Why Modern Data Platforms Fail Without Governance
You've built a modern data stack: Snowflake for warehousing, dbt for transformations, Looker for analytics. Everything works great in development. Then you deploy to production, and things start breaking. Data quality issues, schema changes breaking downstream, duplicate data, and no one knows which dataset to trust.
This is what happens when you skip data governance.
What is Data Governance?
- Accurate: Data reflects reality
- Consistent: Same data means the same thing across systems
- Accessible: People who need data can find and use it
- Secure: Data is protected and access is controlled
- Compliant: Data handling meets regulatory requirements
It's not just documentation. It's the operational practices that make data platforms work.
Why Governance Matters
1. Data Quality Issues Compound
In a modern data platform, data flows through multiple systems: Raw data → Data lake → Data warehouse → Transformations → Analytics
- No one knows where data came from
- No one knows if it's accurate
- No one knows who to ask when something looks wrong
2. Schema Evolution Breaks Things
- Schema changes break downstream transformations
- Different teams use different field names for the same thing
- No versioning means you can't roll back changes
3. Access Control Becomes Chaos
- Sensitive data (PII, financial) is accessible to everyone
- No audit trail of who accessed what
- Compliance violations go undetected
4. Duplicate and Conflicting Data
- Multiple teams create similar datasets
- Different metrics with the same name (revenue, active users)
- No one knows which dataset is authoritative
Building Data Governance
1. Data Catalog
- Metadata: What the data is, where it comes from, when it was last updated
- Lineage: How data flows through your systems
- Ownership: Who is responsible for each dataset
- Quality metrics: Data freshness, completeness, accuracy
Tools: Collibra, Alation, or build custom with dbt docs and Great Expectations.
2. Data Quality Monitoring
- Freshness: Is data arriving on time?
- Completeness: Are expected fields present?
- Validity: Do values match expected formats?
- Uniqueness: Are there duplicate records?
- Accuracy: Do values make business sense?
Tools: Great Expectations, dbt tests, or custom validation pipelines.
3. Schema Management
- Use schema registries (Confluent Schema Registry, AWS Glue Schema Registry)
- Document breaking changes
- Provide migration paths for consumers
- Use semantic versioning for schemas
4. Access Control
- Data owners: Define who can access what
- Least privilege: Give people only the access they need
- Audit logging: Track who accessed what data and when
- Data masking: Automatically mask sensitive data for non-privileged users
5. Data Lineage
- Raw data sources
- Transformations applied
- Downstream consumers
- Impact analysis for changes
This helps you understand the impact of changes before you make them.
Real-World Example
A healthcare SaaS company had built a modern data platform but was struggling with data quality. Their analytics team was spending 40% of their time debugging data issues instead of building insights.
- Data catalog: Documented all datasets with ownership and metadata
- Automated quality checks: Great Expectations tests run on every pipeline
- Schema registry: Versioned schemas with breaking change detection
- Access controls: Role-based access with audit logging
Result: Data quality issues dropped 80%. Analytics team productivity increased 3x. They can now trust their data and make decisions faster.
Common Mistakes
1. Governance as Afterthought
- Document data as you create it
- Set up quality checks from day one
- Define ownership early
2. Over-Engineering
- Spreadsheet for data catalog before buying expensive tools
- Basic quality checks before complex validation frameworks
- Manual access reviews before automated systems
Add complexity as you need it.
3. Ignoring Culture
- Integrate governance into existing workflows
- Provide self-service tools
- Reward good practices
4. Treating It as IT's Problem
- Define data quality standards
- Own their data
- Use governance tools
Key Takeaways
- Governance is operational, not just documentation: It's how you run data platforms
- Start early: It's harder to add governance to existing systems
- Make it easy: Governance that's hard to follow won't be followed
- Measure quality: You can't improve what you don't measure
- It's a team effort: Everyone who works with data needs to participate
Modern data platforms are powerful, but they're only as good as the data in them. Governance ensures that data is trustworthy, accessible, and secure. Without it, you're building on a shaky foundation.