5 reasons why DevOps will be a big thing in 2018

DevOps has been a hot topic for a few years now. During this time, many companies have been trying to adopt one way of working or another, developing outputs to align with business needs. But in 2018, all of this needs to come together and make sense.

Why? We have reached a critical juncture where software and data are driving every aspect of business. Executives and decision makers recognize that technology needs to keep pace with the rapid changes in business needs, and applications need to be able to be assembled, reassembled, and uninstalled instantly.

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Here’s how some major DevOps watchers predict the field will develop in the coming months:

Cross-platform interactions and the Internet of Things mean more and more apps and devices: the typical enterprise has become a nonstop software and data factory, running 24/7. Technology must be used to support this, and DevOps is essential to keep up with the pace of creating, testing, and delivering software 24/7. "Add to that the rise of the Internet of Things, which enables seamless switching across smartphones, TVs, tablets, and other devices," wrote Eran Kinsbruner, chief technology evangelist at Perfecto, on DevOps.com. "In 2018, industries such as financial services, healthcare, retail, and automotive will fully adopt the Internet of Things, and a key step in providing the best user experience is testing, lots of testing. In today's digital revolution, you can never have enough testing, measurement, and development."

"DevSecOps": Security is everyone's top priority, and what needs to be done is to integrate security into the application from the beginning to the end of the life cycle. Chris Carlson, vice president of product management at Qualy, discussed why the word Sec needs to be added to DevOps: "Security teams need to understand that DevOps is rapidly changing the way IT operates and need to work with IT and application development teams earlier in the planning and execution lifecycle." This requires "integrating security into DevOps, rather than doing it after the fact."

Making DevOps Agile: Forrester analyst Diego Lo Giudice found that organizations that combine Agile (developers working closely with end users to make frequent software iterations) with DevOps outperform those that separate the two. "It is unacceptable for any IT organization to focus only on Agile or only on DevOps. There are two sides to the same coin, and one side enables the other." Forrester's latest research found that organizations that are running projects that combine Agile and DevOps have 2 times better business/IT results, improved functional quality, faster time to business value, continuous delivery, and higher predictability of results, in line with requirements.

Frequent new releases require faster updates: Increased expectations for enterprise technology have increased the pressure to ensure continued high-performance applications. Kinsbruner said IT leaders "must realize the importance of providing developers with the tools and time to continuously test throughout the software development lifecycle. Tools like automation and cloud can improve efficiency and allow developers to save time on manual quality checks to ensure that the applications they develop meet consumer expectations."

Artificial intelligence and machine learning may start to play a role in DevOps. There are many solutions on the market that use artificial intelligence and machine learning to help DevOps teams not only track progress, but also predict when and where code will be needed. In an article published last year, Ronald Van Loon and Daniel Cronin explored the ability of AI-based solutions to add cognitive computing to DevOps. For example, Van Loon described "using machine learning algorithms to match human knowledge with log data, combined with open source code repositories, forums, and social cues. With all this information, a database of relevant insights is created, which may contain solutions to solve a large number of critical problems that IT operations and DevOps teams face every day."

Kinsbruner warns that some careful thinking is needed before AI can really relieve some of the burden on DevOps teams. "Developers must first understand what they want AI to help them do within the SDLC and across DevOps, and how. A logical place to start is to understand how they can best leverage AI to analyze their test automation strategy."