The great resignation is changing company trends.
One such trend is that companies must work to find effective and creative ways to keep and appreciate hired talent.
This is because a high turnover rate is expensive. The onboarding time of new hires and the information lost when an employee leaves a company is notable.
The main topic of this article is to examine strategies that are useful to enabling effective engineering by following industry standards. Furthermore, this piece explores the value of making data-driven decisions to understand engineering bottlenecks.
Engineers are busy writing code, delivering new products and stable code releases, and working on bug fixes and patches. The question remains: What are the most effective ways to understand engineering metrics?
Is it Git Commits? Or is it a contribution across several different systems?
How can engineers show the outcome of their work?
The more important question to ask is, how can we use data to understand engineering bottlenecks?
Before going into the solutions, I want to focus on DORA Metrics.
“DORA is the official name of the team (now part of Google Cloud) that surveyed over 30,000+ engineers on DevOps practices for 6 years and came up with the following 4 metrics:”
- Deployment Frequency — How often an organization successfully releases to production.
- Lead Time for Changes — The amount of time it takes a commit to get into production.
- Change Failure Rate — The percentage of deployments causing a failure in production.
- Time to Restore Service — How long it takes an organization to recover from a failure in production.”
These metrics can help to identify Elite, High, Medium, and Low performers. If you follow the Google article then you will notice that it focuses on GitHub or GitLab as initial data sources. In addition to basic Git or GitLab, enterprises are using various other tools like Jenkins, SonarCloud, Zendesk, Asana, AWS Codepipeline, and the list goes on.
It’s challenging to connect to several systems, collect and aggregate the data with a common data model or schema, and then produce the metrics that many organizations need.
To give you a better understanding, take a look at the following catalog from Faros.