Engineering productivity metrics tools that you don't hate?

# Engineering Productivity Metrics Tools That You Don't Hate?

As organizations strive to enhance their software delivery processes, the need for effective engineering productivity metrics becomes increasingly vital. However, the quest to gather these metrics often comes with a mix of excitement and dread. 

Recently, I found myself in a position where I need to start collecting engineering productivity data more formally. Despite my reservations—rooted in the belief that there is no truly objective way to measure individual engineering productivity—I recognize the importance of these metrics for driving improvements in our engineering practices. 

Having read "Accelerate" and familiarized myself with the DORA metrics (DevOps Research and Assessment), as well as the SPACE framework and Developer Experience (DX) metrics, I know that my leadership team shares my cautious approach. They agree that engineering productivity metrics should be taken with a giant grain of salt, yet they have set the expectation for me to start implementing them. 

So, I turned to the community for recommendations: What tools have you used for measuring engineering productivity that you don't absolutely hate?

## Insights from the Community

### Engineering Risk Management vs. Productivity Metrics

One insightful comment pointed out that some metrics often touted as productivity indicators are, in fact, more about engineering risk management. These include scenarios where teams need to roll out a hotfix quickly to mitigate business impact. This perspective prompted me to think critically about the purpose behind the metrics we choose to implement.

From a Lean perspective, the focus is on identifying activities that generate either waste or value. In my experience, one of the biggest sources of waste is an unstable CI/CD pipeline. It's not uncommon for teams to waste days just to achieve a green build due to flaky tests or a high percentage of red runs. Therefore, monitoring these aspects can be a pragmatic approach to improving overall productivity.

### Tools That Make a Difference

Several tools were recommended by community members, emphasizing their varying approaches to measuring productivity:

1. **Jellyfish**: One user expressed their satisfaction with Jellyfish, noting its ability to allow users to focus on epics or initiatives rather than getting bogged down by individual or team-level metrics. While Jellyfish does require some adherence to specific practices in Jira, the clarity of the data it provides is a significant advantage. The emphasis on ensuring good Jira hygiene is crucial, as the quality of insights is directly tied to how well teams maintain their project management practices.

2. **Sweetr.dev**: Another exciting development in this space is **Sweetr.dev**, an open-source alternative currently in early stages. The creator of Sweetr.dev is highly focused on user experience, aiming to prioritize what truly matters for teams seeking continuous improvement. They are actively seeking design partners to help shape the product, offering free subscriptions to organizations willing to provide feedback and collaborate on feature development. This kind of community-driven approach can lead to a tool that aligns more closely with user needs.

## The Path Forward

As I embark on this journey of gathering engineering productivity metrics, I am reminded of the importance of choosing tools that not only fulfill the requirements set by leadership but also resonate with the engineering team's daily experiences. Metrics should serve as a guide to improve workflows, reduce waste, and ultimately contribute to delivering value.

If you have additional recommendations or experiences to share regarding engineering productivity metrics tools, I'd love to hear from you. Let's continue the conversation and explore how we can create an environment where productivity metrics enhance our work rather than hinder it.

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By fostering open dialogue and sharing experiences, we can collectively navigate the complexities of measuring engineering productivity in a way that is both meaningful and constructive.
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