Common Use Cases for Observability With AIOps
Harness the power of AIOps to improve your observability and monitoring efforts.
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“We can't build tomorrow using yesterday's tools.” - Scott McDonald
IT infrastructures have been evolving constantly and rapidly, along with Big Data. Businesses worldwide are moving from predictable and static physical systems to intuitive software resources that can reconfigure and adapt based on consumer behaviours.
This evolution brings a rise in the demand for dynamic technology that can help businesses make this shift seamlessly and stay in tune with IT advancements. AIOps has been the driving force in assisting companies in adapting to the continually evolving environment and enhancing their operational capabilities.
AIOps, simply speaking, is the application of AI technologies to IT operations. Through intrinsic analysis of IT data accumulation, AIOps helps DevOps and IT Ops teams enhance their agility and detect issues with the digital services to resolve them before the customers, or the business operations are affected. The advantages of using AIOps include threat detection, event correlation, auto-remediation, and capacity optimisation.
Let’s take a look at some common use cases for observability with AIOps to understand the true scope of how it can help businesses.
Exploring Observability With AIOps
Digital transformation is no small feat. With information technology driving the transformation efforts, AIOps helps organizations operate at speeds that are necessary for the modern business environment. Here are some everyday use cases which demonstrate how observability with AIOps is aiding businesses in elevating their standards.
Monitoring Operations in Cloud-Native Companies
Small and medium-sized enterprises have been one of the primary users of AIOps in their operations, especially the ones that are native to the cloud. Since these businesses need to develop their software and release updates to the consumers regularly, they leverage the observability powers of AIOps. It helps them to sharpen their data analytics capabilities and detect consumer behaviors consistently. AIOps also helps SMEs predict potential glitches, outages, or malfunctions and prevent them before happening.
For instance, some operators or SMEs can subscribe to notifications for any non-critical events, which can help them identify and improve on any new automation opportunities and reduce the thresholds that can potentially be breached. Similarly, SMEs can subscribe to alerts for any critical events like a potential cyberattack, helping them save their precious data and protect valuable consumer information.
Automating On-Premise and Hybrid Cloud Environments
Organizations are starting to move their workloads to cloud platforms for the various benefits that they offer. However, it is also practical for them to keep their IT infrastructure-specific applications on-premises. This is where hybrid cloud environments come into play.
Hybrid cloud environments come with their own set of challenges for the DevOps and ITOps teams. AIOps helps the operations teams maintain a holistic and centralized control over multiple environments and automate repetitive processes that otherwise consume precious person-hours.
Automated intelligent remediation is one of the most significant advantages AIOps offers in this domain. Identifying known issues and automating the process of closed-loop remediation proves to be of great benefit for the ops teams. AIOps also provides intuitive insights into the most statistically plausible approaches for the remediation process.
A leading example of this is Transamerica Life Insurance. The insurance company harnessed the power of automation and machine learning to boost their staff’s productivity in event management. The company could automatically handle over 90,000 events, which subsequently saved over 9,000 hours of employee time. This allowed the level-2 employees of the insurance company to devote more time to strategic activities and boosting the revenue of the company.
Seamless Collaboration of Dev and Ops Teams
Organizations that operate with a DevOps model can face difficulties maintaining a proper alignment between the various roles. AIOps model helps organizations in seamlessly integrating the Dev and Ops teams or systems. By granting the operations teams full visibility into the developers’ work and providing a holistic view of the IT environment to the Dev teams, AIOps makes both the teams’ collaboration much more streamlined.
This subsequently helps the CI/CD models run without any interruptions and helps in the quicker development of apps and software.
Gartner recently highlighted in its “Augment Decision Making in DevOps Using AI Techniques” report that by 2022, AIOps platforms that monitor, support, and deploy applications will help DevOps teams increase their delivery cadence by 20%.
Seamless Digital Transformation
The digitalization of business processes requires competent and automatable IT technologies for operations to be agile, efficient, and quick. A successful and holistic digital transformation requires cohort analysis capabilities, coupled with a situational understanding of multiple domains. AIOps offers this support to businesses. It provides the ability to collect data from many resources and gives a collective cross-domain overview of various systems and silos.
Monitoring Complex Environments and IT Infrastructures
If your company has an extensive IT infrastructure that is immensely diverse in the types of technologies in use, AIOps can significantly improve your monitoring and observability. The growing demand for agility in IT for large enterprises can only be accommodated by future-ready solutions like AIOps, which can eradicate the problem statements of scaling and complexity like no other.
What’s Next for AIOps?
AIOps has played a significant role in helping businesses reduce their churn and boost productivity. Acting as a synthetic brain bringing together various tools and acting as a central layer for monitoring and coordination, AIOps is an essential asset for businesses of all types in the 21st century.
AIOps will continue to gain importance and witness increased adoption in modern IT environments. We'll witness more and more new applications and services built with AI and ML at their core with the aim of helping businesses intelligently identify issues and automate their remediation. Observability is only going to get easier with the adoption of AIOps-backed tooling.
Published at DZone with permission of Ajit Chelat. See the original article here.
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