Collaboration within diverse project teams is often more difficult to achieve when there is no clearly visible aim, the planning is haphazard and the information shared is inadequate. However, these are crucial hurdles to overcome in any organization—and more so in product companies with globally distributed teams working on different parts of the product pipeline.
Successful product development requires effective and constant communication between various teams through well-defined metrics, not only to reduce the engineering operations overhead but also to improve on the overall engineering deliverables. These metrics, including ROI or profitability, mean time to recover and deployment frequency provide a clearer picture to an engineering or business owner on how the Dev and Ops teams are working, their adherence to processes and guidelines and the technology they’re using.
Every engineering owner likes to know if the technology investment they make is going to yield a significant ROI. It is no different with DevOps. It is important to identify the exact metrics by which the positive impact of DevOps can be measured. The most effective way to illustrate these metrics in a way that would make sense to senior management is revenue impact. Here are a few of them which have had proven success on internet of things (IoT) projects.
Business metrics such as cycle time impacts incremental sales and customer satisfaction. Whereas other business metrics such as product investment and running costs impact profitability, DevOps implementation effectiveness can be measured by both. Cycle time is, in turn, derived from a number of product features delivered, sprint velocity and release planning metrics. The lower the cycle time, the more features get pushed and impacts the go-to-market. It also results in potential incremental revenue in terms of the value brought by reducing the cycle time.
Another dimension to measure DevOps impact is engineering metrics. Companies today are worried about the cost of downtime in the operational phase as much as they worry about time it takes to bring the product to market. DevOps metrics are valid throughout the end-to-end implementation of a project.
Build automation and code quality analysis, for example, can provide significant savings in build cycle time and build rollout time. Automating deployment process improves time to deploy and sprint velocity. Continuous testing improves mean time to resolve, and CloudOps monitoring tools can help reduce mean time to recover and uptime of any application. Further collaboration in automation through ChatOps tools can help improve mean time to respond. Once set in motion, all these matrices start improving the product quality, which leads to less rework. Intelligent automation also incurs direct productivity benefits.
Organizations now measure these codes to release the metrics at the end of the day to get maximum ROI. A lot of these metrics also depend on the DevOps capability maturity. Organizations at the higher end of the maturity spectrum would tend to measure all of these metrics and try to optimize them. Also, the emphasis of these metrics vary with the domain and stage of the product life cycle being worked on. Home automation companies developing their IoT platform, for example, would emphasize build and deployment metrics, whereas companies in industrial automation with large infrastructure deployment would look at CloudOps monitoring metrics more closely. For telematics companies, test automation metrics become important when there is limited time and bandwidth to capture the data.
DevOps is a cultural transformation and most of the time businesses measure the effectiveness in a very obvious way. However, a change in the way the organization operates should be looked in such a way that the metrics can generate long-term value. Thus, continuous measurement of the DevOps practice should also become one of the major focus areas of DevOps teams.
About the Author / Urvashi Babaria
Urvashi Babaria is a Product manager at eInfochips working in new age areas like IoT, DevOps and CloudOps. She is a techno commercial marketer with nine years of experience in product and project management, business analysis and transformation. She has keen interest in areas of data science including visualization, analytics and modeling. You can start a conversation with her at email@example.com