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name: quantitative-management description: Use when establishing measurement programs, analyzing metrics with statistical process control, setting baselines, or implementing CMMI Level 4 quantitative management - prevents vanity metrics and measurement theater

Quantitative Management

Purpose & Context

Implements CMMI MA (Measurement & Analysis), QPM (Quantitative Project Management), and OPP (Organizational Process Performance) through data-driven decision making, statistical process control, and predictive analytics.

Core Principle: Measure what matters, use data to drive decisions, distinguish signal from noise.

Avoid: Measurement theater (tracking without action), vanity metrics (looks good but no value), gaming metrics (optimizing numbers vs processes).

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When to Use This Skill

Triggers:

• "What should we measure?"
• "How do I know if this variation is a problem?"
• "How do I establish process baselines?"
• "What's the difference between Level 2, 3, and 4 measurement?"
• "How do I use DORA metrics?"
• "How do I implement statistical process control?"
• "How do I use metrics for project decisions?"

Use this when:

• Establishing a measurement program
• Moving from Level 2 → 3 → 4
• Implementing DORA metrics
• Analyzing process performance
• Detecting process instability
• Predicting project outcomes
• Making data-driven decisions

Do NOT use for:

• Platform-specific metric collection → See platform-integration skill
• One-time reporting → Just write the report
• Metrics mandated by compliance → See governance-and-risk skill

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Quick Reference: Finding What You Need

You Want To...	Reference Sheet	Key Content
Plan what to measure	measurement-planning.md	GQM methodology, cost/value analysis, anti-patterns
Choose metrics	key-metrics-by-domain.md	Quality, velocity, stability, deployment metrics
Implement DORA	dora-metrics.md	4 DORA metrics, collection automation, baselines
Analyze variation	statistical-analysis.md	Control charts, SPC, trend detection
Establish baselines	process-baselines.md	Historical data analysis, baseline maintenance
Make data-driven decisions	quantitative-management.md	QPM, prediction models, Level 4 practices
Scale L2→L3→L4	level-scaling.md	Maturity progression, requirements by level

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Maturity Level Guidance

Level 2 (Managed): Basic Tracking

Focus: Capture data for visibility

Measurements:

• Counts (defects found, tests run, deployments)
• Dates (when tasks completed, releases shipped)
• Simple averages (average lead time, average velocity)

Tools: Spreadsheets, basic dashboards, manual collection acceptable

Example: Track number of bugs found per sprint, average time to close bugs

Limitation: No statistical analysis, no baselines, reactive only

Level 3 (Defined): Organizational Baselines & Trends

Focus: Establish norms, detect trends

Measurements:

• Organizational baselines (typical velocity, typical defect density)
• Trend analysis (improving or degrading?)
• Comparative analysis (this project vs org average)

Tools: Analytics platforms, automated collection, historical databases

Example: Know that your organization typically delivers 30 story points/sprint ± 10, and current project is trending 25 (slightly below average)

Advancement over L2: Baselines provide context, trends show direction

Level 4 (Quantitatively Managed): Statistical Process Control

Focus: Predict outcomes, control variation

Measurements:

• Statistical process control (control charts, control limits)
• Prediction models (effort estimation, defect prediction)
• Process performance objectives (quantitative targets)
• Variance analysis (special cause vs common cause)

Tools: Statistical packages (R, Python), control chart software, Monte Carlo simulation

Example: Use control charts to detect when defect rate exceeds upper control limit (special cause), trigger root cause analysis. Use regression model to predict project completion date with 90% confidence interval.

Advancement over L3: Predictive, not just reactive; distinguishes noise from signal

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Measurement Anti-Patterns

Anti-Pattern	Symptom	Better Approach
Measurement Theater	Tracking many metrics, no action taken	Use GQM to link metrics to decisions
Vanity Metrics	Numbers look impressive but don't drive improvement	Focus on actionable metrics with clear business value
Gaming Metrics	Teams optimize numbers instead of processes	Measure outcomes, not activities; use multiple metrics
Lagging-Only	Only measure results after the fact	Balance lagging with leading indicators
Analysis Paralysis	Too many metrics, can't make decisions	Focus on critical few (3-5 key metrics)
Flying Blind	No metrics, decisions based on opinion	Start with Level 2 basics, build up
Over-Precision	Measuring to 3 decimal places when ±20% is noise	Match precision to decision granularity

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Integration with Other CMMI Process Areas

REQM (Requirements Management):

• Metric: Requirements volatility (changes per sprint)
• Metric: Requirements traceability coverage (% with tests)
• Use baseline to detect abnormal churn

CM (Configuration Management):

• Metric: Branch protection compliance
• Metric: Baseline stability (changes after freeze)
• Use SPC to detect configuration drift

VER/VAL (Verification/Validation):

