AIOps is emerging to address the vast challenges faced by IT teams as they come to grips with rapidly expanding tech.
Managing the myriad functions of a modern IT operation has become increasingly difficult. Teams are under incredible pressure to manage a vast array of issues.
In recent years analysis of IT operations and AI solutions have grown to meet this challenge. IT Operations Analytics (ITOA) became a field unto itself, and using big data analytics to study vast quantities of data in order to find solutions. The result is improved information for decision makers and better informed design choices. Add in machine learning, AI and automation along with intelligent systems and white-label solutions, and companies are now able to tackle this monumental challenge through the emergence of Artificial Intelligence for IT Operations (AIOps).
Here, Toufic Derbass, Managing Director, Micro Focus, Middle East & Africa shares 5 guiding principles on deploying AIOps:
Align with your goals
AIOps can seem intimidating, a powerful tool, but hard to apply correctly. The key is to focus on identifiable issues that can be easily fixed and ensure executive buy in. Small projects will allow for easier management and quantifiable goals. For example, using AI to filter and manage internal IT events can drastically reduce noise within the company.
Employ interconnectivity
Entirely integrate the AIOps system into the company’s infrastructure. This is good practice for several reasons. Firstly, the more data, the faster the system learns, freeing the team to work on complex issues. Secondly, errors cascade. Connecting the entire system will allow for AIOps to address every element of the system and correct problems. Finally, issues at the base level may only become apparent further up the stack, the system has to be able to find the root cause.
Pan-domain AI
Domain level tools are often limited in terms of their operations. Having an AI that is pan-domain feels it of constraints, allowing it to point ITOps teams to the root cause of problems, or address the problem itself. This results in a vast increase in first time fixes, saving time and resources.
Central data
Creating a central ‘data lake’ is vital. This includes both historical and live data, to enable the AI to train, to understand what is normal, and what is problematic using historical data, and to learn how to respond, or not, to live data. This also allows the use of single-pane dashboards in ITOps teams that still allow for system wide observation and makes roll outs easier; pilots and projects are easier to deploy and scalability is easier to achieve.
Watch then act
AIOps cannot achieve its full potential until action is taken. AI can be used to address issues, to automatically deal with problems as they arise. It is only when the majority of fixes are made without human interaction that the true value of AIOps can be appreciated.
The use case for AIOps is dependent on your business, but these are the steps you will need to take to reap the benefits.
