DevOps Maturity Model

What is DevOps Maturity Model?

While some people assume DevOps as having a continuous integration, continuous delivery pipeline, and infrastructure in place, they overlook its crucial part. DevOps is much broader and is not restricted only to the technical aspects. To implement a successful DevOps strategy typically teams struggle with these things that are not technical at all:

• Assessment of the processes that need to be automated to remove hassles and manual processes.
• Come up with a proper release management process.
• Encourage teams and managers to adopt a product-centered mindset.
• Building multidisciplinary teams that can take responsibility for their products.
• Improve collaboration between the teams.
• Achieve continuous integration and continuous delivery into reality.

And here comes the DevOps Maturity Model. It is a conceptual model which measures: where does an organization stand? Which direction is it heading into (forward or backward)? And what to aim next.

Areas That Define DevOps Maturity:

Every organization is different and so is its need and goals. And what defines a DevOps maturity for one can be a minute thing for another. That’s why you need to look at all the areas that define DevOps maturity. Here is a list of areas that you need to look at:

• Culture & Organization:  The important aspect of the DevOps is to bring a cultural shift in the organization. So it is important to understand that DevOps should be considered a cultural practice rather than a technological one. To get the most out of it, you need to establish deep collaboration across different teams and embrace the mentality of rapid failure. Most importantly, once you decide to switch to it, you should have confirmation from all the stakeholders to proceed further.
• Incorporation of Continuous Integration and Continuous Delivery Pipelines:   The goal of CI/CD is to deliver better quality software by monitoring issues before they appear through testing. This becomes possible as you develop the ability to identify defects and call quality issues on minute changes earlier in the process. This process helps you shorten the feedback loop between the end-users and the development team. Plus, it allows developers to commit changes more often while still managing the stability.

  some people think that CI/CD is helpful for agile only, yet it is the backbone of the DevOps initiative as well. Be it agile or DevOps, more layers of manual effort can bring down the success rate of the development to the ground. Hence, it is the best practice to automate the build and testing process and find bugs early without putting your precious time into manual activities. To achieve this process, you also need to build a pipeline that should be scalable over time so that new features and requirements in the automated build processes can be added transparently and quickly.

• Rate of Application Deployment:  Nobody wants to see a lapse in the time where something has been built and users are unable to use it. By making small changes in production, we can decrease volatility and hesitancy. If you can reduce the cognitive load of shipping to a minimum, you can help development teams to achieve efficiency in terms of deployment. It will be a small thing to digest and the customer will be able to use that much sooner.
• Efficient Build Process:  A good build process includes artifacts, logs, and status. It gets triggered automatically with each code commit and the history is available for the team to review what has happened over the last execution. Whenever a problem is found like a fault in the test or the security issues in the build phase, a good build process would mark this as failed because the code is analyzed at every stage. Each artifact is tagged and versioned so that the build can be traced across the pipeline. Every metric is collected and further analyzed so that the entire process can be improved.
• Deployment:  Deployment pipelines exist to deploy to all environments using the same standard process. Regardless if it is production or not, there is no need for manual tasks, which makes the process difficult. There is no human effort required for deployment, and they are executed continuously. Releases are disconnected from deployment and features can be done using flags or configuration values. No downtime is required whenever getting a new version to production.

  Once the application health is measured on different levels, it is expected to ensure that everything is working fine. If a problem is detected in production, the deployment process is used to rapidly roll forward the fixes, without the need of rolling back previous changes nor making manual changes as each deployment is immutable and there are even self-healing tools in place.

• Code Management:  To ensure rapid release, there should be no or minimal branching - a process that lets developers run the experiment with varied versions of the underlying source code in source control. As more experimentation alters the code submitted by other developers. And no feature branch should stay longer than a day. The team performs frequent commits multiple times a day. All changes related to the application are stored in version control, which includes infrastructure, configuration, and database.
• Data Management:  To ensure repeatability and control, database changes are done through code migrations or scripts stored in version control fully automated, versioned, and performed as part of the deployment process.
• Continuous Testing:  Many organizations are now releasing code to production weekly, daily, or even hourly. Hence, testing and maintenance need to be performed much more quickly to maintain the desired output. Continuous testing has evolved out of this need. Continuous testing is a type of software testing characterized by the combination of testing early, testing often, testing everywhere.

