tl;dr:“A lot of engineering leaders are feeling the pressure: execs have been sold on massive productivity gains, and so many have inflated expectations. It’s on engineering leaders to ground these conversations in reality, by focusing on what the tools are actually being used for, the impact they’re having so far, and what it’ll take to enable teams to get more out of AI. Here’s Laura with a practical guide to help.”
tl;dr:“Laura shares a set of principles for effectively setting targets while avoiding common pitfalls. Whether you’re an engineering leader or part of a DevProd or Platform team, this guide should be helpful for identifying metrics to align around.”
tl;dr:(1) Dismissing intuition. (2) Data-driven theater. (3) Trying to be smart instead of making other people smart. (4) Not utilizing experts soon enough. (5) Not realizing that I’m not an engineering leader.
tl;dr:(1) Dismissing intuition. (2) Data-driven theater. (3) Trying to be smart instead of making other people smart. (4) Not utilizing experts soon enough. (5) Not realizing that I’m not an engineering leader.
tl;dr:Laura reframes this into another question that leaders need to ask to evaluate reports: “what data are you going to use to evaluate my performance?” Her high level advice, which the article dives into: (1) Determine how you want to measure performance first, then find metrics to measure what's important to your company. (2) Focus on outcomes over output, using output metrics mainly to debug missed outcomes. (3) Watch out for metrics encouraging the wrong behaviors. (4) Metrics alone aren't enough - you still need active performance management and feedback.
tl;dr:Laura reframes this into another question that leaders need to ask to evaluate reports: “what data are you going to use to evaluate my performance?” Her high level advice, which the article dives into: (1) Determine how you want to measure performance first, then find metrics to measure what's important to your company. (2) Focus on outcomes over output, using output metrics mainly to debug missed outcomes. (3) Watch out for metrics encouraging the wrong behaviors. (4) Metrics alone aren't enough - you still need active performance management and feedback.
tl;dr:The SPACE Framework of Developer Productivity is a holistic approach to thinking about and measuring software developer productivity. The SPACE framework is not a list of metrics or benchmarks. Instead, it outlines five different dimensions of productivity that can inform your own definition of productivity, and by extension, your measurements: (1) Satisfaction and Well-being. (2) Performance. (3) Activity. (4) Communication and Collaboration. (5) Efficiency and Flow.