The term AIOps was first used by Gartner in 2016 to describe the use of artificial intelligence (AI) to benefit IT operations. Modern IT deployments must increasingly deal with constant demands for more data and faster access to data. Often times this data is unstructured and streaming in real-time from a large network of siloed sources. A major part of AIOps involves the collection of this mass of data from various sources and analyzing that data to give actionable insights to organizations. By utilizing this big data through the means of modern machine learning and other advanced analytics technologies, I.T teams can, directly and indirectly, enhance operational functions such as monitoring, automation and service desk processes. AIOps platforms can be used to help IT teams make sense of the volume of the big data by using correlation and in some cases machine learning to identify and remediate problems, trigger automatic responses to current or potential issues and, in some cases, suggest remedies.
A major focus of AIOps is to reduce decision fatigue. Many vendors have added AI capabilities to existing application performance or system management tools, or vice versa, and offer AIOps as a service. If the system itself can make decisions and take action on the mundane tasks and assist operators in times of reduced or violated SLAs, having a robust system is easier and incidents can be avoided.
Organizations are increasingly turning to AI to help manage, optimize and secure complex IT systems. This article breaks down AIOps, explaining how AI works, its pros and cons, and how IT pros can best take advantage of it.
How Does AIOps Work?
The concept of AIOps is not new. Business intelligence professionals have always used data analytics and AI to make smarter and faster business decisions. AIOps can also be used by IT professionals to harness the power of machine learning and data science to automate the analysis of data streaming from IT monitoring tools.
If you would like to start exploring AIOps, you will first need to investigate the use of an AIOps platform. The job of an AIOps platform is to ingest, index and normalize data on thousands of events per second from components across your IT infrastructure, ranging from PCs and sensors on the Internet of Things to servers, network routers and firewalls. Advanced data analytics and machine learning tools then correlate and examine the data, often comparing them with baselines of “normal” activity and to patterns that preceded previous outages, slowdowns or cyberattacks. The tools alert system administrators, security operations or business users to potential problems. Some vendors also use AI and ML to screen “noisy” (irrelevant or incorrect data) to ensure higher-quality analysis. In addition to alerts, many AIOps platforms can trigger automated responses to issues or recommend manual remediation steps, again based on AI analysis of previous responses.
Machine Learning and AI
As suggested in the name, of course, the core characteristic of AIOps is Artificial Intelligence. Machine learning (ML) uses predictive and intelligent analysis to supplement and enhance a system’s decision-making ability.
Another main characteristic of AIOps involves monitoring all the layers of the IT ecosystem. This includes the monitoring of server, storage and network systems, as well as all relevant metrics including customer experience and application performance.
AIOps systems should be easy to use and intuitive, giving users only the information they need to see to optimize IT operations.
AIOps systems need to be able to analyze and process large amounts of data at speed. Real-time processing allows enterprise IT organizations to respond immediately to issues like anomalies and security breaches.
AIOps tools must correlate data from a wide variety of sources and identify new insights from it. While very few solutions will currently work with all data sources, we expect these capabilities to grow over time.
API integration is central to AIOps tools, enabling them to serve as a thin meta-data management layer linked to existing management tooling rather than requiring their replacement.
AIOps systems should examine events, incidents and resource usage to identify anomalies that may signal an attack, such as a significant increase in disk I/O activity that could indicate ransomware reading and locking many data files.
AIOps systems should adjust IT systems for maximum efficiency and effectiveness, taking into account everything from cost to flexibility.
Analytics and alerting
AIOps systems should employ metric-based reporting and analysis, in which business outcomes are tied to relevant metrics, thus directly linking IT performance to business activity.
Platforms and environments
AIOps tools should be able to work agnostically across cloud environments as well as on-premises. This reach should also extend to the IT development teams, and to the most popular development platforms for cloud-native and traditional application development.
Compliance and privacy
AIOps tools should be able to understand when a system is out of compliance or an unusual event has occurred, thus reducing the time required to restore compliance. This includes tracking patch levels, the workloads running on a system and its criticality to the business.
A true AIOps system is able to recognize and follow complex rules and patterns, in order to accurately detect and assess events, and respond appropriately.
This is one of the key reasons why AIOps is receiving such enthusiasm from the industry. Effective AIOps solutions and systems reduce IT operators’ workloads by automating menial or repetitive tasks, increasing efficiency on the human side of the enterprise.
Top 5 AIOps Use Cases
AIOps is not only a great way to clearly flag current problems and inefficiencies, but more importantly, it gives you the ability to peer ahead and predict future potential issues. AIOps “can ensure that potential problems are proactively flagged before they become actual problems that impact end-users, by filtering and correlating data ingested across the IT environment, including third-party solutions. There are many systems and processes that can benefit the introduction of AIOps.
The top five uses cases among companies using the AIOps strategy are:
- Predictive Alerting
- Root Cause Analysis
- Prioritizing Events
- Predictive Outages
- Service Desk Ticketing
In other words, AIOps provides a second pair of eyes whose AI-based pattern tracking can help forecast the future.
Why You Need AIOps
AIOps is not just an optional luxury anymore to those teams that can afford a solution. It has become a necessary tool for all teams from simple to more dynamic and complex IT environments. You will find that no matter the size of the environment, you will already be using at least a portion of AIOps, whether it is network monitoring or a firewall collecting security logs. By only using a portion of AIOps you are only capturing a portion of the important information that is relevant and can often reside on multiple systems. Traditional IT management solutions cannot keep up with the volume of data and cannot intelligently sift through events from the sea of information from multiple sources.
By implementing AIOps at its fullest, you will improve the efficiencies of the I.T department, provide better visibility of your IT environment, and allow your IT team to proactively respond to any technical issue that arises. AIOps builds real-time systems in the form of context-rich data lakes that traverse the full application stack in order to reduce noise in modern performance and fault management systems and drive automation—with the ultimate goal of improving time to resolution.