Ensuring Agility And Quality Using Devops

CIO Review Europe | Tuesday, December 22, 2020

According to research conducted by Software intelligence company Dynatrace, on an average, organizations release software updates to their critical applications every 41 days and this frequency is expected to increase by 58% in the next two years. Such rising demand for faster releases often puts quality at risk. In fact, over a quarter of respondents (22%) say that they are frequently under pressure to meet the demand for rapid innovation that they must compromise code quality. To strike a perfect balance between speedy digital innovation and quality, it is essential to scale up DevOps practices to accelerate the release of high quality digital services. In the present era, IT has gradually evolved from a support function to a mission-critical role for the majority of businesses. Henceforth, Developers and DevOps professionals are suddenly surrounded by possibilities, with a renewed focus on rapid improvements and new developments.

“The use of cutting edge predictive analytics in DevOps and AIOps facilitates companies to deliver faster while retaining the quality”

In the aforementioned context, there will be a rise in popularity for Microservices Architecture among companies. The Microservices Architecture's ability to connect fragments of units separately allows DevOps to focus solely on the individual units, which is critical for the speedy delivery of complex systems. The combination of DevOps with Microservices design saves money, time, and resources. Also, COVID-19 has created a remote-work environment with individuals working from all over the world, thereby exposing several security flaws. With the rise of security and cyberattack dangers, it's more vital than ever to integrate security into every layer of corporate operations. Businesses will increasingly use a DevSecOps process to integrate security into their DevOps processes for early detection and mitigation of vulnerabilities, automated quality assurance testing and many more.

Use of cutting edge predictive analytics in DevOps and AIOps facilitates companies to deliver faster while retaining the quality. Predictive analytics are critical for avoiding latency and managing changing user requirements. Using Predictive Analytics in DevOps and AIOps allows companies to maintain a strong focus on continuous improvement in DevOps services. It gives the infrastructure and operations teams a better understanding of the resources and services in use, as well as how they might be employed in the future to get the greatest results.

Organizations using old approaches and out-of-date technologies often find it cumbersome to handle large volumes of data in daily operations. To ease such problem of volume, speed and diversity involved in data analysis, a DevOps method based on Artificial Intelligence (AI) and Machine Learning (ML) can be implemented. This technology allows enterprises to compute and analyse data of any size and scale. It also improves their workflow, allowing teams to efficiently build, produce, deploy, and manage apps.

Businesses often rely on a variety of internal self-service platforms, which tend to be bulky and lack standardisation. As a result, cross-platform code cannot be as lean and automated as it needs to be. Reducing reliance on self-service developer platforms is a viable alternative for firms wanting to grow their DevOps rather than investing valuable time and talents in standardising them. Further, use of infrastructure automation and a continuous delivery model enables DevOps teams to plan and execute self-service, automated delivery services on-premises and IaaS environments and provide customer-focused agility and robust improvements. Such technological innovation can reduce staffing cost, ensure better reliability with faster updates and improve collaboration between teams.

While DevOps remains the greatest answer for enterprises looking to save time and money, improve quality, and shorten time to market, it does necessitate updating and incorporating contemporary tools and processes to keep up with changing market demands. After considering the DevOps trends and forecasts in the recent years, it's safe to state that self-service capabilities, hybrid design, prioritising edge, AI and ML integration, and becoming cloud-centric in an all-digital world are the future of DevOps.

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