The problem with productivity metrics

Overview
The article examines common productivity metrics and explains why they often fail to capture the true impact of knowledge work. It proposes alternative ways to assess human contribution that align better with engineering and leadership goals.

Key Takeaways

• Traditional output-based metrics (e.g., lines of code, tickets closed) can be misleading.
• Measuring outcomes, quality, and value delivered provides a more accurate picture.
• Leaders should focus on continuous improvement and team well-being over raw volume.
• Data-driven insights must be balanced with context and human judgement.

Who Would Benefit

• Engineering managers looking to refine performance evaluation.
• Technical leaders seeking better ways to motivate teams.
• Product owners interested in aligning work with business outcomes.
• Developers who want clarity on meaningful productivity.

Frameworks and Methodologies

• None