IT operations are quite hectic for companies that provide multiple services including cloud services, on-site services, SaaS applications, and everything in between. IT companies tend to keep performing better and keep the stakeholders satisfied by meeting their expectations, but it is getting harder and harder with every passing day.
The volume of data generated by companies is skyrocketing and they need efficient operations to capture and analyze data to improve their business process. With this recent explosion in data volume, the number of experienced technical IT workers has brought the IT industry in an uncomfortable position.
According to a survey conducted by Gartner, 63% of the executives mentioned that the shortage of skilled IT workers was becoming a problem for their company. By 2030, it is estimated that a shortage of more than a million IT and telecom professionals will affect the US. While a shortage of 756,000 skilled workers is expected in the next 18 months according to the European Commission.
So how will the IT industry tackle such a shortage of a large workforce? One solution which has the potential to save the IT operations industry that the experts are looking forward to is AIOps.
What is AIOps?
AIOps refers to the use of artificial intelligence in information technology operations. This is done with the integration of machine learning (ML) and data science along with big data analytics to improve proficiency and to improve all basic IT operations.
These operations include but are not limited to identifying, troubleshooting, and resolving available and performance-related issues. AIOps was developed to keep the lights on and to make sure that the performance of applications and infrastructure keep on performing as expected. The article will focus on getting to know what AIOps is, how it works, its evolution, and direction.
How AIOps work?
AIOps are implemented as a software platform using cutting edge technologies like machine learning and data analytics in the areas of monitoring, automation, and service desk.
Monitoring is the first thing that comes into play when using AI with IT operations, monitoring is done by collecting data which was previously stored and aggregating it. Since the data is aggregated into a single file, it becomes easier for machine learning algorithms to access the network characteristics and perform better as compared to before.
Another ease that AIOps have provided IT operators is response automation. Usually, IT operators detect breaches using the KPI as metrics, what AIOps does is that it automates the tasks by having a predefined value of KPI set by the IT operator.
These KPI are of specific applications or servers and are defined by running a series of tests to determine acceptable thresholds of KPI or any other metric. Once breached, AIOps software starts an automated root cause analysis and implements a solution if available.
The third area that is most affected by AIOps is the service desk. Every IT organization has its incident management system at its core which is referred to as a service desk. When applied, AIOps software automates the responses to routine alerts, this in return reduces the time spent by IT operators on doing mundane low-level tasks.
Another benefit of employing AIOps for the service desk is that the AIOps tools are capable of feeding the data directly into the incident and problem management process. This acts as a valuable source of data and analysis which drives businesses and improves the core IT functionalities.
Core Components of AIOps
At its core, AIOps can be best described as a wide set of technologies that make up the whole platform instead of just a single app. With its evolution, AIOps has branched into a platform that provides a variety of features but with a single commonality between them, the use of AI. AIOps can be divided into the following basic components.
Real-time processing brings a lot to the table in terms of the benefits associated with it. IT organizations can analyze large amounts of data in real-time with the help of artificial intelligence. This enables the organization to act quickly to security threats and anomalies that are picked up by their AIOps tools.
Usually, the data is scattered previously as every platform had its data collection method which makes it harder to gather all of that data at a single place. Data aggregation is a key component of AIOps as it enables data from different sources like event logs, tickets, and job data into a single file for easier analysis. The aggregated data makes it easier to keep an eye on the whole IT infrastructure and correlation of events which in return makes it easier to get to the root problem.
Every IT organization has its own specific goals and data which is unique to them. These goals and data define the domain algorithms for each of them, specific to their environment and their structure. These domain algorithms are fed to the AIOps tool which in return prioritizes its efforts on the goals of the organization.
AI and ML
Machine learning and artificial intelligence go hand in hand when it comes to AIOps and is considered a defining feature of AIOps. AI deals with the part associated with analyzing the data available to generate an alert based on the circumstances. Whereas ML provides efficient assistance in deciding and predicting when the AI would need to generate an alert to accurately identify anomalies in the network.
Rule and Patterns
Every network has its own set of rules upon which validation takes place, such rules are defined to let the application know which event requires a response. These responses also can be generated based on pattern recognition algorithms. Machine learning tools and algorithms can also be implemented with prior model training. In simpler terms, rules and patterns are used to distinguish between network activity which is considered either normal or deemed anomalous.
AIOps was developed to reduce the workload associated with IT operations on operators. This workload is reduced or eliminated in some cases with the help of automation. Automation in AIOps can be used to automate tasks like real-time testing of new software features and user stories to perform in-depth analysis and to detect anomalies.
Uses of AIOps
The following are some of the uses for AIOps.
- Prevent and predict
- Anomaly detection
- Event correlation
- Intelligent alerting and escalation
- Incident auto-remediation
- Capacity optimation
The evolution of technology has brought many changes to how IT operator has worked previously because all of the previous practices have become obsolete. That is because the amount of data generated by applications is quite large and it is not possible to go through it without modernized tools like AIOps. AIOps makes it easier as it automates tasks like alert generation, anomaly detection, and data aggregation. All of these tasks are done artificial intelligence backed up by machine learning algorithms. A lot of players have entered the AIOps arena to develop their applications, these include Stackstate, Ops Ramp, Opsani, and Dynatrace among others.