Data Classification Recommendation Success Metrics
Description
This recipe measures the operational and governance success of Data Classification Recommendation (DCR) models. It evaluates recommendation outcomes, governance efficiency, and user engagement to highlight how effectively AI-driven classification supports trusted data governance. By tracking acceptance rates, review backlogs, and steward activity, this recipe provides visibility into: The performance and reliability of DCR models. How quickly governance teams act on AI recommendations. Which users and models drive the most impact. How rejection patterns and Smart Score variations reflect trust and model tuning needs.
Details
AI-driven classification recommendations only scale when teams can measure model quality, steward efficiency, and adoption trends. Without standardized metrics, backlogs persist, retraining decisions are guesswork, and trust in automated classification remains low. This recipe delivers an operational and governance scoring framework to quantify acceptance rates, model reliability, steward productivity, Smart Score correlations, and time-based trends for continuous improvement.
Business value
- Measure model effectiveness to prioritize retraining and tuning.
- Reduce steward backlog with visibility into pending recommendations.
- Drive trust by correlating Smart Scores with acceptance outcomes.
- Identify high-impact models and underperforming ones for remediation.
- Improve catalog completeness and governance through data-driven actions.
Who benefits
- Data Governance Stewards
- Privacy & Compliance Owners
- Data Engineering & Model Teams
- CDO / CAO & Leadership
- QA, Monitoring & Product Owners
What you receive
How the recipe works (guided flow)
Step 1 — Read: Ingest recommendation and model logs
Read recommendation records (pending/accepted/rejected), model metadata (model id, algorithm, Smart Score), steward actions, and timestamps to build the event dataset.
Step 2 — Compute: Aggregate pending recommendations per model
Count outstanding recommendations for each model to surface backlog hotspots and review queues.
Step 3 — Compute: Calculate acceptance and rejection volumes
Aggregate accepted and rejected recommendation counts per model and per steward to measure outcome distributions.
Step 4 — Compute: Derive overall acceptance rate and KPIs
Compute global and model-level acceptance percentages, average review time, and SLA breach metrics for governance dashboards.
Step 5 — Compute: Compare Smart Score averages (accepted vs rejected)
Calculate average Smart Scores for accepted and rejected recommendations to validate scoring thresholds and tune models.
Step 6 — Compute: Build time-series trend metrics (30/60/90 days)
Produce rolling-window metrics to show how recommendation volume, acceptance rate, and model reliability evolve over time.
Step 7 — Compute: Rank stewards and models by performance
Create leaderboards for top stewards (accepted classifications) and rank models by accepted volume and rejection frequency for prioritization.
Step 8 — Output: Generate governance-ready reports and dashboards
Export pending dashboards, steward engagement reports, Smart Score correlation tables, model rankings, and time-series charts for leadership and audits.
Sample insights (illustrative)
| Insight Category | What the recipe discovered | Business Impact |
|---|---|---|
| Model backlog | Model A has 1,200 pending recommendations, exceeding SLA thresholds. | Prioritise steward reviews or increase automation to avoid stale classifications. |
| Smart Score validation | Accepted recommendations have an average Smart Score of 0.82 vs 0.46 for rejected ones. | Use threshold-based routing to auto-accept high-confidence suggestions and queue low-confidence for manual review. |
| Steward productivity | Top 5 stewards resolved 40% of accepted classifications in the last 30 days. | Highlight high-performers for scaling best practices and allocate backlog to active stewards. |
Make sure the following ingredients are available in your workspace:
- Connected datasets are crawled and profiled
- Columns have distinct counts and top values generated