Critical Data Elements - Rule Coverage
Description
This recipe provides a clear governance scorecard by measuring how effectively Critical Data Elements (CDEs) are monitored through active Data Quality Rules. It identifies which CDEs are covered, which remain unmonitored, and quantifies coverage through KPIs and visual insights. This allows data governance teams to quickly understand risk areas, prioritise rule creation, and strengthen overall data quality maturity across the organisation.
Details
This recipe evaluates the completeness of data quality rule coverage across all Critical Data Elements (CDEs) in the system. It identifies which CDEs have at least one active data quality rule and which remain unmonitored, calculates overall coverage metrics, and visualizes the results using a KPI summary and a coverage donut chart. This provides instant visibility into governance gaps, highlights high-risk CDEs lacking controls, and helps teams prioritize rule creation to strengthen data quality maturity.
Business value
- Expose governance gaps by quantifying which high-value data elements lack monitoring.
- Enable prioritization of rule creation to reduce business risk tied to unmonitored CDEs.
- Provide a repeatable KPI for tracking improvements in data quality coverage over time.
Who benefits
- Data governance teams seeking to close control gaps on critical assets.
- Data stewards and owners who need visibility into their CDE coverage.
- Data quality engineers and SREs planning rule authoring and remediation efforts.
What you receive
How the recipe works (guided flow)
Step 1 — Identify all critical elements
Compile the list of Critical Data Elements from data catalog to create the universe of CDEs to evaluate.
Step 2 — Check if each element has ≥1 active rule
For every CDE, search rule definitions and mappings to determine whether at least one active data quality rule is assigned.
Step 3 — Check how many CDEs do not have active rules
Aggregate results to count CDEs without any active rules and flag them as uncovered or unmonitored.
Step 4 — Compute coverage % and list uncovered elements
Calculate overall coverage percentage, generate a list/table of uncovered CDEs, and prepare the pie/donut visualization for reporting.
Sample insights (illustrative)
| Insight Category | What the recipe discovered | Business Impact |
|---|---|---|
| Coverage gap | 12 of 120 CDEs have no active data quality rules | Immediate focus required on high-risk CDEs to reduce exposure |
| High-priority uncovered CDEs | Customer ID and Billing Address are uncovered | Potential regulatory and billing errors if left unmonitored |
| Coverage trend | Coverage improved from 78% → 88% after targeted rule rollouts | Demonstrates value of prioritized rule creation and tracking |
Make sure the following ingredients are available in your workspace:
- Connected datasets are crawled and profiled
- Columns have distinct counts and top values generated