Most data governance is either too strict (blocks everything) or too loose (governs nothing).
When your data lives in Collibra (catalog), Snowflake (warehouse), BigQuery (warehouse), and Databricks (processing), you can build data governance that enables analytics while maintaining control.
The 4 data governance pillars that matter
1. Data Catalog
A data catalog answers fundamental questions: What data exists in your organization? Where does it live across different systems? Who owns each dataset and is responsible for its quality? What does the data mean and how should it be interpreted?
2. Data Lineage
Data lineage tracks the complete journey of data: Where did it come from originally? How was it transformed through various processes? Where is it currently being used? What other systems or reports depend on it? This visibility is essential for understanding data dependencies and impact.
3. Data Quality
Data quality ensures reliability by answering: Is the data accurate and free from errors? Is it complete with all expected records present? Is it timely and updated frequently enough for your use case? Is it consistent with the same definitions applied across all sources?
4. Data Access
Data access governance controls: Who can access what data based on their role and needs? What are the access policies that define permissions? How is access audited to ensure compliance? What are the compliance requirements like GDPR or CCPA that must be met?
What to build first (week 1)
Start with a simple data governance foundation that includes a data catalog documenting tables, columns, and definitions so teams know what data exists. Add data lineage tracking to show data flow from source to consumption. Implement data quality monitoring for completeness, accuracy, and timeliness. Finally, establish data access controls that define who can access what data.
Once you have these basics, add data classification to tag data as PII, sensitive, or public. Implement data retention policies that define how long to keep data. Add data privacy controls for GDPR and CCPA compliance. Finally, add data usage analytics to see who’s using what data, helping prioritize governance efforts.
Why most data governance fails
Most data governance fails because it’s too strict, blocking legitimate use cases and frustrating teams who need data access. It’s too loose when policies exist but aren’t enforced, creating compliance risks. It’s manual, requiring constant maintenance that becomes unsustainable. Most critically, it’s disconnected when governance tools don’t connect to the data tools teams actually use.
When you build data governance right, you can enable analytics rather than blocking it, finding the right balance between access and control. You can maintain control by enforcing policies automatically. You can automate governance to reduce manual work and make it sustainable. Most importantly, you can connect tools so governance happens where data lives, not in separate systems.
The hidden cost of poor data governance
When data governance is poor, you face compliance risk with potential violations and fines from regulatory bodies. Security risk increases with unauthorized access to sensitive data. Quality risk emerges when bad data leads to bad decisions. Efficiency risk occurs as teams duplicate work because they don’t know what data exists.
Good data governance means compliance is maintained with no violations because policies are enforced automatically. Security is strong with authorized access only, protecting sensitive data. Quality is high because data is monitored and maintained, leading to good decisions. Most importantly, efficiency is high because teams can discover and reuse existing data rather than recreating it.