Module 3.1: R&D Capital Audit Methodology
The complete methodology for conducting an R&D Capital Audit: from stakeholder discovery through economic modeling. This is the executive track for CTOs, PE partners, and board members.
🎯 What You'll Learn
- ✓ How to structure a comprehensive Discovery phase (stakeholder interviews, data collection)
- ✓ How to perform Technical Assessment (DORA, debt inventory, team topology)
- ✓ How to build Economic Models that translate engineering data to financial data
- ✓ How to calculate ROI of debt remediation for PE and board presentations
Lesson 1: Phase 1 — Discovery
Discovery is the 2-week phase where you gather data, interview stakeholders, and understand the organization before you begin analysis. The quality of your discovery determines the quality of everything that follows.
Interview CTO, VP Engineering, Engineering Managers, and 2-3 individual contributors. Each perspective reveals different aspects of the engineering reality.
Git history, JIRA data, cloud spend reports, incident logs, team org chart, product roadmap, and financial statements. The data tells you what people won't.
Architecture docs, ADRs (Architecture Decision Records), post-mortems, tech debt backlogs. The gap between documentation and reality reveals organizational health.
Create a discovery checklist for your organization. List every data source, stakeholder, and document you would need for a comprehensive audit.
Lesson 2: Phase 2 — Technical Assessment
The Technical Assessment is a 3-week deep dive into the engineering organization. You're producing quantitative data that will feed the economic model.
Measure all four DORA metrics from actual data (not surveys). Deployment frequency from CI/CD logs. Lead time from Git + deploy timestamps. CFR from incident logs.
Catalog every instance of significant technical debt. Classify by type (code/architecture/infrastructure/dependency). Estimate remediation effort.
Map how teams are organized. Identify Conway's Law violations (team structure doesn't match architecture). Find bottleneck teams.
Calculate the percentage of engineering time spent on maintenance, bugs, support, and infrastructure vs. new features.
Perform a mini-assessment on one team: calculate their DORA metrics, estimate their Innovation Tax, and identify their top 3 technical debt items.
Lesson 3: Phase 3 — Economic Modeling
This is where engineering data becomes financial data. You're translating technical findings into dollar amounts, risk scores, and projections that finance leaders understand.
Aggregate technical debt into a single dollar amount. This is your "total debt exposure" — the cost of all accumulated technical debt.
Project when maintenance costs will consume 100% of engineering capacity based on current debt accumulation rate.
Calculate how technical debt affects the company's enterprise valuation. Use EV-SE to model before/after remediation scenarios.
For every $1 invested in debt remediation, what's the return? Include: freed engineering capacity, reduced incident cost, faster time-to-market.
Create an economic model for a hypothetical company: $10M engineering budget, 45% Innovation Tax, 80 engineers. Calculate PDI, TID, and ROI of reducing Innovation Tax to 30%.
📊 Module Assessment
Complete to demonstrate mastery of Module 3.1: