As we know, IT operations management can be pretty complex. However, there are some relatively straightforward solutions. And, on that track, AI has paved its way into the IT world. As a result, AIOps platforms have come to stay! Today, we’re highlighting the opportunities AI provides to achieve AIOps insights. But what is AIOps? Let's find out!
The term AIOps emerged in 2016 through a Gartner report. This technology combines two specific words, AI and IT, which encompasses Big Data and Machine Learning capabilities. These two areas are essential in the IT world, especially in the AI field. This type of software is ideal for analyzing event telemetry. AIOps identifies the value patterns that provide information and support efficient responses. Some specific characteristics define AIOps platforms. For instance, these use advanced mathematical models, correlation, and analysis. Further, AIOps supports operations like automation, monitoring, and service desk. Last but not least, this approach collects, records, and transforms data sources into aversion readable, like graphics or histograms. AIOps platforms use data to collect, present, and analyze technology.
Users who use AIOps relate to DevOps, cloud computing systems, and big data analytics. The goal of these tools is to improve network infrastructure and security. According to an analysis, at least 40% of companies globally will use AIOps by 2023. AIOps are ideal for large companies that need constant monitoring of their operations. Small businesses can also make use of these platforms. Yet, they need time and money to maintain this type of system. AIOps also supports extensive data analysis, as seen in Johns Hopkins’s fantastic Covid-19 Maps on GitHub. This model computes comprehensive data pulled from different sources.
There are three essential phases in AIOps structures.
In this phase, it's necessary to process the data in real-time. The AI detects the problems triggered by the anomalies returned by the data. These anomalies pass an analysis process and, after that, a clustering process.
AIOps platforms notify the human team about possible problems. Problem filtering reduces IT operators' workload and prevents alert fatigue. In short, there are no repetitive operations.
Automation levels increase when workflows apply to routing. It doesn't matter if there is human intervention or not. A great example is Transamerica, which has saved more than 9,000 work hours for its employees with AIOps, allowing human talent to focus on other activities.
AIOps is a set of techs surrounding a platform. These platforms rely on AI to cover the needs and responsibilities of an IT team. There are some essential components to an AIOps system.
The main component of AIOps software is data from different sources of integration. The elimination of data silos allows for easy infrastructure maintenance and monitoring. In turn, it enables the network's event correlation to determine the cause from the source.
Real-time processing balances the ITOps and security analyst's responsibilities. AIOps tools are ideal for detecting security anomalies in real time.
AIOps comprises algorithms that include creating app rules and pattern recognition. Companies can use ML algorithms to design their own rules.
Domain algorithms are characteristic of a business operations or IT environment. Their content and structure are born from the organization of IT data.
AI takes care of intelligent and big data analytics. In turn, they integrate mathematical models that synthesize a deep analysis. They correlate and analyze data to develop histograms, visualizations, and graphs.
One of the main reasons AIOps tools exist is to reduce the IT operators' workload. Automation is an essential element of AIOps as they automate real-time tests. It includes integrating new features, log analysis, anomaly detection, and more. Each AIOps tool varies, but they always keep similar workflows. It seeks to solve security problems, provides solutions, and saves time.
At the same time, we can highlight five algorithms that AIOps tracks.
● Data. Data selection is optional to determine potential problems nowadays. However, activating it enhances problem detection for up to 99% of the data.
● Patterns. Data similarity studies start from a logical point of view for further analysis.
● Inference. AIOps discovers a problem's causes and studies issues that may affect operations.
● Collaboration. This approach informs IT operators about possible data relevance. They also preserve relevant data that can speed up future problems discovery.
● Automation. Here, automation improves fast, concise, and precise solutions. It optimizes execution processes and reconciles best practices to create more efficient alternatives.
AIOps allow teams to identify, address and resolve problems in complex environments. This function translates into different beneficial points, like the following:
AIOps can identify the root cause with noise reduction and data correlation. To all these, it's ideal for providing quick solutions. With this mechanism, the Vivy infrastructure reduces its MTTR by three days and one day. It is even less.
Automatic identification and scheduled responses help reduce costs. In turn, it allows the reasonable distribution of resources. Besides, it enables human staff to focus on other complex activities. Providence saved over 2 million dollars and certified its performance during peaks.
Using AIOps makes it easy to collaborate between teams. These teams include DevOps, ITOps, security, and governance. Dealerware completes further visibility to its architecture represented in containers. As a result, it got better efficiency during the pandemic and cut delivery latency by 98%. With predictive analytics, AIOps target alerts with greater urgency. For example, Electrolux accelerated problem-solving from 3 weeks to one hour. At the same time, it saves more than 1000 hours per year by automating tasks.
Integrating AIOps in IT ops is a considerable challenge, and we can't deny it. To include AIOps in a company, there's a series of requirements:
1. Select the right platform to start automation processes.
2. Determine which manual functions are candidates for automation.
3. Instruct and introduce automation knowledge and practices to build a trustful framework.
4. Collect different data and its inclusion in the AIOps platform.
AIOps gathers enough AI to face the dynamic IT environment and digital transformation challenges and complexities. Artificial Intelligence for IT Operations is becoming part of our day. Consequently, complex IT operations are more and more common. To invest in an AIOps system, you must have a solid plan and business case. Analyzing the time and effort required by IT solutions is decisive. Do you want to incorporate AIOps capabilities into your business operations? We can help you!
