A Complete Guide to AIOps

Steve Manjaly May 31, 2022
- 8 min read

IT operations (ITOps) have seen a series of evolutions over the last decade, all in response to the changing market and technological environment. DevOps evolved by combining developer teams with IT operations to create more open communication and collaboration for shorter development cycles. DevSecOps evolved to bring about shared responsibility for security and make it an integral part of the software development lifecycle. 

AIOps evolved to bring the benefits of artificial intelligence and machine learning to IT operations. Let’s explore what it is.

What is IT Ops? What is AIOps?

IT operations is one of the core functions defined by the ITIL framework and is responsible for maintaining the IT infrastructure that delivers services to the organization, its employees, and its customers. They’re responsible for everything from maintaining the server hardware and configuring the networks to provisioning PCs and mobile phones to the employees. 

The goal of ITOps is to ensure that the underlying infrastructure can deliver the availability, performance, and capacity standards defined in the service level agreement. They’re responsible for regular data back-ups and ensuring service continuity. ITOps personnel are also in charge of developing a disaster recovery and business continuity plan and implementing them throughout the organization. 

AIOps introduces artificial intelligence and machine learning to the world of IT operations. The term was originally coined by Gartner in 2016 and stood for Algorithmic IT Operations. It now stands for Artificial Intelligence for IT Operations. As organizations started building AI and big data solutions on a large scale, it was noted that the ITOps that supported these very same solutions often relied on old systems and solutions to manage their processes. 

By introducing AI, the idea was to use automation, intelligent monitoring, and big data analysis to improve IT operations. AIOps collect data from various aspects of IT operations, analyze it, and offer insights into how it can be improved. And ITOPs can adapt and cope with the evolving demands presented by organizations. 

AIOps are seen as the key to helping businesses with their digital transformation and supporting faster development and deployment in the age of DevOps, IoT solutions, machine learning, cloud computing, and other innovative technologies. 

How does AI play into the world of IT Operations?

AIOps present a shift from traditional IT operations and place a larger emphasis on data and data analysis. It encourages breaking down traditional data siloes and bringing together data from multiple sources. In fact, one of the major features of AIOps is its ability to manage diverse and large volumes of data. 

The two major aspects or components of AIOps are big data and machine learning. 

AIOps capture a large amount of data from different tools in ITOps, from network monitoring tools to ticket management solutions, irrespective of the vendor. And it needs to be able to store and process large volumes of data from multiple sources and bring them together in a way that makes sense. 

Machine learning is used to fast track and automate the analysis of this data to find patterns or trends and predict issues. The insights from these solutions are then used to improve IT operations and prevent issues in the future. 

AIOps can bring together all the data from multiple sources and filter it to remove any noise or false positives. This large amount of data may contain valuable insights which simply cannot be processed manually. This is where machine learning comes in. Here the patterns and trends are further analyzed to find the root cause of the problem which is then used to automate processes and prevent issues.  

An AIOps platform does all of this, aggregating data from multiple sources, analyzing them to find patterns and then root causes, and making sure it reaches the relevant personnel. 

Gartner’s 2021 market guide for AIOps divides AIOps into three continuous segments: Observe, Engage, and Act. The observe part is where data analysis, performance analysis, and anomaly detection happens. The engage part is where we use AIOps for ITSM and the act part is where we automate processes, based on insights gained from AIOps. 

As with IT operations, AIOps is continuous; the idea is to get continuous insights by collecting and analyzing data and using them to continuously improve IT operations. And because of this, AIOps is referred to as the CI/CD for IT operations. 

What are the benefits of AI Ops?

AIOps were developed to help IT operations help organizations in their digital transformation and cope with the rapid pace of innovation. Here are some of the benefits of AI Ops. 

Data-driven decision making

AIOps take out the guesswork from IT operations. They empower the operations team to monitor, collect data from, and analyze every process and activity they carry out. 

Consider the simple process of demand management. ITOps can bring intelligent analytics to predict the resources they’ll need based on the patterns of business management and historical data. You can reduce the resources allocated as a buffer or the just-in-case-its-needed resources. 

Organizations can develop better risk management strategies using the data and plan their resources accordingly. Even something as simple as the possibility of a hard disk failure can be calculated with intelligent analytics. 

You can reduce the need for eye-balling it. 

Improved operational efficiency

AIOps optimize how the IT operations team functions. It removes the repeated tasks from the workflow and leaves the team for activities that need more attention. AIOps can identify bottlenecks in the workflow and help ITOps streamline their activities. 

For example, IT teams are responsible for frequent data backups or monitoring the IT networks for any issues. But with AIOps, IT operations teams can automate these tasks. AI systems can ensure that if any IT systems are facing problems, the relevant personnel (and only them) will be alerted on time. In fact, they can even spot errors or failures even before they happen and help ITOps make their plans accordingly. 

AIOps can also reduce the noise in IT operations. IT operations are sometimes cluttered, creating confusion among workflows and inconsistencies in data streams. AI tools can filter out this noise, removing erroneous data logs and making robust models for ITOps. These systems can learn to send which alerts are to be sent to which team. For example, if a disk failure issue is to be sent to the networking team, the database team, or another team. 

