If you are involved in managing the day-to-day IT operations, you probably agree that today’s application environments are growing in complexity. The key components of digital transformation – Cloud infrastructure, Software-as-a-Service (SaaS) applications, agile development models – are forcing companies to restructure the way they manage IT ecosystems. Also, Artificial Intelligence (AI), which has picked up significant momentum in the recent past, has shown great promise in transforming IT operations.
AIOps, as it is commonly called, is more than just the application of AI for IT operations. It combines algorithmic and human intelligence to provide full visibility into the state and performance of the IT systems that businesses rely on.
Under the hood, it is a multi-layered technology (Figure 1) that automates and enhances IT operations. It uses analytics and machine learning to crunch high volumes of data collected from various IT operations tools and devices, to spot and fix issues in real-time. Thus, enabling IT decisions to be taken much faster and an efficient manner.
But, how exactly does AIOps make a difference? Is it the silver bullet everyone is claiming it is? We take a close look at the ways AIOps can help you, and where it doesn’t.
How AIOps helps IT operations?
1. Work at a higher speed and scale
Repetitive and mundane tasks which are traditionally performed manually eat up a lot of IT operations time. It gets even more challenging with the ever-growing increase in volume and variety of requests. AIOps automates such tasks and hence frees up much time for them to focus on more strategic and value-adding initiatives.
2. Bring down data silos
AIOps eliminates data silos by correlating information across multiple data sources. It allows for a holistic vision across the entire IT environment – compute, network, storage, across on-premise, and cloud infrastructure. Thus, it enables a smooth collaboration between different teams and accelerates diagnosis and resolution times. Resulting in minimal disruption to business users.
3. Recognize issues faster and accurately
False alerts waste much time of already over-stretched and fatigued IT teams. This is a typical manual error caused due to the threshold value of various monitoring alerts set too low. By applying machine learning models, which continuously learns from the data, AIOps can help you arrive at optimal threshold alerts. Also, alerts monitored in isolation can again lead to inaccurate alarms. Since AIOps eliminates silos, it can analyze data from all the dependent systems and take situation-based decisions.
What to watch out for before going the AIOps way?
- AIOps does not mean ‘fully-automatic’
Having AIOps doesn’t mean that you can turn it on and forget about it. Strategic investment of time and effort is required to train and manage it to reap the business benefits. This is why it possibly makes more sense for larger enterprises with a broad spread of IT systems.
- It is only as good as the data
An AIOps system has to be trained on the various scenarios it should act upon. Hence, a comprehensive and high-quality data-set is critical for effective training. If the data is not valid, or log files are corrupted, or alerts are not fed in real-time, AI systems will fail badly impacting your business continuity.
- Consistency is the key
For AIOps to produce useful results, consistency across your organization’s IT systems is essential. This will significantly reduce the set-up and training time and help experience the business benefits faster. If your organization is following different IT practices and policies across various departments and divisions, you must consider standardizing the processes before going the AIOps way.
AIOps technology is still relatively new, with new use cases unfolding steadily. However, by focussing on prediction instead of reaction, business-driven actions instead of mere analysis, it has built a strong business case for itself. With the rapid strides, technology is taking every single day, AIOps will inevitably mature and soon become mainstream.