Don’t Just Do Something, Stand There

📰 Medium · DevOps

Learn to balance action with observation in engineering leadership, understanding that variance is inevitable and over-reaction can be harmful

intermediate Published 21 May 2026
Action Steps
  1. Analyze the situation to understand the source of variance
  2. Determine if the variance is due to a special cause or common cause
  3. Apply statistical process control to monitor and manage variance
  4. Avoid over-reacting to variance by taking a step back and observing the system
  5. Implement changes only when necessary and with a clear understanding of the potential impact
Who Needs to Know This

Engineering leaders and managers can benefit from this lesson, as it helps them make informed decisions and avoid over-reacting to variance in their teams and systems

Key Insight

💡 Variance is inevitable, and over-reaction can be harmful; balance action with observation to make informed decisions

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💡 Don't just do something, stand there! Understand variance and avoid over-reacting to improve engineering leadership #engineeringleadership #variance

Key Takeaways

Learn to balance action with observation in engineering leadership, understanding that variance is inevitable and over-reaction can be harmful

Full Article

Title: Don’t Just Do Something, Stand There

URL Source: https://medium.com/@jwhite_15604/dont-just-do-something-stand-there-cf5626d77dcb?source=rss------devops-5

Published Time: 2026-05-21T21:18:22Z

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# Don’t Just Do Something, Stand There

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[Jay White](https://medium.com/?source=post_page---byline--cf5626d77dcb---------------------------------------)

8 min read

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May 21, 2026

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_What Engineering Leaders Can Learn from Variance_

Something goes wrong. A deployment fails. An AI-generated change causes an incident. A team misses its sprint commitment for the third time in a row. The room tightens. Leadership asks: what are we going to do about this?

The instinct to act is nearly universal, and it is nearly always the wrong one.

W. Edwards Deming was a statistician and management theorist whose ideas about quality, systems, and variation became the intellectual foundation for Lean, Agile sof
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