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Meaningful Metrics

In this blog post, Michael Bolton explores the concept of meaningful metrics and some of the uses to which metrics can be put. In particular, Bolton explores how metrics can be used as first order approximations, as estimations rather than predictions (thereby removing the need to “punish” people when the results based off of those metrics weren’t exactly what was expected), and how an organisation should only collect metrics that are important to the strategic efforts of the group. While some of the advice seems elementary, it’s easy to understand how these most basic ideas can be lost in the bureuocracy of daily business. For instance, can you cite examples where metrics are being collected only because they look good in a report to upper management? Bolton finishes his overview with this thought: In summary, they viewed metrics in the same kind of way as excellent testers view testing: with skepticism (that is, not rejecting belief but rejecting certainty), with open-mindedness, and with awareness of the capacity to be fooled. Their metrics were (are) heuristics, which they used in combination with dozens of other heuristics to help in observing and managing their projects. The software development and testing business seems to have a very poor understanding of measurement theory and metrics-related pitfalls, so conversations about metrics are often frustrating for me. People assume that I don’t like measurement of any kind. Not true; the issue is that I don’t like bogus measurement, and there’s an overwhelming amount of it out there.

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