  By using automation, it addresses business risks associated with a software risk early. Continuous testing improves code quality, accelerates time-to-market, offers a continuous feedback mechanism, and eliminates the disconnect between development, testing, and operations.

• Architecture & Design: Application architecture is one of the main factors that enable or prevent a company from achieving a rapid release through DevOps. If the system is not designed to be tested quickly, easily, and frequently, you’ll end up with a bottleneck that won’t help you. The same goes for deployment.

  Therefore, it is critical to focus on the key non-functional requirements associated with the benefits that you want to achieve, like modularity, testability, agility, etc. Different architecture styles can support these goals, such as microservices, service-oriented or event-driven architectures. The challenge is to choose the right one that fits your needs and align it with other infrastructure and development technologies.

What Are Various DevOps Maturity Levels?

Whether you’re a pro or a beginner in your DevOps journey, there is a specific level that not only defines your progress but also encourages you to move to the next level. If you want to know your current rank in this journey, please take a look at the following DevOps maturity levels:

• Initial Level:  At the initial stages, the organization might not be aware of DevOps or its potential benefits. Hence, the organization has to start from the very beginning. Organizations on this level follow a waterfall project management - long approval and change processes, teams structured around a skill for their IT projects. They plan and design everything up-front before the development teams start coding, and when all is done, separate teams deliver the application to production. Tests start very late in the process. It is very likely they do not practice the concepts of shift-left security.

  Operation is a separate team that waits for developers to hand over their applications with a thick manual of instructions on how to deploy them. The handover of the new version and associated information about deployment is characterized by missing information, miscommunication, and finger-pointing when things go wrong. For some organizations, an initial DevOps level can be overwhelming. But they can also benefit the most if someone from the leadership decides to take action to solve the problem.

• The Repeatable Level:  At this level, organizations know the core principles of DevOps and apply them according to their daily jobs. Environments and their configurations are versioned and can be set up consistently. They are on the right way to facilitate the collaboration between development and operations. Changes do not come as a surprise but are well communicated.

  Organizations operating at this level are not just “reactive” to all that comes across their path. They are proactive and work their way towards repeatable processes for the areas they understand well. However, teams tend to ship rather big features that are difficult to manage and test. Breaking bigger services into smaller microservices remains a challenge. Operations teams need to manually intervene when things in production go wrong.

• Defined Level:  A key characteristic of this level is consistency across areas and topics. Processes are repeatable but also standardized. For example, database changes are performed automatically with every release, non-production deployments are rolled out automatically, and monitoring is integrated with every application. Integration tests are executed automatically and act as quality gates for any later stage in the delivery pipeline.

  Teams are organized around projects or products and not around skill-sets. Development teams work towards the execution of clear requirements that deliver clear business value. All processes are communicated clearly to all people involved, and documentation and release notes are created automatically.

• Managed Level:  At the managed level, all of the environments are managed effectively. Database changes and rollbacks are tested with every iteration of the product itself. The delivery process is predictable and runs frequently. Therefore, stakeholders know what and when to expect. Applications are actively monitored in production, and metrics are gathered.

  Teams know how to incorporate feedback for their next iteration. The organization uses a knowledge management tool to capture existing knowledge and bring more knowledge to the teams. Mentors coach the teams to push them forward. Culture does not remain a bottleneck and welcomes change to achieve organizational goals.

• Optimized Level:  At the optimized level, processes are fully automated and testing is done in production. They know how to deal with problems like overloaded systems. The system itself will scale or adjust to peak requirements. It also adjusts to potential problems, like network interruptions or other infrastructure failures.

Conclusion:

DevOps maturity model defines the journey of an organization that is either planning or already using DevOps. Once the organization gets the idea of ground reality, things become easier for them as the DevOps maturity model clearly states a particular stage, its goal, and strategy to move on to the next stage.

All businesses should assess their DevOps maturity level to successfully implement the core principles of DevOps as early as possible.