The governance gap is growing
Every organization today runs on data. But most still lack basic controls over what data they have, who can access it, and whether it’s reliable.
This isn’t just a technical problem — it’s a business risk. Inaccurate data leads to bad decisions. Ungoverned access creates compliance exposure. And without visibility into data flows, teams can’t assess the downstream impact of changes.
Three forces driving urgency
1. Regulatory pressure
The regulatory landscape has shifted dramatically:
- GDPR requires organizations to maintain records of processing activities and respond to data subject requests
- The EU AI Act classifies AI systems by risk and mandates governance controls for high-risk applications
- CCPA/CPRA gives consumers rights over their personal data
Non-compliance isn’t theoretical — fines under GDPR have exceeded €4 billion since 2018.
2. AI adoption
As organizations deploy more AI models, the question of data governance becomes inseparable from AI governance. You need to know:
- What data trained your model?
- Is that data still accurate?
- Who owns it, and do you have the right to use it?
- What happens when upstream data changes?
Without answers to these questions, AI deployments carry hidden risks that traditional ML monitoring won’t catch.
3. Data team productivity
Data engineers and analysts spend a disproportionate amount of time on “data discovery” — figuring out what tables exist, what they contain, and whether they’re reliable. Studies consistently show that data workers spend 30-40% of their time just finding and understanding data.
A well-implemented governance layer — with a searchable catalog, clear ownership, and quality signals — directly reduces this overhead.
What good governance looks like
Modern data governance isn’t about locking things down. It’s about making data discoverable, trustworthy, and compliant — while keeping teams productive.
Key pillars include:
| Pillar | What it means |
|---|---|
| Discovery | Every data asset is cataloged, searchable, and documented |
| Quality | Automated checks validate data freshness, completeness, and accuracy |
| Ownership | Every dataset has a known owner and steward |
| Access | Permissions are granular, auditable, and self-service |
| Compliance | Regulations are mapped to assets with recertification workflows |
| Lineage | Data flows are visible from source to dashboard |
Starting small
You don’t need to boil the ocean. Start with your most critical data assets:
- Connect your primary data warehouse
- Assign ownership to your top 20 tables
- Define quality checks for the data that drives revenue decisions
- Map compliance requirements for any PII or regulated data
From there, expand coverage incrementally. The key is to start — because the cost of ungoverned data only grows.
Want to see how Qarion can help? Request a demo and we’ll walk you through a governance setup tailored to your stack.