Better response times and decreased downtime

As mentioned before, AI systems can predict and detect failures or issues with enough time. This will help IT operations to respond quickly and fix them before they cause a service discontinuity. AIOps can also empower IT teams to automate intelligent responses to incidents and improve response times. 

AIOps can help businesses bring down their mean time to detection (MTTD) and mean time to resolve (MTTR). Advanced anomaly detection systems can continuously monitor systems and look out for any malfunctions or anomalies before they become a problem.

With predictive analytics, AIOps can also make the team more proactive. For example, they can analyze the usage patterns to see if a network bandwidth may reach its limit or if a server may overheat. 

Improved cybersecurity

With advances in monitoring and detection systems, the organization’s cybersecurity will get a boost. With organizations going remote, many businesses are using AIOps to secure their employees’ devices that are distributed geographically. 

According to an MIT report, Siemens USA was one of the leaders here; their AIOps system collected data from all of their employee devices and analyzed them continuously to detect any threats. 

As cyber threats continue to disrupt operations and cost businesses millions in revenue, AIOps is a potential solution that can mitigate this risk, along other strategies like creating a culture of cybersecurity at work. These solutions can proactively identify threats and automatically initiate responses saving precious reaction time and protecting the organization from further damage. 

Drawbacks and challenges of AIOps

AIOps is a fairly new concept and organizations are only dipping their toes into it. As IT operations try to implement AIOps, they often face a couple of challenges. Here are some of them. 

Lack of AI and ML expertise

AI and Ml are fairly new fields and while they have been used to make applications, their application in IT operations is not tried and tested. So while IT teams are familiar with the benefits of AIOps, they’re finding it difficult to implement it, or even determine where they can best use it. AIOps experts are not easy to find and since they’re not used in IT operations, it's hard to find an ML expert among them. 

This may change soon as more organizations are investing in AIOps and more knowledge and best practices come to light. 

Not having a clear set of goals 

As with any IT initiative, AIOps need a clear set of goals to work towards. Mostly stemming from a lack of ML or AI expertise, IT teams often have unclear or unrealistic expectations from AIOps. AIOps are not a complete solution that can eliminate ITOps or one switch that can detect and stop all cybersecurity threats or predict all failures. 

Before investing in platforms or solutions, the ITOps team needs to gain a clear picture of what AIOps can solve and what they cannot. 

And they need a top-down approach, determining the problems they need to solve, defining metrics of success, and then using AIOps to achieve that. 

Going all-in on AIOps without understanding it and trying to improve processes without looking at the overall picture may not work out well. 

Poor quality or unstructured data

ITOps have been fully manual for a long time and the personnel and their processes have adapted to these manual processes. And the documentation or processes have revolved around humans and making processes or requirements clear for them. For example, tickets have been highly descriptive; teams may use labels, but that was purely for analysis. 

But when you move for machines to understand these processes, it meant that most of this information was unstructured. These historical data had to be structured and normalized before you can use them for ML or AI.

This also meant that organizations had to rethink their way of working to get the most out AIOps. They had to adapt their workflows and processes to leverage AI for the best. 

Difficult to get teams to adopt and trust

AIOps can detect and defend against threats automatically. But how do you know it just can? More than that, how can you get your teams to trust it? Particularly when your team isn’t used to AI or ML solutions? This is a problem you’ll have to grapple with once you decide to go with AIOps. 

AI systems are often black boxes, and there’s no way you can tell how an AI predicted a disc failure or a cyber attack. This means you may run around trying to fix a false positive. This is a question you’ll often have to defend against when implementing AIOps. 

The answer will be (should be) in the metrics you have defined before implementing the AIOps. It can show you if AIOps is working or not. 

Where can you use AIOps?

There are many applications where you can deploy AIOps tools in IT operations. Here are a few of them. 

Intelligent alerting: As mentioned before, AIOps can intelligently route alerts to the relevant teams. It can also filter out false alerts. AIOps systems can also collect data streams from multiple sources and group them together if they’re all triggered by one event, instead of letting alerts from all these systems flood the dashboards. 

For example, if a certain router goes down, you don’t want alerts about networks going down, server outages, or any number of related issues pinging all over the place. This is what intelligent alerts prevent. 

Root cause identification: These solutions help companies quickly identify the underlying cause of a problem and launch a response. ML-based root cause identification solutions can understand problems in real-time and provide insights to resolve them. 

Monitoring: Teams can use AIOps tools to automatically monitor their network for issues, find any anomalies and raise alerts without manual effort. 

Capacity planning and demand management: Using intelligent analysis, AIOps can predict the hardware and networking resources needed to meet the required capacity based on previous usage patterns. IT teams can easily use these tools to find out the bandwidth, processing power, and hardware required to ensure that the capacity and availability standards defined in the SLA are met. 

Read other articles like this : IT General, DevOps, Workplace, aiops

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