A data-first approach to evaluate dev team effectiveness ft. Chris Bee, CTO @Superhuman

Over the years, I started finding ways to bring more predictability to how we function as a team, giving both me and my team the peace of mind we deserve. This is what I am trying to share here – a crash course of my learnings!

As a seasoned CTO, I’ve had my fair share of challenges in ensuring the effectiveness of development teams. Early in my career, I relied heavily on intuition and subjective assessments to measure team performance. I would often find myself in lengthy meetings, trying to understand why projects were delayed or why certain bugs kept resurfacing. Despite these efforts, our progress was inconsistent, and identifying the root causes of issues felt like shooting in the dark.

In this week’s CTO diaries, we will deep dive into Chris Bee’s framework and approach to evaluating effective dev teams. Chris has been a great support and mentor throughout the groCTO journey. Let's hear from him in his own words about how to benchmark and lead dev teams with fair evaluation in this latest edition ⬇

Firstly, the biggest question that every engineering leader has :
Why do we even need to measure dev effectiveness?

• Objective Insights: Data-driven evaluations eliminate biases and provide clear insights into the team’s performance.
• Continuous Improvement: Regular measurement helps identify areas for improvement and track progress over time.
• Enhanced Productivity: Understanding performance metrics can help optimize processes, leading to increased productivity.
• Better Decision-Making: Data-driven insights support informed decision-making, aligning team efforts with business goals.

So, how do we go about executing it?
I call this as a ‘WHAT’ framework :

• What to track
• How to track
• Areas to improve
• Track success

The first step in this journey is what to track and why. The ‘Why’ is really critical to understand as it should align with your tech priorities & business outcomes. Here’s how you go about executing it.

What to track?

• Velocity: Measure the amount of work completed in a given timeframe (e.g., story points per sprint).
• Cycle Time: Track the time taken from starting work on a task to its completion.
• Code Quality: Use metrics like code churn, bug density, and code review coverage to assess the quality of the codebase.
• Deployment Frequency: Evaluate how often code is deployed to production, indicating the team's ability to deliver value continuously.
• Lead Time for Changes: Measure the time taken from committing code to it running in production, reflecting the efficiency of the deployment pipeline.

How to track?

• Automated Tools: Implement tools like Typo to automatically analyse all your Jira, Git, Jenkins, and SonarQube data on the defined metrics & surface insights.
• Continuous Validation: Keep a constant check on these data trends (e.g., weekly or bi-weekly) to see whether the collected data makes sense or not.
• Visualization: Show the team how we are doing collectively and provide an at-a-glance view of key performance indicators.

Areas to improve

• Benchmarking: Compare current metrics against historical data or industry standards to identify performance gaps.
• Root Cause Analysis: Conduct in-depth analysis to understand the root causes of any identified issues.
• Actionable Insights: Translate findings into actionable plans for process improvements, training, or tooling enhancements.

Track success!

• Pilot Changes: Start with small, incremental changes and monitor their impact on the metrics.
• Continuous Feedback Loop: Establish a feedback loop where the team regularly reviews metrics and adjusts practices accordingly.
• Iterative Improvement: Foster a culture of continuous improvement where the team is encouraged to experiment, learn, and adapt.

3 simple hacks for efficient execution!

1. Start Small and Scale Gradually: Begin with a few key metrics that are most relevant to your team’s goals. As the team becomes comfortable with data-driven practices, gradually introduce additional metrics and tools.
2. Foster a Data-Driven Culture: Encourage a culture where data is used to guide decisions, not to penalize. Ensure that the team understands the purpose of measurement is to support improvement, not to assign blame.
3. Regularly Review and Adapt: Schedule regular reviews of the metrics and the effectiveness of the implemented changes. Be flexible and willing to adapt your approach based on the insights gained from the data.

Last words :

Adopting a data-driven framework to evaluate development team effectiveness can lead to significant improvements in productivity, quality, and overall performance. By defining key metrics, systematically collecting and analysing data, and fostering a culture of continuous improvement, CTOs can ensure their teams are aligned with business goals and poised for success.