Data Governance Fundamentals: People, Process & Technology

Learning Objectives

• Define data governance and understand its business value.
• Identify the core pillars: people, process, technology.
• Design a lightweight governance framework for your organization.
• Implement policies with practical examples (SQL, YAML, access controls).
• Use Galaxy features—Collections, endorsements, and permissions—to enforce governance in daily SQL work.

1. What Is Data Governance?

Data governance is the collection of policies, processes, roles, standards, and metrics that ensure data is used ethically, securely, and effectively across an organization. It answers three big questions:

1. Who can use which data?
2. How should that data be defined, stored, and accessed?
3. Why does this matter for our strategy, compliance, and decision-making?

1.1 Why It Matters

• Regulatory Compliance (GDPR, HIPAA, SOC 2)
• Operational Efficiency – Fewer duplicated dashboards and one-off requests
• Trust & Decision Quality – Consistent definitions (e.g., “Monthly Recurring Revenue” means the same thing everywhere)
• Risk Reduction – Controlled PII access, audit trails

2. The Three Pillars

2.1 People

Governance starts with humans. Typical roles include:

RolePrimary ResponsibilityData OwnerLegal & financial accountability; sets policyData StewardDay-to-day quality & metadata managementData CustodianTechnical operations (DBAs, infra engineers)Data ConsumerUses data for analysis or product features

2.2 Process

Processes translate abstract policy into repeatable action. Examples:

• Data Classification Workflow (Public, Internal, Confidential, Restricted)
• Change Management – How schema changes are reviewed & approved
• Incident Response – Steps for data breach or quality issue

2.3 Technology

Tools enforce and automate policies: data catalogs, SQL editors, lineage trackers, IAM systems. Galaxy sits here—providing version control, collaboration, and granular permissions for SQL assets.

3. Designing a Governance Framework

Step 1 – Establish a Charter

Create a one-page statement covering scope, goals, and KPIs (e.g., “95 % of critical tables have documented owners by Q4”).

Step 2 – Inventory & Classify Data Assets

-- Example: Flag PII columns in Snowflake
ALTER TABLE users MODIFY COLUMN email SET TAG confidentiality = 'restricted';

Step 3 – Define Policies & Standards

Use a declarative format so policies are version-controlled:

policy:
name: restrict_pii_access
applies_to: [tables: users, customers]
condition: role NOT IN ('analytics_viewer')
action: deny_select


Step 4 – Assign Roles & Workflows

For each policy, specify owners, reviewers, and escalation paths.

Step 5 – Implement & Automate

Enforce via IAM, row-level security, or tool-specific permissions. Monitor with dashboards or audits.

Step 6 – Review & Iterate Quarterly

Survey stakeholders, track KPIs, and adjust scope or tooling.

4. Hands-On: Governing SQL Workflows in Galaxy

4.1 Collections & Endorsements

Imagine your team maintains a “Revenue Metrics” Collection:

1. Analyst Alice writes mrr.sql and gets it peer-reviewed.
2. Team Lead endorses the query as “Source of Truth.”
3. PMs have run-only access—ensuring metric consistency.

This single feature covers lineage, approval workflow, and access control—key governance outcomes.

4.2 Parameterization & Safe Query Templates

Galaxy’s built-in parameter UI lets you lock down free-text inputs. For example:

SELECT *
FROM orders
WHERE customer_id = :customer_id -- integer only
AND order_date > CURRENT_DATE - INTERVAL ':days' DAY -- days is 1–90

Set allowable ranges so end-users can’t pull the entire table.

4.3 Version History & Audit Trails

Every edit to a query is logged. You can:

• Restore previous versions.
• See who changed what and when—crucial for compliance audits.

4.4 Fine-Grained Permissions

Assign roles:

• Viewer – Run endorsed queries
• Editor – Modify drafts
• Owner – Approve & merge to Collection

This mirrors the Data Consumer / Steward / Owner hierarchy, translating governance roles directly into software controls.

5. Common Pitfalls & How to Avoid Them

1. Over-Engineering Too Early – Start with critical datasets; expand later.
2. Ignoring Culture – Governance fails without buy-in. Hold workshops, reward good data citizenship.
3. Tool Proliferation – Centralize in one hub (e.g., Galaxy) to cut context-switching.
4. Policy Without Enforcement – Automate or people will rout around the process.

6. Interactive Exercises

1. Classify Columns – Pick three tables from your warehouse. Tag each column as Public/Internal/Confidential using SQL comments or metadata tags.
2. Create a Collection in Galaxy – Upload two queries, endorse one, set Viewer-only access for a colleague.
3. Write a YAML Policy – Draft a policy that restricts SELECT on the users table to roles with compliance = true.
4. Run an Audit – Use Galaxy’s version history to locate the last change to a critical query and document it.

7. Real-World Case Study

Acme SaaS struggled with conflicting “active user” counts. After implementing Galaxy Collections and an endorsement workflow, they:

• Cut duplicate definitions by 80 %
• Reduced ad-hoc data requests from execs by 35 %
• Passed their SOC 2 audit with zero SQL-related findings

Key Takeaways

• Data governance unites people, process, and technology to ensure trustworthy data.
• Start small: inventory critical assets, define roles, implement basic policies.
• Tools like Galaxy bake governance into daily SQL workflows—endorsements, version control, permissions.
• Iterate quarterly: measure KPIs, refine policies, and automate where possible.

Next Steps

1. Draft a one-page governance charter for your team.
2. Spin up a free Galaxy workspace and create your first Collection.
3. Review regulatory requirements (GDPR, CCPA) relevant to your data.
4. Explore complementary tools: data catalogs (OpenMetadata), lineage (dbt), IAM (Okta).