AI Readiness & Interoperability
Governance for AI Analytics: Change Control for Semantics
Governance ensures semantic changes are intentional and traceable.
TL;DR
- • Semantic changes must be reviewed.
- • Governance prevents drift and surprises.
The problem (layman)
- • Metrics change without documentation or approval.
- • AI outputs drift over time.
Why it matters
- • Governance maintains trust and compliance.
- • It protects downstream consumers.
Symptoms
- • KPIs change unexpectedly after model updates.
- • Stakeholders lose confidence.
Root causes
- • No change control for semantic updates.
- • Lack of ownership for key metrics.
What good looks like
- • Change review process for metrics.
- • Owners accountable for definitions.
How to fix (steps)
- • Implement a semantic change review process.
- • Track versions and communicate updates.
- • Automate tests for key metrics.
Pitfalls
- • Governance too heavy to use.
- • Changes made outside the process.
Checklist
- • Metric owners assigned.
- • Change reviews implemented.
- • Version history maintained.