What is AIOps?

Data is being collected at such a rapid pace that human intelligence has hit a ceiling and is quickly becoming overwhelmed. This is one of the major problems currently facing the IT industry. Technology is constantly evolving, and so is the quality and quantity of data being produced. Data collection and processing has seen exponential growth each year. With no apparent signs of this growth slowing down, how will the IT industry adapt?

The solution lies in artificial intelligence for IT operations, or AIOps. Many big names in this space have reported that AIOps will be adopted by the vast majority of organizations in the coming years. Gartner estimates that up to 30% of enterprises will have incorporated AIOps into their infrastructure by as early as 2023. Broadcom also found that 90% of IT professionals agree that AIOps will pave the road for the future of the IT industry.

Before we go further let’s take a look at the history behind AIOps.

The Information Technology Roadblock

Information technology, also known as IT, started out as a means to support businesses. As technology has changed over the years, however, so have the digital demands of businesses. Today, it’s not uncommon to find specific apps, platforms, or even services tied to individual businesses. Software unlocks new ways to host the customer experience entirely through a digital space.

Users have rapidly shifted towards communicating with businesses through digital platforms. How well a consumer can interact with a business online ultimately determines how valuable the business is in the eye of the public. Businesses that can’t keep up with rapid market growth and new trends will often find themselves struggling to keep their heads above the water. 

This transition of increased digital services has introduced a necessary shift towards the adoption of cloud services. Services that operate using the cloud have created vast new opportunities for businesses. However, they have also presented a new challenge for IT departments.

Cloud services offer a variety of microservices. These can range from storage and file management to security and performance monitoring. Each of these microservices also includes a monitoring service which helps to keep track of the service metrics and the data it processes.

The result? Organizations are now generating terabytes of information. The rapid collection of this information has become known as big data.

In order to keep up with the collection demand, IT infrastructure had to change and adapt. This caused existing infrastructures to become even more cluttered with applications and services.

Today’s IT environments often rely on tech stack combinations that involve complex digital integrations and deployments, multi-cloud architectures, and multi-layered security tools. As one problem was solved, the need for additional tools arose. Now other tools were needed to monitor and bring other areas up to speed.

This ended up exponentially increasing the amount of disparate data pools that IT teams need to make heads or tails of. As such, a cycle was created. IT teams were quickly losing the battle, as data collection rates far exceeded what they could handle.

The original approach to this problem was to expand the IT department. More staff meant a stronger ability to combat the increasing demand. Unfortunately, this is not a viable solution in the long run. The demand continues to grow each year, and now outpaces and even overwhelms human intelligence.

At this point, IT teams have now begun to turn to artificial intelligence for help. Thus, we introduce the concept of AIOps.

H2 Defining AIOps?

The term artificial intelligence for IT Operations, or AIOps, was first coined by Gartner back in 2016.

“AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination.”

Gartner

AIOps solutions can aggregate various sources of big data and integrate machine learning techniques to process any influx of information. They bridge multiple applications and services so that the information collected using each product can be pooled, sorted, and visualized in real-time. These solutions typically centralize this process, and display the results through one platform or dashboard.

AIOps tools start out requiring some degree of human intervention. The machine learning models need to ingest data in a specific way in order to learn and develop. By selecting what information you feed into the model, you can ensure that the model will be able to process that type of information faster and more efficiently.

For more information about what AIOps is, check out this video by Atlas Systems:

AIOps vs MLOps

AIOPs is commonly confused with MLOps (Machine Learning Operations). And in some cases, they are used interchangeably. While both appear to reside in the same vein of the IT industry, they are fundamentally different. Both also work to achieve different results.

AIOps:

Uses machine learning and big data with the goal of aiding IT operations. These tools also help with visualizing large disparate data chunks and process automation.

MLOps:

Focuses on standardizing the creation, deployment, and maintenance of machine learning models. MLOps strives to leverage data to improve performance.

One focuses on automating machines to simplify complex operations (AIOps), while the other focuses on standardizing processes to bridge gaps between teams and achieve better performance results (MLOps).

For more information about MLOps, check out our blog post on the topic!

Use Cases for AIOps

AIOps platforms offer a handful of benefits to businesses, such as:

• End-to-end infrastructure visibility
• Anomaly detection
• Performance monitoring improvements
• Root cause analysis
• Reduced IT service ticket volumes
• Better handling of large alert volumes compared to traditional monitoring tools
• Automated workflows
• Predictive analysis
• Machine learning

As mentioned earlier, complex infrastructure designs create large volumes of data that aren’t always connected. This means getting end-to-end infrastructure visibility becomes a challenge. AIOps platforms provide a solution to these observability issues.

Infrastructure Monitoring

Infrastructure Visibility: AIOPs tools aggregate and visualize data sources. This opens the door for better infrastructure visibility. Instead of having various information pools that each need to be analyzed individually, AIOps tools can gather and analyze this information instantaneously.

Anomaly Detection: The anomaly detection capabilities of AIOps platforms help IT operation teams detect and remediate service outages in real-time. IT teams are only alerted to the issues that need to be focussed on immediately, which cuts down on the time it takes to detect an issue. The root cause analysis allows for the teams to better understand the underlying reason for each issue and focus on remediation.

Performance Monitoring: Combining better infrastructure visibility with advanced anomaly detection results in better overall application performance monitoring. These metrics are more advanced than traditional monitoring tools. Gaining insight into multiple components across your tech stack allows you to have a better understanding of downtimes, outages, and other potential problems.

Ticket Automation

Ticket Volumes: Another problem that surfaced as a result of multiple monitoring solutions is the overwhelming amount of IT service tickets and alerts. AIOps platforms are able to clean this up and effectively reduce the volume of service tickets through alert optimization.

Automated Workflows: After compiling and sorting the service tickets, AIOps services can then also automate workflows based on insights gained from this process. Orchestrating workloads decreases service ticket response times.

Predictive Analytics

Predictive Analytics: ​​Another important feature of AIOps is predictive analytics. Predictive analytics is considered a branch off of advanced analytics. It leverages machine learning, statistical analysis, and data mining techniques to identify data patterns. Analyzing data patterns can help predict risks and opportunities within an organization.

Predictive analytics is the result of increased infrastructure visibility, anomaly detection, better performance monitoring, reduced service ticket volumes, and automated decision making. When you combine these features, you gain actionable insights across your technology stack. These insights can be used to predict and strategize your next step.

The Future of IT Operations

AIOps as a concept is invaluable to many businesses. As mentioned earlier, if you can’t keep up with changes in a volatile market, your business will quickly fall behind. AIOps platforms are directly tied to why your business either sinks or swims. It’s safe to say AIOps is the future of IT operations, and you can look forward to the role it’ll play in the years to come!

Click here to learn more about AIOps. Have you used an AIOps solution before? Consider checking out our website and writing a review!