Written by Bob Bloem, Managing Director at Unify Consulting
After achieving initial visibility into development velocity, a new, more sophisticated question emerges: “We’re moving faster, but are we building well?” Answering this question marks the second crucial stage in an organization’s analytics maturation. Shipping features quickly only helps the business if those features are stable, reliable, and don’t create a mountain of technical debt.
This is where we enrich the data model by incorporating critical quality metrics. The goal is to create a healthy tension between speed and stability. Key quality metrics we integrate include:
- Change Failure Rate (CFR): What percentage of our deployments cause a production failure? This directly measures the quality of your release process.
- Code Churn / Rework Rate: How much new code needs to be significantly changed shortly after it’s written? A high churn rate can be a leading indicator of unclear requirements, technical struggles, or low-quality initial code.
- Bug Density: Are we creating more bugs in certain parts of our application? This helps pinpoint “hotspots” in the codebase that may need refactoring or more rigorous testing.
By visualizing these quality indicators alongside velocity metrics like cycle time, leaders can have a much more nuanced conversation. They can see if a push to increase speed is leading to a spike in production failures, allowing them to fine-tune processes for sustainable, high-quality delivery.
With a balanced view of both velocity and quality, an organization is finally ready for the final stage of maturation: using data for true strategic advantage. In our final post, we’ll explore how this foundation allows you to measure the real impact of new technologies like Generative AI.
Missed Parts One and Two of The Engineering Analytics Journey? Check them out here.
