Conventional IT tools have a hard time handling the increasing amount of IT related data such as log files and the development, storage, analysis, and management of that data. IT teams are faced with the demanding task of providing speed, protection, and reliability in an increasingly mobile and connected world. To satisfy these tasks, AIOps (also known as IT operations analytics – ITOA) becomes imperative to the management of today’s complex IT systems.
Though industry analysts are not great at predictions, their estimates are still a good measure of market expectations. Analysts expect AIOps market to grow at >30% Compound Annual Growth Rate (CAGR) until 2023 which indicates significant optimism about this industry.
Will AIOps replace traditional ITOps systems?
Yes. While still early in implementation, businesses are taking advantage of AI and machine learning to enhance application support and network management. AIOps, the integration of AI and ITOps, will transform network management.
Today’s IT monitoring architectures produce significant volumes of data volumes that are discarded without detailed analysis due to limitations of most companies ITOps toolkit. Traditional ITOps toolkit include rules based alerts which require pre-written guidelines to direct decision-making. While these rules start simple, over time they become complex and hard to manage.
AIOps includes approaches such as using continuously learning anomaly detection algorithms on log files. This approach puts all the generated IT data to good use, uncovering issues without the need for managing complex sets of rules.
These findings are being validated by practitioners as well. 40% of AIOps Exchange survey respondents said that AIOps is a reasonable substitute for IT event management systems. It is possible to run AIOps in parallel or even against the data produced by older systems. This makes the switch to AIOps a bit more expensive but less risky for IT management.
What are the main AIOps use cases of the future?
An AIOps Exchange survey states that 45% of businesses are using AIOps for better root cause analysis and to help predict potential problems.
Early adoption of AIOps is based on automating repetitive or minor tasks— like filtering through warnings that are created by infrastructure monitoring tools. Advanced machine learning and analytics are the main components for improving IT operations through tracking and automation. The future of AIOps is already underway in the use cases listed below:
Anomaly detection is one of the most prominent use cases of AIOps systems. It can help avoid future outages and delays that businesses can face.
Since anomalies can occur in any part of the technology stack, there is a significant amount of IT data to be processed. Machine learning algorithms can be run relatively cheaply on IT data detecting issues real-time. AIOps can reliably identify the actual trigger that can help IT teams conduct effective root cause analysis in almost in real-time.
An important special case of anomaly detection is security analysis. AIOps can play a vital role in improving the security of the IT infrastructure. Security systems can benefit from AI-powered algorithms to conduct analysis for uncovering data breaches and violations. By linking various internal sources, such as log files, network and event logs, to external malicious IP and domain information, machine learning algorithms can be used to detect risky events through analysis. With advanced AI-powered algorithms, businesses can discover potentially unwanted and malicious behaviors inside their infrastructure.
Optimal Capacity and Resource Planning
Businesses benefit from cloud elasticity to boost their application scaling up or down automatically. With AIOps, IT administrators rely on predictive analytics to improve the auto scale mechanisms. By identifying changes in usage in advance and adapting capacity accordingly, AIOps systems can ensure that system availability levels remain high.
While IT systems are being shifted to the cloud, the complexity of these systems increases as architects introduce new configurations involving parameters like disk types, memory, network and storage resources. AIOps can decrease workload by AI-powered recommendations that are continuously improved as the AIOps system learns resource consumption dynamics.
Data Store Management
Within optimal capacity management, data store management is a major topic. AIOps can also be used to control the network and storage resources. Using AI for both network and storage management, routine activities such as reconfiguration and recalibration can be automated. Predictive analytics can dynamically change available storage space by proactively adding new volumes.
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