What is AIOps?

Gartner has defined AIOps as

“AIOps (Artificial Intelligence for IT Operations) combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination”

The key elements of this definition are data, data science and the automation of IT processes. For AIOps to be successful, it is essential that there is access to huge volumes of data and the necessary domain and data science expertise to use the data and convert it into meaningful outcomes in the form of automation and actionable insights.

If we were to simplify the explanation of AIOps, it is the conversion of data and domain expertise into actionable insights. The data input here is the data that has been collected from millions of network devices, millions of client devices and tens of thousands of customer configurations. The machine learning from the environmental factors, traffic characteristics, user and device behavior and user experience collected over the years is used to produce AI-driven outcomes that will automate root cause analysis, detect anomalies and preempt issues before they have a broad impact and use peer benchmarking and proactive performance tuning with configuration recommendations. This combination of AI analytics and actions is called AIOps. It is a next generation solution and requires data-driven results that have been proven over a large number of customer installations. They have to be comprehensive enough to apply precision insights and recommendations to individual customer environments.

It is this combination of Data, Data Science, Domain Expertise and proven results that will increase the efficiency and effectiveness of network operations.

The need for AIOps

The need for AIOps has arisen from the increasing complexity of modern networking infrastructure which is due to the increased network size, increased volume of traffic and the number of diverse devices and applications in networks. It has become a challenge to manually configure these networks without consuming large amounts of time and increasing the probability of errors creeping in. It has also become a big challenge keeping up with the exploding number of IoT devices and the distributed connectivity from the remote edges to the branch, campus and datacenters. It is essential that network operators need increased operational and user insights and intelligent automation driven by AI to effectively manage networks.

IT networking professionals today are typically facing challenges that include exploding network requirements, resources being stretched to limits, having to use manual processes, an overload of information and a lack of timely and actionable insights. It is very evident that Artificial Intelligence is required to transform IT Operations. What they need is something that makes life easier by reducing trouble tickets and ensuring SLAs deliver world-class user experiences. What they need is a way to quickly resolve connectivity problems using automated root cause analysis, precise recommendations, and remediation, where AI predicts and preempts issues before they happen. This will then allow IT to focus on the company’s strategic business objectives and on driving business value instead of spending time on everyday mundane tasks.

What are the key benefits of AIOps?

AIOps eliminates manual troubleshooting and can reduce mean time to resolution by up to 90%. AIOps can identify issues, such as connectivity and authentication, using AI to determine root cause and provide prescriptive recommendations to improve troubleshooting efficiency. AIOps allows IT to meet SLAs by predicting issues before they become a problem. This reduces trouble tickets by identifying issues before they impact the business. It Increases network utilization as much as 25% using per site and peer-based configuration optimization. It provides precise data-driven insights and recommendations with greater accuracy. It continuously optimizes performance by analyzing data from network devices and sites.

What are the critical elements for AIOps to be successful?

There are some critical elements for the success of AIOps. Access to a large volume and variety of data is needed to train the models. This is essential as it is a well know principle of AI that the models learn to accomplish a specific task based on continuous training on the data they are intended to process. When Machine Learning models are presented the wrong data or too little data to train the models, the results are unusable. This means that if the AI used to make configuration changes has not seen enough data to match a given network, then you cannot rely on it to improve the performance of that network. That is why data is so important. There should be available domain expertise which is required to know which problems to attack, analyze and provide prescriptive actions with greater accuracy. A team with strong data science expertise is required to match the right technology with the right problem and with years of experience of customer validation of AI operating in live environments. Also critical is the ability to be able to scale to any size organization.

Summary

To summarize, AIOps is the combination of Data, Data Science, Domain Expertise and proven results that will increase the efficiency and effectiveness of network operations. It is the conversion of data and domain expertise into actionable insights. The need for AIOps has arisen from the increasing complexity of modern networking infrastructure which is due to the increased network size, increased volume of traffic and the number of diverse devices and applications in networks. It has become a challenge to manually configure these networks without consuming large amounts of time and increasing the probability of errors creeping in. AIOps will allow IT to focus on the company’s strategic business objectives and on driving business value instead of spending time on everyday mundane tasks.

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