DevOps Trends for 2020 and Beyond

Source:-devops.com

Since the ideation of DevOps, technologists have made great strides to benefit from it. Despite advancements and the success that teams have seen within pockets, many of its core challenges are still prevalent: scaling across the enterprise due to manual processes, poor visibility between dev and ops teams, and tool sprawl that hurts agility. Luckily, there’s light at the end of the tunnel for organizations hoping to get the most from DevOps.

In order to succeed in DevOps and foster agility in this next decade, the secret is pretty simple: Get the right work to the right team at the right time. Here are three ways development and operations teams will get closer to this formula over the coming years.

Change Automation Based on Policies, Data and Automation Will Take Hold
Despite large investments to double down on DevOps initiatives, the reality is most organizations have only made progress on development processes, while downstream gates and processes such as change management are still manual and time consuming. Too often are developers having to log in, create change requests and fill out forms manually–a frustrating, drawn out and overall unnecessary process. The reality is, manual processes in DevOps simply cannot continue in the next decade. They create unnecessary friction, disrupt the flow between dev and ops, and get in the way of other essential work.

However, the tide is starting to turn as organizations look to automate tasks like change management by leveraging dynamic controls through data and policy automation to achieve true CI/CD. As automation becomes more commonplace, developers will spend more time actually developing and will be able to deploy apps faster and with greater efficiency.

Organizations Will Rely on AI/ML and Analytics to Drive Efficiency and Greater Visibility
Despite all the advancements in DevOps over the past 10 years, one hurdle hasn’t been addressed: the limited visibility across all the pipelines from development, and the connection to how those deliverables run in the real world–i.e., knowing what changed, how it’s been tested, understanding the impact and assessing the risk, how something performs in production etc. The lack of visibility presents challenges of its own (distrust among teams, slower approvals, bottlenecks and inefficient processes).

Organizations are starting to recognize and address this challenge, realizing the gains from local optimization within departments along the way. As this continues, they will increasingly rely on analytics to drive efficiency and end-to-end visibility across processes. What’s more, analytical tools will draw planning, repository, test, deployment and performance data from across the DevOps lifecycle to provide insights into reducing cycle times, risk and improving project outcomes. AI and ML will play a role in leveraging that information to better direct work, to adjust stage gates and policies based on prior performance and more.

At its core, analytics enables IT teams to make changes at the pace of DevOps, while unprecedented visibility assisted by ML puts IT at the center of developers’ culture and mindset.

Tool Sprawl Will Continue to Be a Problem but DevOps Management Platforms Will Emerge
With continued adoption of cloud and microservices architectures, new tools will continue to emerge–different teams within an organization often use different tools for planning, version control, build automation, testing, deployment, etc. But, a new trend is emerging as the industry moves toward platforms that streamline management across various tools and teams to improve development speed and agility. Platforms also enable IT teams to quickly accommodate the rapid addition of features to software, while automating the approval process–making product development faster and more efficient, while also breaking down silos.

The Bottom Line
Despite advancements from the last 10 years, there are still many ways to deliver on the value of DevOps and grow adoption. The vast number of tools used by developers and IT makes it difficult for leaders to get a holistic view of how each team is performing. Lack of visibility paired with manual processes slows the behavioral and cultural change needed to succeed with DevOps.

But, as we enter the next 10 years of DevOps, the combination of automation, analytics and workflow platforms will arm teams for success.