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Agile . since Jun 23 . Index . DOCs TOP TOC
We discovered our practices by running experiments and analyzing the data. We came up with several options for each category. We then tried each one for an iteration or two and analyzed our metrics. Our primary metrics were velocity and red card rate.
Only later did we explain why the chosen practices outperformed their competitors. Looking at the winning practices, and feeling the way the team operated, the ties to Beginner’s Mind, competencies, and so forth are obvious. After we had discovered this trend, we used it to predict likely successful future practices. However, our adoption process was deductive, not inductive.
Agile . since Jun 23 . Index . DOCs TOP TOC
There are three general strategies for deciding who works on which tasks: assign them to the most-qualified person, assign them irrespective of skill, or assign them to the least-qualified person. We tried all three approaches.
Interestingly, these data showed an overall increase in velocity when tasks were consistently assigned to the least qualified person. The difference was especially marked over long periods. Choosing the least-qualified strategy really pays off after the team has used it for several iterations, but outperforms the others even in the first iteration. The data on red card rate corresponded with those on velocity: the least-qualified teams produced the code that had the fewest surprises.
We didn’t run these experiments while we were hiring. Therefore, we don’t have data correlating task selection approach and ramp-up time. However, we assume that the least-qualified selection strategy also helps with new-hire ramp-up time as it leads to the fastest propagation of skills.