The critical role of AIOps in managing enterprise application solutions

The digital transformation of companies has led to the creation of an IT infrastructure comprising giant data warehouses and hybrid and multi-cloud systems. The development has led to the generation of huge data sets from various channels, customer touchpoints and device platforms. The unprecedented pace of data generation makes it difficult for organizations to manage IT, which is critical to optimizing operations, improving monitoring, and pursuing business continuity. Given the limitations of existing IT solutions to manage data, companies are leveraging AIOps to perform a myriad of activities. These include understanding and predicting customer behavior, detecting anomalies and determining their reasons, and offering prescriptive advice. It helps to detect dependencies responsible for creating problems in an IT infrastructure. Furthermore, with AI that has features like containerization, continuous monitoring, predictive or adaptive cloud management, companies can gain a next-generation perspective on their business.

What is AIOps?

It is a software system that encompasses big data, machine learning, and artificial intelligence to improve the capacity and performance of all primary IT functions. IT functions may include automation, IT service management, performance monitoring and event correlation and analysis, among others. In other words, AIOps is applying data science and machine learning to the DevOps framework to make it more efficient and productive. The benefits of integrating AI into the value chain are:

• Quickly and accurately process all types of data generated from various sources. This translates into ensuring data integrity and achieving tangible results.
• Analyze huge data sets to generate actionable insights for DevOps engineers to understand and make infrastructure adjustments (if needed).
• Identify event patterns and set automatic triggers in response.

AIOps vs. DevOps: the difference

DevOps is arguably the best software development methodology that increases the speed of implementation of quality software solutions in any organization. So why has AIOps become such a crucial requirement for businesses? Let’s find out.

• The main difference between AIOps and DevOps is the multi-layered formation of the former that can automate IT operations and enable algorithmic analysis on its own. On the other hand, DevOps transformation involves leveraging agile development methodologies and using them to automate self-service operations.
• AIOps executes tasks in real time without human intervention. You can analyze and organize IT tasks based on data sources, which traditional DevOps cannot understand, much less execute.
• AIOps can perform a host of data-driven analytics activities, such as streaming data management, historical data management, and log data ingestion, among others. You can enable stakeholders from multiple business units to view information by taking advantage of visualization capabilities.
• While DevOps QA can automate build deployment using containers and automation tools, it lacks areas like security and compliance and system operations.
• DevOps QA helps streamline SDLC through CI/CD pipelines, while AIOps offers a scalable platform to automate and manage IT operations involving huge data sets.
• The importance of AIOps will increase in the coming days as next-generation enterprise applications running across multiple cloud ecosystems will need to be monitored and managed in real time.

Why should companies adopt AIOps?

Building and deploying next generation business applications would involve the use of AIOps methodology powered by artificial intelligence and machine learning. The benefits of leveraging this next generation methodology are:

Eliminate IT noise: IT noise can expose teams to false positives, hide root cause events, and make outages difficult to detect. It can also lead to performance issues, increased operational risks and costs, and the undermining of enterprise digital initiatives. AIOps-powered tools can reduce or even eliminate noise by creating correlated incidents that point to the root cause.

Superior customer experience: With customer experience becoming the most crucial factor in driving profitability, AIOps can perform predictive analytics and automate decisions related to future events. Through data analysis, AIOps can predict events that affect the availability and performance of IT systems. Plus, by identifying the root cause of IT problems, you can help resolve them instantly.

Best cooperation: AIOps can break down functional silos and streamline workflow for IT groups and other business units. You can generate custom dashboards and reports so teams quickly understand their tasks and act on them.

Improve service delivery: AI, ML, and automation can help any company’s service delivery team resolve queries by analyzing usage patterns, support tickets, and user interaction. By applying probable cause analysis, you can predict underlying performance issues and help resolve them.

conclusion

Although DevOps test automation is the de facto standard for enabling IT process automation, AIOps can be a completely different ballgame. It can rightly take over from DevOps as its next-generation avatar by minimizing companies’ reliance on specific automation tools. In addition, AIOps can monitor the behavior of the IT infrastructure and, by aligning data resources, can streamline work processes and drive profitability.