Building a Data Governance Framework: A Practical Guide

Data governance gets a bad reputation for being bureaucratic and slow. That’s because most governance programs focus on policies and committees instead of practical outcomes. Here’s how to build a framework that works.

What Data Governance Actually Means

Data governance is the set of practices that ensure your data is accurate, secure, discoverable, and used consistently across the organization. It answers questions like:

• Who owns this data? (Accountability)
• Is this data accurate? (Quality)
• Who can access this data? (Security)
• What does this field mean? (Documentation)
• Where did this data come from? (Lineage)

The Non-Invasive Approach

We follow Robert Seiner’s Non-Invasive Data Governance philosophy, which recognizes that people are already governing data as part of their daily work — they just aren’t calling it governance. Instead of creating new bureaucratic structures, Non-Invasive Data Governance formalizes and supports the stewardship that’s already happening.

If you want to go deeper, we highly recommend Seiner’s books, especially Non-Invasive Data Governance Strikes Again: Gaining Experience and Perspective — it’s the most practical guide to implementing governance that actually gets adopted.

The Essential Components

1. Data Ownership

Every dataset needs an owner — a business person (not IT) who is accountable for its quality and definitions. Without clear ownership, data quality issues have no one responsible for fixing them.

How to implement:

• Assign domain owners (Finance owns financial data, Marketing owns marketing data)
• Document owners in your data catalog
• Make ownership part of the data onboarding process

2. Data Quality

Data quality isn’t a one-time cleanup — it’s an ongoing process built into your data pipelines.

Key quality dimensions:

• Completeness: Are required fields populated?
• Accuracy: Does the data reflect reality?
• Timeliness: Is the data fresh enough for its use case?
• Consistency: Does the same entity look the same everywhere?
• Uniqueness: Are there duplicates?

How to implement:

• Build automated data quality tests in dbt or your transformation layer
• Set up freshness monitoring and alerting
• Define SLAs for data delivery and quality thresholds

3. Access Control

Not everyone should see everything. Implement role-based access control (RBAC) that’s granular enough to protect sensitive data but not so restrictive that it blocks productivity.

How to implement in Snowflake:

• Use functional roles mapped to business functions
• Implement dynamic data masking for PII
• Use row-level security where needed
• Audit access regularly

4. Data Catalog & Documentation

If people can’t find data or understand what it means, governance fails. A data catalog makes data discoverable and self-documenting.

How to implement:

• Use tools like Dataedo to catalog all data assets
• Document column definitions and business logic
• Map data lineage from source to consumption
• Keep documentation close to the code (dbt docs, catalog descriptions)

5. Data Lineage

Understanding where data comes from and how it flows through your systems is essential for debugging issues and assessing the impact of changes.

How to implement:

• dbt generates lineage automatically for transformations
• Document source-to-target mappings for ingestion
• Use your data catalog to visualize end-to-end lineage

Common Pitfalls

Starting Too Big

Don’t try to govern everything at once. Start with the datasets that matter most — the ones that drive business decisions or have regulatory requirements.

Governance by Committee

A governance committee that meets monthly to review policies will never keep up. Build governance into your data engineering workflows so it happens automatically.

Ignoring the Business

Governance exists to serve the business, not the other way around. If your governance program slows people down without clear benefits, it will be circumvented.

No Tooling

Manual governance doesn’t scale. Invest in tools (data catalog, quality monitoring, access management) that automate the tedious parts.

A Practical Framework for Getting Started

Month 1: Foundation

• Identify your top 10 critical datasets
• Assign business owners to each
• Set up a data catalog and document these datasets

Month 2: Quality

• Add automated quality tests to your critical data pipelines
• Set up freshness monitoring and alerting
• Establish SLAs with data consumers

Month 3: Security

• Audit current access controls
• Implement RBAC aligned with business functions
• Set up dynamic masking for PII

Ongoing: Expand and Iterate

• Gradually expand governance to more datasets
• Refine quality rules based on actual issues
• Update documentation as systems change

Need Help with Data Governance?

Building a governance framework that balances control with agility requires experience. We help organizations implement practical data governance using Snowflake, Coalesce, and Dataedo. Schedule a free consultation to discuss your governance needs.