Engineering 360
Why measure engineering effectiveness?
Engineering leadership and platform engineers face a critical question: Where should we focus our DevEx efforts to make the biggest impact? With limited resources and competing priorities, identifying the right areas for improvement can mean the difference between meaningful progress and wasted effort.
The journey
Engineering360 is about avoiding the trap of perfectionism when it comes to analysis, and instead optimizing towards immediate measurement, insights and improvement regardless of the maturity of your Development Platform. In the paragraphs below, we'll explore a tried and tested formula for initiating a culture of continuous improvement, in multiple cycles of measurement and improvement.
Surveys
When chaos blocks accurate metric measurement, do surveys
In the early stages of DevEx maturity, the development platform itself is often fragmented across multiple tools and systems. While Port is very effective at modeling and bringing order to this chaos, it takes time for customers to collect comprehensive quantitative data that provides a clear picture of developer productivity and satisfaction.
During these formative periods, qualitative data from surveys often serves as the most reliable north star - while less precise than hard metrics, it offers rapid and meaningful insights into developer pain points, workflow bottlenecks, and areas where platform improvements would have the greatest positive impact.
Use the survey as a catalyst for change, and to empower your team
Don't just make the developer survey yet another administrative task. With the right approach, you can make your developer survey a seminal moment for your new Platform initiative, and communicate the "why" behind it. To maximize the impact of your developer survey, involve senior leadership in its rollout, create open channels for feedback, and ensure high participation through reminders and visible engagement. Share anonymized results and insights with the team to demonstrate that their input is valued and leads to meaningful action.
You'll learn more about surveys in the next part of the solution, surveys.
DORA metrics
DORA (DevOps Research and Assessment) metrics are a set of key performance indicators that help engineering organizations measure and improve their software delivery performance. The four core DORA metrics are:
- Deployment Frequency: How often your team successfully releases to production.
- Lead Time for Changes: The time it takes for a code commit to reach production.
- Change Failure Rate: The percentage of deployments causing a failure in production.
- Mean Time to Recovery (MTTR): How quickly your team can restore service after a production incident.
By tracking these metrics, engineering teams gain a data-driven understanding of their delivery process, identify bottlenecks, and benchmark their performance against industry standards. DORA metrics are widely recognized as a standard for measuring DevOps and engineering effectiveness, helping organizations focus on both speed and stability.
You'll learn more about DORA metrics later in this solution.
More engineering metrics and improvement inititatives
A team using Port was able to correlate service complexity (measured by number of dependencies) with deployment frequency and incident rates. This revealed that services with more than 5 inter-service dependencies had 3x more incidents, leading to targeted architectural improvements.
Another customer was able to identify Tribes with a materially faster time to 10th PR metrics and chose to follow up with a secondment of engineering managers to those teams to learn.
Port's flexible data model and managed relations create unique opportunities for measuring sophisticated engineering metrics. Unlike traditional tools that are limited to predefined metrics or siloed data sources, Port can normalize and connect data from across your entire engineering ecosystem. This enables tracking of custom metrics that matter specifically to your organization - whether that's measuring cross-team dependencies, tracking technical debt across multiple repositories, or analyzing the impact of architectural decisions on delivery speed. The managed relations between entities allow for multi-dimensional analysis, helping you understand not just what's happening, but where and why. For example, you could analyze deployment frequency not just by team, but by service type, technology stack, or business domain. This deeper insight helps engineering leaders make more informed decisions about where to focus improvement efforts.
You'll learn more about measuring arbitrary engineering metrics later in this solution.