Three Ways Companies Are Changing Their Thinking About DevOps

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DevOps has become a cornerstone of the IT department in organizations looking to accelerate software development and deliver new applications faster. With the cloud now ubiquitous, this once niche field is changing the number of businesses operating and bringing the days of clunky, cumbersome IT implementations to an end.

DevOps will continue to evolve in the coming years as companies identify new IT challenges that need solving, particularly as they look to automation to optimize or even remove processes that stand in the way of business agility. That’s according to a new report from tech analyst firm Forrester, which says tech leaders should prepare for “big changes” as DevOps changes the face of business innovation and transformation.

Read on for three key takeaways from Forrester’s Future Of DevOps Report

Organizing teams around outcomes

The principles of DevOps are based on the concept of cohesion and bringing people and processes together for better results. As such, companies must break down silos and invest in training if they want to create successful cross-functional product teams.

Forester believes that DevOps teams will redefine themselves based on their purpose, rather than their function. For example, function (tailored to company/end user), activating (coaching and internal advice), platform (service for the developers in the function teams) and complicated subsystem (mainframe or special hardware). This makes it easier for teams to understand their role and function, which can be important when addressing design challenges.

TO SEE: Why DevOps matters

Engineers will also become a shared resource, rather than assigned to individual teams, Forrester predicts, especially as IT teams ditch specialized infrastructure teams in favor of platform teams with a wider range of software engineering, configuration and monitoring responsibilities. This means that DevOps teams also get better tools.

Practices will overtake processes

Achieving goals or results within a company typically involves following a series of steps in a particular order. This makes it easy to introduce delays, as each step often requires a person or department to sign it off. Transfers can be clunky, and in the process, targets can become muddled or misaligned.

Forrester predicts that in the future, DevOps will help companies shift their focus from process to practice, agreeing on organizational priorities and how things are done within the company. These will be “optimized for speed, with more automation around governance, compliance, security and default operations” and minimal human interaction.

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For example, risk management remains a lengthy and often burdensome process, even if it justifies more manual oversight than some other IT processes. Forrester predicts that in the future, companies could use machine learning to analyze findings from the development process to provide automated analysis of risk changes, significantly accelerating the software release and deployment process.

Platforms will consolidate, expand and deepen

DevOps requires a rich set of technologies that can integrate and work harmoniously. As business requirements evolve, so will the DevOps market requirements and the tools needed to support business transformation.

Forrester believes that organizations’ current pick-and-mix approach to DevOps tools and tech stacks will give way to a more unified approach, with each team sharing the same end-to-end Integrated Software Delivery Platforms (ISDPs) are used. Likewise, organizations will look for ways to simplify the development of tightly integrated business apps that don’t work well with the type of custom software that can accelerate app development and delivery.

Some suggested solutions here include: low code/no code tools, that allows even non-technical employees to create business apps, as well as machine learning and automation. Forester notes that there are challenges here: low-code platforms are currently not well integrated with DevOps pipelines, while machine learning testing on critical business applications can be risky — something MLOps (machine learning plus operations) aims to solve.

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