Effective DevOps optimization requires careful planning, precise measurement, and constant refinement to yield substantial results.
For VPs of engineering and CTOs, efficiency is paramount. The DevOps model hinges on the principle that an agile, cross-functional team spanning development and operation outperforms siloed development and operations groups. One of its core philosophies is that there's always room for improvement.
Optimizing DevOps resources can significantly impact productivity, making process refinement a top priority for leaders. This article explores what the benefits of DevOps optimization are and how to achieve themit.
Why DevOps Resource Optimization Is Important
Although DevOps is designed to streamline product development and operation, it's still vulnerable to inefficiencies. Tools deployed in one part of the workflow may not work as expected. Engineers may struggle with one set of operations but excel at another. Each of these misalignments can create drag on a DevOps team. The slower your team, the slower you improve your product.
However, if you focus on DevOps optimization, you'll improve the efficiency of your team over time. The more productive your team is, the greater the return on time invested. Focusing on optimization not only improves productivity in terms of time saved; it also means a faster time-to-market, improved quality, and increased innovation.
For example, automation is a key pillar of DevOps because it saves engineers from constantly performing simple, repetitive jobs like testing. The process takes even less time if you can deploy automation via a no-code platform like DuploCloud, freeing workers up for more productive tasks.
How to Achieve Peak DevOps Resource Optimization
Of course, not every process investment has a return on investment (ROI) as clear as automation. That's why team leaders need a framework for DevOps resource optimization that helps them systematically maximize their time savings.
Step 1: Audit Current Resource Allocation
The first step in meeting any challenge is understanding it. For DevOps optimization, that means auditing how your resources are currently distributed. You'll likely need time-tracking software to figure out how long each task takes on average. While your engineers work, map the projects each of them works on, including the tools used and the workflows the projects slot into.
If you don't already have documentation, this is a great time to create it and flesh it out for faster and easier processes in the future. You're essentially following a pattern of continuous improvement. This involves identifying bottlenecks, brainstorming solutions, implementing changes, evaluating results, and then starting the process all over again until you get to the point of diminishing returns where you've optimized yourself into a world-class DevOps space. The goal here is to create a baseline against which you compare the results of your changes.
Building on the foundation of continuous improvement and documentation, process mapping is a crucial tool in your DevOps journey. By visually representing your workflows, you can quickly identify inefficiencies and areas for enhancement. As changes are implemented, these maps can be updated to reflect new processes, creating a living document that evolves with your practices. This approach not only highlights bottlenecks but also clarifies dependencies, leading to more comprehensive solutions:
Example courtesy of Aniket Bhattacharyea
Step 2: Identify Pain Points
Once you have reliable data about time usage and a working process map, you should be able to identify bottlenecks in your DevOps workflow. Some common bottlenecks include the following:
- Technical debt and infrastructure issues: Examples include unchecked technical debt, inconsistent environments, and inefficient automation.
- Process and workflow bottlenecks: Examples include manual intervention, legacy change management, lack of operational maturity, and outdated testing practices.
- Communication and collaboration issues: Examples include ineffective communication about requirements and unclear ownership of infrastructure.
To get even more detail, you need to talk to your engineers. Are they happy with their tools? Is work flowing in an order that makes sense to them? Gathering this data can help you develop a picture of your employee's pain points.
But remember, DevOps teams don't have the full picture. They may highlight workflow components they find frustrating, but these won't always be the bottlenecks you should prioritize. There may be smaller, simpler fixes in your processes that can save time without investing in a new tool or hiring another worker. DevOps resource optimization requires balancing smaller fixes with larger-scale ones.
Using a combination of employee input and time-tracking data, determine which bottlenecks you address first. Try to address no more than three at a time. Managing the rate of optimization helps you isolate the variables you're aiming to improve. That makes it easier to measure results and minimizes the chaos of workflow changes.
Step 3: Plan for Upcoming Goals
Now consider the targets you hope to hit in the near future. Will there be bottlenecks in achieving your targets? If so, determine how you plan to streamline work and make note of any new tools or processes you expect to use.
DevOps Research and Assessment (DORA) metrics are designed to prevent teams from prioritizing speed over quality. For example, while deployment frequency encourages fast releases, metrics like change failure rate ensure that quality is not sacrificed, balancing speed with stability.
In terms of ROI, tools that reduce deployment time can save significant resources. For instance, troubleshooting production issues often requires replicating the environment in a sandbox, which can be time-consuming. A tool like DuploCloud can automate this setup, reducing time spent on repetitive tasks and enabling faster issue resolution. This efficiency allows teams to focus on innovation, ultimately driving a stronger return on investment.
Once you've addressed any bottlenecks, create a plan for how each workflow change will fit in. The more answers your workers have, the more quickly they can adapt, and the faster you get accurate data about your efficiency improvements.
Just as a scientist needs a hypothesis to interpret their findings, you need the proper context to understand how your changes impact overall productivity. To that end, use historical time-tracking data to estimate how long each task or project will take. Be sure to account for any new changes to the workflow. While these changes may slow down productivity in the short term, they should eventually streamline development cycles and operational tasks.
Step 4: Implement Changes and Start Measuring
Here's where the rubber meets the road. As your engineers truck through their workloads, keep an eye on how long they spend on each task. Make sure you're asking these questions:
- How much time are they saving or losing?
- How will continued use affect those results?
- Will developers grow more accustomed to their new tools and workflows, making them more efficient in the long term?
Hard data from time-tracking can shed light on these questions, but so can qualitative information from employees. Frontline engineers may even see additional paths to DevOps optimization within the new workflows.
Keep in mind that staying agile means being open to reverting changes. If a process adjustment hasn't saved the time you thought it would, returning to the old way of doing things isn't a failure. The data you collect can help you continue to refine your DevOps, learning what works for your team and what doesn't.
Step 5: Do It All Again
Once you've collected enough data, you can make calls about which changes to keep, which to roll back, and what needs more attention. Return to the beginning of this process with new information in hand and determine what bottlenecks to address next. Repeat this process often and you'll build out a DevOps team with an ever-escalating productivity.
Leverage Automation to Drive DevOps Optimization
Automation provides some of the most effective ways to optimize DevOps resources. While it's true that building your own automation from scratch can pay dividends, using a no-code/low-code DevOps automation platform can deliver results faster and more efficiently.
DuploCloud DevOps Automation Platform aims to do just that, accelerating infrastructure provisioning by a factor of ten even as it reduces costs by 75 percent relative to the standard app lifecycle. Compliant infrastructure out of the box and sporting thorough documentation, the DuploCloud platform makes it easy to achieve peak DevOps optimization. Schedule a demo today to learn more.
