Not all metrics are created equal

In order to improve the usage and consumption of the metric, teams need to agree on the most important metrics that we want to track in the immediate future and then work toward the secondary metrics. We need the prognosis metrics that will point the direction of work and then the diagnostic metric will help us determine what work needs to be done to accomplish our goals. For teams to be successful they need to agree on standardizing what they measure and how they calculate metrics. Instead of solving for everything, this requires a very targeted approach and solving for L0 (golden signals) first and then keep going down the list.

Software without metrics is art, it is abstract and is not iterable. Metrics play a very critical role in engineering. Most tech companies have done a good job of collecting and reporting metrics. Now, they need to get them to the next level which is reporting them consistently for all, covering engineering, operations, and product.

A key attribute of a successful monitoring strategy is to have a vivid customer lens to determine what is important and what is not. As engineers who are passionate about tech, we sometimes over-index on tech details/metrics. A common trait of a successful product is excellence in engineering, operations, and product. The goal for the team is to ensure that these metrics are easily available and highly visible. One would want to provide easy access and a tiered approach so that we don’t overwhelm the consumer. Let’s cover the different attributes of excellence and other details of engineering, operations, and product.

Terminology

• Excellence: attributes that make the craft stand out. It boils down to what matters the most.
• Leading Indicators are like inputs — they measure the activities necessary to achieve your goals. As the name implies, these indicators lead to results, they come first. Leading indicators can be hard to measure, but easier to influence and describe how to achieve your goals. Engineering excellence metrics are a leading indicator. e.g — Teams with higher code quality have fewer production issues.
• Lagging Indicators are like outputs — they measure the actual results. Lagging indicators show the final score of your strategy and/or efforts. These metrics are easy to…