• Metric: Test coverage, defect density, escape rate
• Metric: Code review effectiveness
• Use control charts for quality gates

RSKM (Risk Management):

• Metric: Risk exposure (probability × impact)
• Metric: Risk burndown (risks closed over time)
• Use trends to predict risk realization

DAR (Decision Analysis & Resolution):

• Use measurement data as input to DAR
• Track decision quality (outcomes vs predictions)

Cross-references:

• requirements-lifecycle - Metrics for requirement quality
• design-and-build - Metrics for development process
• governance-and-risk - Risk and compliance metrics
• platform-integration - Metric collection automation

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Getting Started (Quick Start Scenarios)

Scenario 1: First-Time Measurement Program (Level 2)

Goal: Start tracking basic metrics for visibility

1. Read ./measurement-planning.md - Learn GQM methodology
2. Pick 3-5 metrics using GQM (don't start with 20+)
3. Set up manual or automated collection
4. Track for 4-8 weeks (establish baseline data)
5. Review monthly, adjust what you track

Example metrics for first program:

• Deployment frequency (how often we ship)
• Lead time for changes (commit to production)
• Defect escape rate (bugs found in production)

Scenario 2: Implementing DORA Metrics (Level 2→3)

Goal: Industry-standard DevOps metrics

1. Read ./dora-metrics.md - Understand the 4 metrics
2. Read ./measurement-planning.md - Ensure DORA aligns with goals
3. Automate collection (see platform-integration skill for GitHub/Azure DevOps)
4. Establish baselines (4 weeks minimum)
5. Set quarterly improvement goals
6. Review weekly/monthly

Scenario 3: Statistical Process Control (Level 3→4)

Goal: Detect process instability automatically

1. Read ./statistical-analysis.md - Learn control charts
2. Establish process baselines (Level 3 requirement)
3. Calculate control limits (mean ± 2-3 standard deviations)
4. Plot data on control charts
5. Investigate points outside control limits (special cause)
6. Update baselines quarterly

Example: Defect escape rate control chart with UCL=15%, LCL=2%, current value 18% → investigate

Scenario 4: Data-Driven Project Management (Level 4)

Goal: Use quantitative data for project decisions

1. Read ./quantitative-management.md - Learn QPM practices
2. Read ./process-baselines.md - Understand baseline usage
3. Set process performance objectives (e.g., defect density < X)
4. Use prediction models for estimates (see statistical-analysis.md)
5. Monitor against objectives using SPC
6. Adjust process when out of control

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Common Questions

Q: How many metrics should I track? A: Level 2: 3-5 basic metrics. Level 3: 5-10 organizational baselines. Level 4: 3-5 with statistical control. Focus beats breadth.

Q: How long to establish a baseline? A: Minimum 4 weeks for initial baseline. Prefer 12 weeks (1 quarter) for statistical validity. Update quarterly or when process changes.

Q: What's the difference between MA and QPM? A: MA (Level 2+): Measurement & Analysis - defining, collecting, analyzing metrics. QPM (Level 4): Using statistical process control and prediction models to manage projects quantitatively.

Q: Do I need Level 3 before Level 4? A: Yes. Level 4 requires organizational baselines (Level 3). Can't do statistical process control without knowing what "normal" looks like.

Q: Leading vs lagging indicators - what's the difference? A: Lagging: Measure results after the fact (defects found, deployment time). Leading: Predict future outcomes (code review coverage, test coverage). You need both. See ./key-metrics-by-domain.md for examples.

Q: How do I avoid measurement theater? A: Use GQM methodology (see ./measurement-planning.md). Every metric must answer a question that drives a decision. If you can't explain the decision, don't track the metric.

Q: When should I use control charts? A: When you have 20+ data points and want to distinguish normal variation from abnormal (special cause). See ./statistical-analysis.md for guidance.

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Reference Sheets

1. measurement-planning.md - GQM methodology, cost/value analysis, measurement repository design
2. key-metrics-by-domain.md - Quality, velocity, stability, deployment metrics with selection guidance
3. dora-metrics.md - Deployment Frequency, Lead Time, CFR, MTTR implementation
4. statistical-analysis.md - Control charts, trend detection, confidence intervals, SPC
5. process-baselines.md - Baseline establishment, maintenance, usage for estimation
6. quantitative-management.md - QPM practices, prediction models, process performance objectives
7. level-scaling.md - Level 2→3→4 progression with concrete requirements

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Related Practices

• ../requirements-lifecycle/SKILL.md - Requirements management metrics
• ../design-and-build/SKILL.md - Development process metrics
• ../governance-and-risk/SKILL.md - Risk and compliance metrics
• ../platform-integration/SKILL.md - Metric collection automation in GitHub/Azure DevOps

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Last Updated: 2026-01-25