Essential Strategies for Measuring Development Team Productivity

The idea of measuring productivity can leave a bad taste in the mouths of business leaders. They do not want to place too much emphasis on numbers and money when building HR policies, yet they need to manage their resources effectively, which is difficult to do without those numbers. Moreover, global market competition and consumer demands put leaders in a difficult position to increase performance to match competitors.

Before, when the digital technologies we take for granted were only emerging, hiring a team of software developers, giving them excellent benefits, and letting them loose to create cool applications and software was possible and the acceptable approach. The reality today, however, is that the era of cheap money has ended. Businesses need to utilize the data their teams produce to understand what is happening in projects and where there are roadblocks. The question, of course, is how.

The benefits of efficiency metrics for a business

Another side of metrics is that employees tend to associate them with the idea of control. They question why businesses are looking at productive metrics. Control has a negative side, called micromanagement, but it also has a positive side, which ensures that businesses keep things in order, which is what efficiency metrics do. With the end of cheap money, it is necessary to maintain disciplined processes through data that comes from metrics.

These types of metrics provide a means of measuring how software development teams are working without sacrificing their productivity. The benefits of using metrics as guides in a business strategy allow leaders to:

• Evaluate progress
• Improve results
• Increase speed
• Optimize processes
• Enhance quality
• Forecast risks
• Make well-founded decisions

Examples of efficiency metrics

Put simply, efficiency metrics display speed, flexibility, and quality. They show how fast a team or individual engineer completes certain tasks, how well they adapt to changes in the project or context, and whether the final result brings value to the client. Here are the common metrics that fall under this category and what they measure:

• Cycle time: Measures the time it takes from starting a task to completing it. A shorter cycle time indicates faster delivery.
• Lead time: Tracks the total time from when a request is made to when it's delivered. It includes waiting, development, and testing time.
• Throughput: The number of tasks (features, bug fixes, etc.) a team completes in a specific time period. Higher throughput often reflects better team efficiency.
• Velocity: Measures the amount of work (usually in story points) a team completes in each sprint. Consistent velocity helps predict future work capacity.
• Code churn: Tracks how much code is rewritten shortly after it's been written. High churn can indicate poor planning or misaligned objectives.
• Defect rate: The number of bugs or issues reported in relation to delivered features. Lower defect rates suggest higher code quality and efficiency.
• Sprint burndown: Tracks remaining work over time in a sprint. A steady burndown graph indicates balanced workload distribution and progress.
• Deployment frequency: Measures how often a team deploys updates to production. Frequent, small releases can indicate efficient DevOps practices.
• Mean time to recovery (MTTR): The average time it takes to recover from production failures. Faster recovery times show effective incident management.
• Code review time: The time it takes for a code review process to be completed. Shorter review times indicate efficiency in delivering features while maintaining quality.
• Work-in-progress (WIP): Reflects how many tasks are currently being worked on. Keeping WIP low helps avoid bottlenecks and maintain focus.

The ineffective use of efficiency metrics

While these metrics have clear benefits for business, they do not provide the whole picture of team productivity. Even with this data, it is wrong to use it to rush into decision making. In that case, metrics become a tool for measuring employees. There is a term for doing this, Goodhart's law, which states that "Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes." In other words, if these metrics are the only method for measuring performance, they will disappoint and give leaders an incomplete picture of the problem. They are only part of the story of your development team's performance. Likewise, with a long list of metrics, it can be tempting to focus on individual numbers instead of taking them all together. Efficiency metrics break down a team's workflow into individual steps, but they must be used together to understand the whole context.

Other factors, which cannot be displayed as numbers, are important as well. As the authors of the SPACE framework argue, leaders need to consider them when rating the performance of individual engineers or their teams. Examples of SPACE metrics include satisfaction, well-being, and communication. Therefore, efficiency metrics should not be used as the only tools for deciding on an employee's future within a company or current pay or experience rating.

Here is what our team suggests leaders do when metrics send warning signals:

1. Talk to the team to understand the context
2. Analyze the entire picture to find the source of the problem
3. Make targeted improvements

These steps embody the Enji team's principle of data-driven decision making. Communication and understanding are key to creating performance-driven work processes that consider the unique traits of your teams and individual engineers. It is better to view metrics as signals that indicate where something could be wrong and not as proof of the exact reason.

Enji's features for metrics

Enji boasts several features that gather software development business metrics, such as the efficiency metrics mentioned above, and present them to management in a clear, easy-to-read format. With these regular reports, leaders can see the signals of high performance or roadblocks and approach teams with specific questions to understand the context. Enji provides data-driven performance to reduce misunderstandings and increase efficient communication between teams and management.