Platform Engineering Teams Are Lying About Their Results | Weave Intelligence

Why It Matters

Accurate measurement transforms platform engineering from a nebulous cost center into a demonstrable value driver, influencing funding decisions and competitive speed-to-market.

Key Takeaways

• Nearly 30% of platform teams admit they don’t measure.
• Self‑reporting bias creates a 5% discrepancy in metric awareness.
• DORA metrics dominate, but many ignore security and cost dimensions.
• Qualitative “vibes” cannot replace quantitative data for stakeholder buy‑in.
• Simple tools like spreadsheets can bootstrap measurement before advanced analytics.

Summary

The conversation between Sam and Michael, two platform‑engineering ambassadors, centers on how teams measure—or fail to measure—their impact. Their latest report reveals that while 40.8% of teams rely on DORA metrics and 31% cite time‑to‑market, a startling 29.6% admit they do not measure anything at all.

The data also expose a self‑reporting bias: 24% of respondents say they don’t know whether metrics have improved since adopting platform engineering, creating a 5‑percentage‑point gap between claimed measurement and actual awareness. The authors link this gap to a lack of product‑mindset, noting that 25.4% of teams also reject treating the platform as a product, which correlates with the non‑measuring cohort.

Examples illustrate the payoff of rigorous measurement. By tracking DORA throughput on build servers, one team reduced pipeline lead time from 20 minutes to 8 minutes—a 60% improvement—through controlled experiments. Conversely, relying on “vibes” such as smooth meetings can mask red‑flag metrics, prompting a call for triangulating qualitative feedback with quantitative data like IDE telemetry, ticket volumes, and PR merge rates.

The takeaway for executives is clear: without objective metrics, platform teams struggle to justify budgets, secure stakeholder buy‑in, and align with broader business goals. Starting with simple data collection—often as basic as an Excel sheet—allows teams to establish a baseline, prioritize dimensions such as velocity, security, quality, people, and cost, and gradually adopt more sophisticated observability tools as capability matures.

Original Description

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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