Overview
In today’s data-driven world, organizations are rapidly adopting cloud-native platforms like Snowflake to modernize their data infrastructure. But with great data power comes great responsibility—data governance is no longer optional. It’s mandatory.
Whether you’re just starting your Snowflake journey or scaling your enterprise data platform, implementing strong governance practices ensures data quality, security, compliance, and trust across your organization.
In this blog, we’ll explore data platform governance best practices, with a focus on Snowflake implementations.
Why Data Governance Matters in the Cloud Era
Modern data platforms like Snowflake offer unprecedented scalability, flexibility, and performance. But without governance, these benefits can quickly turn into risks:
- Data sprawl leads to inconsistent reporting.
- Lack of access control exposes sensitive data.
- Poor metadata management hampers discoverability.
- Non-compliance with regulations like GDPR or HIPAA can result in hefty fines.
A well-governed data platform ensures that data is accurate, secure, and used responsibly—empowering teams to make confident, data-driven decisions.
Guiding Principles
As a Snowflake partner with over 20+ implementations, here are our Data Governance guiding principles:
1. Establish a Data Governance Framework
Start with a clear governance framework that aligns with your business goals. This includes:
- Data ownership: Define who owns which datasets.
- Data stewardship: Assign stewards to maintain data quality.
- Policies and standards: Document naming conventions, data classifications, and retention policies.
💡 Tip: Use Snowflake’s native features like object tagging and masking policies to enforce governance rules programmatically.
2. Implement Role-Based Access Control (RBAC)
Snowflake’s RBAC model allows you to manage access at a granular level:
- Create roles based on job functions (e.g., analyst, engineer, auditor).
- Assign privileges to roles, not individuals.
- Use role hierarchy to simplify permission management.
🔐 Best Practice: Regularly audit role assignments and use Snowflake’s Access History to monitor usage.
You can learn more about our RBAC best practices here.
3. Leverage Data Classification and Tagging
Snowflake supports object tagging to classify data by sensitivity, business domain, or compliance requirements.
- Tag PII, financial data, or regulated datasets.
- Use tags to drive automated masking, access policies, and auditing.
🏷️ Pro Tip: Use Snowflake’s Information or Account_Usage schemas and object tagging features to automate metadata capture, classification, and governance across your data platform.
4. Data Accountability
As data platforms grow, centralized governance can slow things down. A better approach is domain-based governance, where responsibility is distributed across business units like Sales, Finance, or Marketing.
- Data Owners are accountable for data quality, access, and alignment with business goals.
- Data Stewards manage metadata, monitor quality, and ensure compliance within their domain.
This model promotes agility while maintaining control.
🔐 Snowflake Tip: Use object tags and role-based access to isolate domains and enforce policies (e.g., domain:sales, sales_analyst, or finance_owner). You can then apply masking and row access policies based on domain tags.
5. Automate Data Quality Monitoring
Data quality is the foundation of trust. Implement automated checks for:
- Null values
- Outliers
- Schema drift
- Data freshness
🛠️ ETL Tip: Matillion allows flexibility to define and build custom data quality checks for your organization. Whereas, dbt has automated tests and checks within your data pipelines.
Learn more about our data quality check framework here.
5. Enable Data Lineage and Observability
Understanding where data comes from—and how it’s transformed—is critical for governance.
- Use Snowflake’s Query History and Information Schema to trace lineage.
- Integrate with observability platforms to monitor data pipelines and usage patterns.
🔍 Lineage helps with impact analysis, compliance audits, and debugging broken dashboards. Learn about our ETL observability Streamlit application here.
6. Audit, Internal Compliance & Third-Party Regulations
Strong governance isn’t just about internal control—it’s also about proving compliance to auditors, regulators, and partners.
Data Governance must support both internal audits and external regulations.
- Use Snowflake’s Access and Query History to track data usage and user activity.
- Apply tags, masking, and row access policies to protect sensitive data.
- Ensure alignment with standards like GDPR, HIPAA, SOC 2, and PCI-DSS using Snowflake’s built-in security and compliance features.
🛡️ Tip: Maintain a simple compliance checklist to map Snowflake features to your regulatory needs.
Learn how we helped a client adhere to SOX compliance following their IPO here.
Example
Let’s bring governance to life with a real-world example: Human Resources (HR) data.
HR data often includes sensitive information that requires strict access controls and classification.
User Access
- All Employees: Read-only access to basic HR data (e.g., name, hire date)
- Payroll Managers: Read/write access to salary and bank details (reviewed every 3 months)
- People Managers: Read/write access to salary data for their direct reports only (reviewed every 3 months)
This HR data governance example demonstrates best practices by aligning user access with data sensitivity through clearly defined roles and access control policies. It ensures secure handling of classified data elements like salary and bank account numbers, with periodic reassessment for access permissions.
Summary
Data governance is not a one-time project—it’s an ongoing journey. By embedding governance into your Snowflake data platform, you create a foundation for scalable, secure, and trusted analytics.
Needing to enhance your governance standards within your organization? Connect with our team.
