AIOps is the way forward for future ventures

According to a survey conducted by IDC, 64 jetabytes (Zb) of data was generated in 2020 and this trend is expected to increase by 23% annually by 2025. This observed explosion of data usage is creating an increasingly complex environment for IT. Teams and increasingly complicate their task. As the situation becomes more complicated, the system needs more time to run smoothly to manage and process the data volume. AIOps are therefore becoming a necessity for companies wishing to conduct their day-to-day operations.

The growing complexity of on-premises and cloud-based digital enterprise applications will lead to unprecedented increases in adoption of modern architectures, such as containerization, data volume and complexity. While generated overhead repairs can delay operations and overwhelm IT teams, “noise” data is no longer a barrier.

Implementing sophisticated strategies and centralized AIOps systems enables organizations to enhance the customer experience, ensure application reliability and optimization through intelligent automation, and improve as an Autonomous Digital Enterprise (ADE). Indeed, traditional approaches to IT operations may no longer be realistic, making the adoption of AIOps inevitable in order to measure resources and effectively manage the modern environment.

AIOps in the service of simplification

The combination of artificial intelligence (AI), machine learning (ML), and Big Data Analytics enables IT teams to improve the speed, agility, and efficiency of their operations to improve and automate IT operations. This AIOps method has been made possible by real-time intelligent detection and even correction of problems and inconsistencies. Dealing with the explosion of operational data volume, the complexity of the IT environment, and digital transformation initiatives in general is essential.

It can also help DevOps teams increase system reliability. For example, where customers already have knowledge of pattern identification, using more sophisticated methods, including no-code functionality, may be helpful in applying this user-defined knowledge.

In other words, AIOps also help manage and reduce disruptions and generally reduce the load on IT teams to give them time to innovate and improve the organization. In addition, through automated processes through AIOps, companies can benefit from higher employee satisfaction, improved customer retention and time and resource savings that have a direct impact on results.

Data at the center of the conversion to AIOps

Upstream of an AIOps project, it is important to determine its objectives and benefits, whether it involves adapting to current needs, or identifying areas of ITOps friction that must be processed.

Also, IT companies operate in highly complex hybrid environments. In this context, traditional approaches are not only more expensive, they can quickly become uncontrollable despite the increase in staff. In terms of the amount of data to be monitored and analyzed, AIOps must have an open and interchangeable approach with existing IT tools and data sources.

In the second step, identifying the right processes that promote agility and collaboration between functions will allow integration of development, activity, and security. Also, as much attention should be paid to individuals, including ensuring that the right tools and processes are in place and can be used based on the information collected. The exact effectiveness of this method is largely based on the collection of topological data. So it is not just a question of quantity or volume, but a question of relevance. This allows for in-depth analysis of the data and being able to combine data from multiple sources is essential.

Through this intelligent analysis, companies can move their problem-solving methods from responsive to predictive and ultimately proactive. By giving employees the ability to anticipate problems before they occur, companies can avoid obstacles, reduce the time spent on routine tasks, and free up time for team members to focus on innovation that drives operational excellence and helps businesses move toward the ADE model. .

As data volume and complexity increase and IoT and 5G adoption become more widespread, intelligent solutions like AIOps will prove to be key for data-driven businesses. In the next two or three years, this approach should be adopted as companies will develop into autonomous digital enterprises.

In the face of this continuous data growth, performance requirements and their desire to meet service levels will encourage them to seek out more AIOps. So the future depends on the advancement of technology, reduction of scale economy and generalization of algorithms. AIOps will not only be the tool of choice for some data scientists but also an affordable, useful and easy-to-use technology for tomorrow’s business.

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