Platform engineering teams are lying about their results | Weave Intelligence

In this conversation, Sam and Michael Wolbert delve into the critical aspects of platform engineering, focusing on the importance of measurement and metrics. They discuss the alarming statistic that nearly 30% of platform engineering teams do not measure their performance, the implications of self-reporting bias, and the necessity of a product mindset for effective measurement. The conversation also covers the selection and analysis of metrics, the balance between tooling and culture, and strategies for improving platform adoption. Michael shares insights on experimentation and the significance of qualitative metrics alongside traditional DORA metrics, emphasizing the need for a comprehensive approach to platform engineering.

In this episode:
- 29.6% of platform engineering teams do not measure their performance
- Self-reporting bias can lead to discrepancies in perceived success
- Evidence-based data is crucial for informed decision-making
- A product mindset enhances focus on measurement and metrics
- Metrics should align with business goals and strategies
- Start with simple tools like Excel to understand measurements
- Adoption metrics should follow an S-curve model for tracking success
- Improving reload times can significantly reclaim developer productivity
- Qualitative metrics are as important as quantitative metrics
- Retention of developers improves with better platform experiences

💬 "Evidence-based data doesn't lie."

Check out Michael's ROI article here: https://platformengineering.org/blog/platform-roi-showcase-how-2m-emerged-from-one-platform-shift
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