A number of ITOps teams have already implemented AIOps capabilities and are reaping substantial benefits. Let’s understand the implementation of AIOps use cases.
Enterprises have struggled to capitalize on their data’s real potential. However, thanks to advances in AI and ML, we now have solutions to manage and navigate through this previously unmanageable data.
These solutions are aggregately termed artificial intelligence in IT Operations.
Although the framework hasn’t yet reached maturity, a number of companies are definitely benefiting from it.
IT teams have been trying to find methods to employ it to simplify their functioning and make their teams more efficient.
AIOps Use Cases
AIOps Use Cases Event Noise Reduction: Ensono
In today’s increasingly dynamic, complex, and interdependent environments, teams are swamped with vast volumes of events.
This increases redundant workloads, inefficiency, and increased risk of missing critical alerts in between all other events.
Adopting AIOps, teams are now able to apply machine learning to real-time as well as historical data to spot patterns and identify and suppress events that come near the range of normalcy.
AIOps can use inference models to group these alerts together, thereby transforming an inbox of overloaded alerts into a few that matter.
This leads to a reduction in event noise, ensuring that the critical alerts are paid attention to more effectively and quickly. With the rest of the event noise being cancelled, the crucial events are better highlighted.
A US infrastructure management provider, Ensono operates IT infrastructure and provides support to mission-critical workloads of many top enterprises.
As Ensono grows its business, efficient monitoring of its IT infrastructure becomes essential for providing unhindered services to its clients.
Ensono is standardizing its technology and allied processes across its various hardware, OS, and applications.
TrueSight Operations Management provides services such as monitoring of performance and event management.
Onboarding TrueSight enabled Ensono to maintain exceptional service to its customers by managing their tickets by bringing down their number from over 10,000 to a few hundred per month.
Often, IT teams find issues in the system after the users do and are then forced to hurry through it to find and rectify the bug.
This hampers service levels as well as staff productivity. AIOps provides the capability to define dynamic baselines that aid in identifying anomalies and generate alerts, predicting them by applying analytics to past and real-time performance metrics.
This way, the teams could start addressing the issues before they affect any services areal and in turn, do not compromise on customer satisfaction.
Place Park Technologies is one such example that is leveraging the power of AIOps to its advantage. Their platform monitors your hardware continuously.
It uses machine learning to predict a fault based on previous and real-time data of the system before it even occurs.
A ticket is created automatically if and when a fault is detected. This ticket includes all the necessary details required to resolve the issue.
Another example in this context is that of TDC NetDesign.
They partnered with ScienceLogic to monitor their networks. The technicians are able to view what’s happening in real-time. It also marks the events according to their priority.
It is near to impossible to manually check the health of the entire infrastructure every day without AIOps.
As complexity in infrastructure and the number of metrics grow, AIOps becomes a crucial resource to implement.
Schaeffler Group is a German manufacturer that produces precision products for various machines.
For better monitoring and early warnings, in its Open Systems, Schaeffler uses IntelliMagic Vision.
By now, IntelliMagic’s AIOps implementation has been done for 50+ storage systems in more than 20 different locations.
IntelliMagic provides users with options to create warning dashboards. It can also be used for trend analyses and to probe into unexpected performance through various drill-downs.
This allows for easy and quick evaluation of new and existing hardware.
Companies often face hindrances in the services they provide to clients on account of the limitations of their performance monitoring tools.
These limitations could also lead to difficulty in supporting hybrid IT environments. Integrating new technologies could become as risky as monitoring the functioning of the newly added modules is limited.
These issues could have a negative impact on current service levels.
AIOps helps companies provide actionable insights to help predict and resolve problems relating to IT operations much quicker and faster.
It monitors the entire system across cloud as well as distributed architecture, summarizes and analyzes the data through relationship mapping, and based on these insights; it defines its future strategy through integrations and automation.
Enablis, a provider of managed communication services, was concerned with the scalability issues regarding its existing monitoring tool.
They selected ScienceLogic as they needed a monitoring platform that was easy to scale and provided all the required services in one place.
With their AIOps solution in place, Enablis was able to save costs that would’ve otherwise been spent in separate automation solutions and ticketing systems.
Their revenue saw over 35% yearly growth on account of improved efficiency.
IT teams in enterprises lack the end-to-end view as they usually work in a siloed approach. This results in substantial time consumption, and hence, enterprises lose precious dollars in the process.
Moreover, in case AIOps encounters a unique incident that it does not have the capability to resolve as of yet, it can be stuck if It does not pass on to a manual resolution channel.
This can hamper revenue generation and customer experience provided for the enterprises.
AIOps uses its AI/ML capability to create a blueprint of every data point, such as laptops, desktops, modems, routers, servers, etc.
With visibility across the enterprise, it helps AIOps to find the real source of the problem which is affecting the system and resolve it sooner.
In case AIOps is unable to, it then triages the incident and pushes it to the manual resolution which can then be addressed and resolved immediately since everything else is handled by AIOps and the IT teams have the bandwidth to resolve such intricate issues.
— Tata Consultancy Services Europe (@TCS_Europe) September 15, 2017
PostNord, a Nordic logistics, and communications enterprise had been facing issues with recurring application errors, and in turn, was struggling to deliver their parcels in time with their delivery trucks being stranded.
Ignio helped PostNord by creating a blueprint and enabling them to resolve their application errors within minutes.
The triaging feature enabled them to resolve intricate issues with ease, helping PostNord achieve zero business outages and unmatched customer service.
The impact of AIOps on Enterprise IT doesn’t come as a surprise and the aforementioned use cases are proof of its capabilities.
Moreover, as new innovations occur in the world of AI/ML, AIOps can move further and include more capabilities.
The market is open with Enterprises beginning to take note of AIOps and their capabilities, and with businesses being more dependent on IT today than ever before, it has become imperative for enterprises to employ AIOps in their IT ecosystem.