- Coverage % • Confidence Index • Decision Latency • Value Realization
- 1 Purpose
- 2 Measurement Philosophy
- 3 EA 2.0 KPI Framework
- 4 Coverage %
- 5 Confidence Index
- 6 Decision Latency
- 7 Value Realization
- 8 EA Maturity Score Computation
- 9 Example Dashboard Tiles
- 10 Data Collection Mechanism
- 11 Governance Cadence
- 12 Visualization Example
- 13 Common Pitfalls
- 14 Benefits
- 15 Takeaway
Coverage % • Confidence Index • Decision Latency • Value Realization #
1 Purpose #
An intelligent enterprise must be measurable.
EA 2.0 replaces vague “alignment” talk with quantifiable evidence — how fast, how confident, and how valuable each architectural decision really is.
The KPI model blends data quality + governance + business value into one continuous scorecard.
2 Measurement Philosophy #
| Principle | Meaning |
|---|---|
| Measure Behavior, not Activity | Focus on decision outcomes, not meeting counts. |
| Integrate Metrics into Workflow | KPIs should update automatically from EA 2.0 events. |
| Cross-Domain Balance | Blend technical, data, and human signals — never only one. |
| Visible Value | Every metric must trace to a business benefit. |
3 EA 2.0 KPI Framework #
Four dimensions make the EA 2.0 Maturity Compass:
- Coverage % – how complete is the graph.
- Confidence Index – how trustworthy is the data.
- Decision Latency – how fast does insight → action occur.
- Value Realization – how much measurable benefit results.
Together they feed a single EA Maturity Score (0–5).
4 Coverage % #
Definition: % of known systems and capabilities represented in the graph.
Formula:
Coverage % = ( Tracked Applications / Total Known Applications ) × 100
Data Sources: CMDB, Cloud Inventory, Finance Catalogs.
Targets: ≥ 90 % for critical systems, ≥ 75 % overall.
Why It Matters: A graph with gaps can’t reason accurately.
5 Confidence Index #
Definition: Weighted data quality score combining freshness, validity, and ownership.
Formula:
CI = ( Freshness × 0.4 ) + ( Completeness × 0.4 ) + ( Ownership × 0.2 )
Thresholds:
- 0.9 – trusted for automation
- 0.7–0.9 – manual verification
- < 0.7 – steward action trigger
Visualization: Radar chart per domain.
6 Decision Latency #
Definition: Average time between event detected and decision executed.
Formula:
DL = Decision Timestamp − Event Timestamp
Segments: Operational (hrs) / Tactical (days) / Strategic (weeks).
Goal: Reduce by > 30 % after EA 2.0 deployment.
Insight: High latency = bureaucracy > clarity.
7 Value Realization #
Definition: Monetized impact of architecture actions.
Examples:
- Decommissioned apps → cost savings.
- Improved uptime → revenue protection.
- Faster onboarding → employee productivity.
Formula:
Value Realization = ( Annual Savings + Avoided Loss + New Value ) / EA Program Cost
Target: ROI > 3× within 12 months.
8 EA Maturity Score Computation #
Score = ( Coverage % / 100 × 1.5 ) + ( Confidence Index × 1.5 ) +
( 1 − Decision Latency Normalized ) × 1.0 + ( Value ROI / 5 × 1.0 )
Scale: 0–5 → Foundational to AI-Native.
9 Example Dashboard Tiles #
| Tile | Metric | Visualization | Insight |
|---|---|---|---|
| Coverage Monitor | Current vs Target % | Horizontal bar | Identify unlinked systems |
| Confidence Radar | Domain DQ scores | Radar chart | Spot weak domains |
| Decision Latency Trend | Rolling 30-day avg | Line chart | Shows EA responsiveness |
| Value Tracker | ROI by initiative | Waterfall | Quantifies impact |
10 Data Collection Mechanism #
- Functions and Event Hub capture timestamps and DQ events.
- Graph DB stores
decision_nodeedges with metadata. - Power BI aggregates KPIs daily via DirectQuery.
11 Governance Cadence #
| Review Type | Frequency | Owner |
|---|---|---|
| Operational Metrics | Weekly | Service Manager |
| Maturity Score | Monthly | Chief EA |
| Value Review | Quarterly | Executive Sponsor |
Each review feeds the Continuous Improvement Loop (chapter next).
12 Visualization Example #
EA Maturity Score (3.8)
Coverage 88 % | Confidence 0.91 | Decision Latency ↓ 32 % | ROI 2.9×
Interpretation: Architecture is Integrated-to-Intelligent; next goal is to achieve AI-assisted optimization stage.
13 Common Pitfalls #
- Tracking too many metrics without context.
- Using static Excel reports instead of real-time dashboards.
- Ignoring qualitative feedback from stakeholders.
- No clear owner for KPI maintenance.
14 Benefits #
✅ Quantifies architecture’s business value.
✅ Reveals bottlenecks and duplication.
✅ Supports funding decisions with evidence.
✅ Links technical health to strategic outcomes.
15 Takeaway #
EA 2.0 doesn’t celebrate diagrams — it measures outcomes.
Coverage, Confidence, Latency, and Value form its four-part heartbeat.
When these metrics move together, the enterprise knows it’s learning — not just reporting.
