What problem does this system address?
Most data governance initiatives fail not because governance is unnecessary but because they impose enterprise-scale bureaucracy on teams that need practical, enforceable guardrails.
I have seen 5 data governance rollouts in organizations under 50 people. Four produced policy documents exceeding 100 pages. All four were abandoned within 6 months because nobody read them, nobody enforced them, and nobody could find the specific guidance they needed when a real decision arose. The fifth produced a single-page framework. That one stuck.
The problem is not governance itself. The problem is confusing governance with documentation volume. According to the DAMA International Data Management Body of Knowledge, data governance encompasses ownership, quality, security, and lifecycle management. None of those require a 200-page document to implement.
How is the system structured?
The framework operates through four enforceable components: ownership tags, quality SLAs, access controls, and retention policies, each defined in a single table or checklist.
Step 1: Ownership tags on every data asset
Every table, every pipeline, every dashboard gets exactly two tags: a domain owner (the team responsible for meaning) and a technical owner (the person responsible for uptime). I store these as metadata in our data governance as code repository. When something breaks at 2am, the on-call engineer checks the ownership tag. No Slack thread asking “who owns this table?” No 30-minute investigation. Ownership is a tag, not a committee.
Step 2: Quality SLAs with four metrics
Each data asset gets a quality SLA defined by four numbers: completeness (percentage of non-null required fields), freshness (maximum acceptable age in hours), accuracy (error rate against spot-checked samples), and uniqueness (duplicate rate on primary keys). I define these in a YAML file per domain. The SLA for a finance table might be 99.5% completeness, 1-hour freshness, 99.9% accuracy, 0% duplicate keys. The SLA for an internal analytics sandbox might be 90% completeness, 24-hour freshness, and no accuracy requirement. Different data, different standards. One framework.
Step 3: Access controls as code
Access is defined in version-controlled configuration, not in spreadsheets managed by IT. I use role-based access with three tiers: public (anyone in the org), restricted (specific teams), and sensitive (named individuals with audit logging). Every access grant has an expiration date. Every sensitive-tier access triggers a quarterly review. This maps directly to what data contracts already enforce at the schema level.
Step 4: Retention policies with deletion automation
Every data asset gets a retention policy: keep forever, keep for N months, or delete after use. Retention policies are not suggestions. They are automated. A scheduled job checks retention tags weekly and either archives or deletes data that has exceeded its retention window. In one implementation, automated retention reduced storage costs by 28% in the first quarter by removing 14TB of data that had no business justification for existing.
How do you validate it works?
Governance effectiveness is measured by adoption rate and incident reduction, not by document completeness or committee attendance.
I track three metrics monthly. First, ownership coverage: what percentage of data assets have both tags assigned. Target is 95% within 90 days of deployment. Second, SLA breach rate: how often quality SLAs are violated. The goal is not zero (that means SLAs are too lenient) but a steady decline. Third, access audit findings: how many access grants exist past their expiration date. Any number above zero triggers remediation. These metrics get reviewed in a 15-minute monthly meeting. No steering committee. No governance board. Fifteen minutes, three numbers, action items assigned. That is governance without bureaucracy, and it works because people actually do it.