Automatic Metrics: Turn Down the Volume and Increase Awareness


But it turns out that this resistance is probably misplaced. Early adopters of this new technology found it more likely that the truth set them free. For example, some managers harbor an unstated suspicion that if they can't see you working then you probably aren't working, which leads to restrictive policies about working from home. Yet one company was surprised to find that their most productive developers were those who telecommuted. Even more revealing, developer productivity in the office increased measurably when the manager was out of town. In other cases, tradition was reinforced. Pareto's principal held true: 80 percent of the work was accomplished by 20 percent of the people.

By turning down the volume of what is being said and simply watching what is actually happening, you can strip away the assumptions and biases that often obscure the truth.

From Data to Decisions
Of course these are not the only metrics you will need. Activity does not convert directly into achievement, so you will still measure traditional benchmarks including requirements coverage, defect arrival and closure rates, and post-release issues. But the key here is that you now have a way to uncover patterns that may provide early indicators of trouble. For example, you may believe that you have your priorities clearly established, only to discover that resources are being hijacked by interruptions or back-door demands.

It may also take some time before these types of metrics acquire meaning, in the sense that you may first see the symptoms and then look back for the cause. Instead of discovering what you think is important and then starting to measure it going forward, you can look back at what actually happened and then look for correlations. The cool part is that the cost of collecting the data is so low that you can afford to track more than you think you need, so if you later discover a use for the data, you will already have it.

And, as always, you have to be thoughtful about how you use the data. Decreeing that a particular measurement is important may simply warp behavior without improving results. The hidden meaning behind metrics is not how many or how much, but so what? Put extra care into determining how the data informs decisions, not the other way around.

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