For organizations to innovate without harming the user experience , companies in the digital age must adopt modern and intelligent development practices and solutions .
integration chains , and the explosion of data resulting from the move to the cloud . This entire set of tasks certainly adds friction to the development process .
With so much work and no additional resources , the pressure on DevOps teams can force them to sacrifice code quality . As a result , encoding errors are more likely to pass through the network , harming digital services and user experiences . It is a crucial challenge because even the slightest applied changes can bring risks to the performance of the software and the operation as a whole .
It is necessary to find ways to measure the impacts and changes brought by each update and preferably in real-time . It sounds simple , but the truth is that it can be hard to understand the true impact of a new software version until it is released . Worse still , it is often difficult to revert the change when it creates a problem and reverts to a previous , stable version of the application .
Much of this challenge is due to the complexity of multi-cloud environments . Digital services are composed of hundreds of millions of lines of code
and billions of dependencies , spanning multiple platforms and different types of infrastructure . This interconnectivity makes it difficult for DevOps teams to understand the consequences of the changes – however small they may seem .
There is also the overhead of alerting , with cloud monitoring tools capturing a volume , velocity and variety of data beyond human ability to manage . It is often impossible for DevOps teams to quickly find the single line of code that triggered an issue .
To prevent low-quality code from reaching production and to ensure seamless user experiences , organizations need a more intelligent approach to software development . It starts with continuous automation to repeatable tasks , which frees DevOps teams to work on higher-value activities .
Combining this observability with AIOps ( the use of AI in operations ) can take these insights a step further by automatically prioritizing issues according to their business impact . This way , DevOps teams can quickly identify urgent alerts and resolve them before users experience problems .
Improving development practices through AIOps , automation and observability can significantly relieve pressure on DevOps teams and help them keep pace with Digital Transformation . As organizations are releasing software faster , it is increasingly essential to integrate intelligence with continuous , automatic insights across the entire digital services environment . Only then will it be possible to accelerate the transformation and deliver seamless software experiences as customers want . p
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