With heightening innovation, efficiency and sophistication, the IT environment is becoming enormously complex. The topical shift to microservices and containers has further added to the number of components that go into a single server towards making up your business application services. All of this eventually makes it even more difficult to orchestrate the entire IT system architecture.
The ability of IT operations teams to manage such a variety of configurations and deployments is fairly limited, and hiring more resources can be both costly and difficult to coordinate. In order for us to address those challenges we’ve take advantage of the shift within the industry with artificial intelligence for IT operations (AIOps).
What is AIOps?
Machine learning technology holds a lot of promise for businesses; however, organizations need to use resilient business application services and stronger automation platforms in order to efficiently use the data gathered via such technologies.
AIOps uses machine learning to provide deep insights into infrastructure, supporting scalable workflows aligned with business goals while being easy and inexpensive to implement. It integrates data analytics into IT operations support, enabling scalable workflows aligned with organizational goals that are highly customized to provide invaluable insights necessary in a highly digitized world.
Top Benefits and Use Cases of AIOps for Businesses
An AIOps solution can help you respond more nimbly to situations without needing more resources, and quickly pinpoint areas of improvement while optimising your existing processes. From making sure your data is processed to helping you create machine learning models, AIOps can be an essential value-add to an IT department.
Some of the major use cases of AIOps are –
- Anomaly detection — AIOps software takes into account historical and real-time data to make determinations on the best possible course of action in terms of operations procedures. Such proactive measures can help prevent service downtime or business impacting situations because they are identifying problems before they occur, rather than when they’ve already occurred, thereby ensuring high uptime, resource efficiency, minimal performance impact and great customer satisfaction.
- Causal analysis — Root cause analysis plays a key role in IT problem resolution. However, problems occurring on one tier may affect other tiers of an enterprise app’s infrastructure as well. In IT management, AIOps tools analyse data from all tiers and create causality/relationships to provide detailed insights across the whole enterprise application services environment in respect to problem location and seriousness levels.
- Capacity planning and management — Artificial Intelligence-enabled, data-driven recommendations allow organizations to map workloads to the right servers and machines. In turn, IT teams have the ability to outsource effectively while reducing maintenance cost and increasing IT infrastructure efficiency.
- Alarm management — By utilizing intelligent optimization and closed loop remediation, AIOps tools are able to rectify problems that may arise and take proactive steps to prevent other similar issues from happening. This allows IT operations teams to focus on real problems, instead of triaging alerts from hundreds of different tools that often lead to alert fatigue.
In addition to the above use cases, AIOps is beneficial to companies for reasons such as better UX, lower OPEX and simplified operations ultimately leading to faster turnarounds.
The ability of AI to learn from data, understand patterns, predict and automate actions gives it a huge potential to transform the IT operations world. With the growing popularity of cloud computing, IT operations is facing a paradigm shift in its approach to maintaining the technology infrastructure. AIOps is the new buzzword for the IT operations industry that provides a framework for collecting and analyzing data generated by the IT infrastructure, making sense of it and taking informed action for quick remediation.
AIOps marries big data with ML to create predictive outcomes that help drive faster RCA and accelerate MTTR for business application services by providing intelligent, actionable insights. This provides the ability for ITOps teams to continuously improve via automation and save time and resources too. We have seen a lot of activity in this space in the last few years and are excited about the possibilities that are opening up for the world.