Managing Data as an Asset

Without governance:

Employees access data they shouldn't (PII, competitive intel)

Data definitions differ (is "active customer" the same in sales and finance?)

Data changes without tracking (who modified that metric?)

Compliance violations (GDPR, CCPA, HIPAA)

Data leaks (sensitive data in unsecured dashboards)

With governance: Clear policies, access controls, audit trails, compliance.

Data Governance Components

1. Data Catalog

Inventory: what data exists, where it lives, who owns it.

**Metadata tracked:**

Name and description

Owner (who's responsible)

Source (where it comes from)

Definition (what does it measure?)

Sensitivity (public, internal, confidential, restricted)

Usage (who uses it, for what)

SLA (how fresh, available, accurate)

**Tools:** Alation, Collibra, DataHub

2. Data Quality Rules

Define acceptable data standards.

``` Rule: Email addresses - Non-null (all records need email) - Valid format (regex match) - Unique (no duplicates) - Alert if: >5% records fail validation

Rule: Customer age - Non-null - Integer type - Range: 18-120 - Alert if: >10% outliers ```

3. Access Control

Who can access what data.

**Role-based access:**

``` Admin: - Access: All data - Permission: Read, write, delete

Analyst: - Access: Non-sensitive data, aggregate data - Permission: Read only

Intern: - Access: Public data only - Permission: Read only ```

4. Data Classification

Label data by sensitivity; apply controls accordingly.

``` Public: - Marketing materials, public blog posts - Access: Anyone - Encryption: Not required

Internal: - Employee data, internal metrics - Access: Employees only - Encryption: Recommended

Confidential: - Customer PII, financial data - Access: Need-to-know roles - Encryption: Required

Restricted: - Executive data, legal privileged info - Access: Authorized personnel only - Encryption: Required, key management ```

5. Audit Logging

Track who accessed what, when, why.

``` Log entry: - User: alice@company.com - Data accessed: customers table - Time: 2026-05-13 14:23:15 - Query: SELECT * WHERE region = 'US' - Rows returned: 50,000 - Justification: "Sales report preparation" ```

Enables: Compliance audit, breach investigation, accountability.

6. Data Retention & Deletion

How long to keep data; when to delete.

``` Customer transactions: - Retain: 7 years (regulatory requirement) - After: Anonymize or delete

Customer feedback: - Retain: 2 years - After: Delete

Website cookies: - Retain: Until user opts out - After: Delete within 30 days ```

Data Governance Policies

**Policy examples:**

**Access Control Policy:**

Who needs access to what data?

How is access approved?

How often is access reviewed?

What happens if employee leaves?

**Data Quality Policy:**

What quality standards must data meet?

Who's responsible for quality?

What happens if data fails quality checks?

How are issues escalated?

**Privacy Policy:**

What personal data is collected?

How is it protected?

How long is it retained?

What are user rights (access, deletion)?

**Security Policy:**

Encryption requirements

Network access controls

Secrets management

Incident response

Real-World Data Governance Scenarios

Scenario 1: The GDPR Audit

Company subject to GDPR. Auditor asks: "Show me all personal data you hold on EU residents."

**Without governance:**

Personal data scattered across 50 systems

No catalog of what data exists

No audit trail of access

Takes 3 months to compile

Likely missing data or including non-EU data

Audit fails

**With governance:**

Data catalog lists all personal data

Classification: PII data identified

For each EU resident: all records found instantly

Audit trail shows who accessed what

Audit completes in 1 week

Audit passes

Scenario 2: The Accidental Leak

Junior analyst creates dashboard with customer PII (names, addresses, phone numbers). Sends to wrong email list.

**Without governance:**

No access controls on what data analysts can use

Dashboard has sensitive data; no one audits

Leak happens; discovered days later

No record of who accessed the data

GDPR fine: $50K-100K

**With governance:**

Data classification: customer PII = "Confidential"

Access controls: analysts can't access customer phone numbers

Analyst can't add PII to dashboard (system blocks it)

If tries to export: logged and audited

Leak prevented

Scenario 3: The Inconsistent Definition

Finance says: "Active customer" = spent $100 in last 90 days Sales says: "Active customer" = logged in last 30 days Result: Reports disagree; strategy confused

**With governance:**

Data catalog: "Active customer" defined once

Definition: "Customer with transaction in last 90 days"

All reports use same definition

Consistency across organization

Data Governance Roadmap

Phase 1: Audit (Month 1)

Inventory all data (what systems, what data)

Assess current access controls (too open? too restrictive?)

Identify compliance gaps (GDPR, HIPAA, etc.)

Phase 2: Policies (Months 2-3)

Define data classification system

Define access control policies

Define data quality standards

Define retention policies

Phase 3: Implementation (Months 4-6)

Deploy data catalog

Implement access controls (role-based)

Set up audit logging

Implement data quality monitoring

Phase 4: Enforce (Months 7+)

Train teams on policies

Monitor compliance

Audit quarterly

Update policies as needed

Common Governance Mistakes

1. **No ownership** — No one responsible for data quality/access 2. **Over-classification** — Everything "Confidential"; loses meaning 3. **No audit trail** — Can't prove compliance 4. **Access creep** — Employees keep access after role change 5. **No data catalog** — No one knows what data exists 6. **Policies not enforced** — Great policies ignored

The Bottom Line

Data is an asset; govern it like one. Define what data you have. Classify by sensitivity. Control access. Audit usage. Ensure compliance.

Good governance enables trust, compliance, and smart decisions.