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At Netflix, we don’t just think about productivity - we engineer it. There’s an entire team within Netflix dedicated to productivity. I lead the Develop Domain along with my Delivery and Observability Domain peers, and together, we make up Productivity Engineering.
I recently sat down with the Dev Interrupted podcast to discuss all things productivity, how I run my team, and how other managers should view employee success. Here’s how we think about it at Netflix:
Objective data to measure software development is here, and it’s here to stay.
For a long time, the notion of using such data was thought to not really be possible. Thought leaders like Martin Fowler and Joel Spolsky basically said it couldn’t be done. Clearly, it’s a challenging task that frustrated software development managers everywhere. Shoot, I wrote an article way back when basically arguing that it is impossible to do.
Well, I’d continue to argue that it was impossible to do. But now, with the rise of tooling like git, Jira, and other project management tools, it started becoming clear that the data is there to enable us to get a closer, more data-driven look at what is going on inside software development projects. That data just had to be revealed.
Look, we know the software development process is not an easy one to measure and manage, particularly as it becomes more complex and more decentralized. In many companies, there are multiple teams working on smaller parts of a big project-and these teams are spread all over the world. It's challenging to tell who is doing what and when, where the blockers are and what kind of waste has delayed the process. Without a reliable set of data points to track across teams, it's virtually impossible to see how each piece of the application development process puzzle fits together. DORA metrics can help shed light on how your teams are performing in DevOps.
Well, these metrics didn't just come out of thin air. DORA metrics are a result of six years' worth of surveys conducted by the DORA (DevOps Research and Assessments) team, that, among other data points, specifically measure deployment frequency (DF), mean lead time for changes (MLT), mean time to recover (MTTR) and change failure rate (CFR). These metrics serve as a guide to how well the engineering teams are performing and how successful a company is at DevOps, ranging from "low performers" to "elite performers." They help answer the question: Are we better at DevOps now than we were a year ago?