How to Optimize Your Analytics in DevOps and Engineering Operations
Analytics in DevOps and Engineering
Analytics in DevOps and Engineering
DevOps and engineering operations analytics have grown in importance as organizations adopt agile development, continuous integration/continuous delivery (CI/CD) and other DevOps practices. A growing number of companies are also measuring their DevOps performance through the combination of both software metrics, such as code quality, test coverage and code complexity, and process metrics, such as release velocity or change failure rate. In a successful DevOps implementation, IT ops teams work together with developers to streamline processes so that software is built, tested and released more frequently while maintaining high standards for quality. This blog post takes a look at why you need to optimize your analytics in DevOps and engineering operations and identifies some of the most important analytics considerations when adopting a DevOps workflow.
Modern digital transformation requires a new way of thinking and acting. A company that leverages DevOps, CI/CD and continuous monitoring effectively is able to differentiate itself by operating at a higher velocity than its competitors. You need to respond quickly to address changes in your customers' needs, and test new features continuously so that you reduce bugs. You also need to deploy code quickly once you have either fixed the bugs or added new features, and monitor everything so you know where frequent problems arise and how to fix them before they happen again. All these strategies allow you to keep focus on what your users are looking for, what they don't like about your product, and the bigger picture as a whole.
Generating actionable insights does not simply mean collecting as much data as possible and storing it endlessly. To answer your business and user-related questions, you must monitor your systems and their performance, spot bottlenecks in your processes, fix them efficiently, and collect the right data at the right time. You also need to select and cleanse your data to make it useful. Managing data is like caring for a garden: you must weed, water, and fertilize it to grow. To maximize its efficiency, you need to eliminate irrelevant information, structure and cleanse it, store it in the right format so that it can be utilized as efficiently as possible, and know how to fertilize it so that it yields the right insights.
Effective data collection is key to enabling fast decision-making; you need to capture the right data, and be sure to collect it at the right time.
Gathering the right information
In order to leverage "end-to-end" software engineering operations analytics, you need to be able to collect data from your Git repositories and project management platforms. Furthermore, you need to be able to connect the dots between these systems to identify, for instance, which ticket relates to a specific pull request and how it is progressing throughout the development cycle. In addition, you need to identify which processes are leading to successes (or failure), and how all these processes are helping (or preventing) you from meeting your company business objectives.
Monitoring engineering metrics and KPIs in real time
Metrics and KPIs will vary based on the size, processes and business goals of your software engineering organization. If you are a digital-native company at an early stage, you are more likely to monitor your software delivery performance in order to deliver features faster to outperform your competitors.
However, if you are scaling your organization, you want to focus on delivery metrics to streamline efficient processes as well look at the quality of your products and services to avoid high customer churn rate. As a DevOps manager, SRE or Scrum Master, you probably want to look at insights that enable you to meet your Sprint goals. Another good approach for any individual contributor is to look at insights that facilitate work prioritization and workload monitoring.
Accessing these insights in real time is required in order to foster a data-driven culture, so you should rely on a platform that allows you to access it without the hassle of data engineering.
If you’ve been actively involved in the DevOps movement for any length of time, you’ve undoubtedly heard that organizational change is the biggest roadblock to DevOps success. That’s why implementing DevOps practices at the earliest stage of your organization will prevent you from facing this challenge. There are two types of change you need to account for when building a DevOps strategy: cultural change and process change.
Cultural change is more significant, because it involves creating a shared language across stakeholder groups. One way to ensure cultural change is to select an executive sponsor for DevOps to represent the business and IT. The sponsor should be someone who understands the benefits of DevOps and can help drive important initiatives, such as putting an end to fire drills. If you’re managing a large-scale DevOps transformation, you may also want to create a steering committee. The committee should include representatives from the business, IT and the various engineering teams.
Organizational change also requires a thorough plan to break down silos and barriers between stakeholder groups. The first step in developing a successful organizational change strategy is to create an actionable vision. This vision should be clear, concise, and tangible. It should include both short-term and long-term goals. It should also describe how the organization's current state (both internally and externally) fits with this vision. The actionable vision should be communicated frequently throughout the organization so that it becomes clear for all stakeholders what the organization is striving toward. Once the actionable vision has been communicated, further planning can begin. To create a culture of change, organizational leaders must communicate the benefits of change clearly and repeatedly. They should also provide sufficient time for employees to adapt to new systems, policies, or procedures. Finally, they must make sure that people have the resources they need to succeed in their roles. Therefore, you may want to consider making investments in IT infrastructure and communications technologies to help facilitate collaboration across departments. In this article, we’ve listed 7 must-have tools for your engineering organization.
Your software engineering team’s success is dependent upon people, processes and technology, and you need to keep an eye on how your DevOps practices impact your software releases, delivery and quality.
The primary objective of engineering operations analytics is to iteratively improve your engineering operations and services to meet the changing needs of your customers and your company’s business goals. As you implement process improvements, track performance metrics, identify opportunities for growth and improve your organization’s execution capability, you’ll be able to better manage your software engineering operations.
If you’re just getting started with engineering ops analytics and have no processes in place, you can seamlessly integrate your Git repos and project management platforms to Keypup — enjoy a 14-day free trial (no credit card required) and a free forever plan for up to 3 projects and unlimited users. Feel free to contact us for a demo so we can help you set up your account to meet your unique processes and goals.