By embracing AI in business, organizations are being able to optimize time and resources, enabling them to stay ahead of competitors in the marketplace. Increasingly, organizations use AI to prevent unplanned downtime of IT services and, in exceptional cases, to identify and resolve them with maximum effectiveness.
The average cost of IT downtime is $5,600 per minute according to Gartner. As a result, companies are looking for ways to avoid these interruptions.
Process automation has always been present within IT operations. From early implementations of mainframes to cloud computing and Digital Transformation, technology has always sought to reduce errors through process improvement.
AIOps or Artificial Intelligence for IT Operations is the next step for IT operations to automate, streamline and add business value to service deliverables and increase IT efficiency. It is a term for using cloud solution monitoring tools and complex infrastructure management to automate data analysis and routine DevOps operations.
Traditional methods of managing IT operations are frustrating, not being able to deal with digital transformation in the right way. To reverse this situation, Big Data and Machine Learning are two main components of AIOPS, which are combined to generate insights for continuous improvement and environmental troubleshooting.
The primary goal of AIOps is to leverage artificial intelligence to make IT operations better and faster. The platform leverages the advances brought about by digital transformation to enhance the IT environment, empower technologies and automate routine activities. This enables IT staff to gain visibility into the environment, ensure effective execution, and focus on strategies to bring business results.
The ultimate goal of AIOps solutions is to make life better in the midst of digital transformation. AIOps is not an option anymore, it is a prerequisite for organizations with complex and dynamic environments. Increase in data overload is already visible with the rise of the containers, cloud, distributed architectures and even microservices. Processing all the data generated is not humanly possible. However, this is exactly the type of task wherein artificial intelligence algorithms stands out.
Some of the significant benefits of AIOps include:
- More Value from Big Data
- Improves collaboration to integrate Data
- Monitor Stakeholders and provides more practical insights to it
- Smarter Automation
- Cost Reduction
- Improved End-User Experience
- Increased IT Effectiveness
Applying this practice results in tremendously better customer experience, more efficient use of infrastructure, and better collaboration within the team.
The use of Artificial Intelligence in operations is accelerated by factors such as:
With new technologies, IT environments are no longer just one physical structure. Modern IT environments include cloud, third party services, mobile devices and more. Traditional manual management does not support the complexity of this new environment. And with advances in technology, the situation will only get worse.
With the arrival of IoT devices, APIs, mobile applications, the data numbers and information generated increased exponentially, making monitoring and analysis difficult without the aid of artificial intelligence.
New technologies have also affected user behavior. The consumer of newer technologies are quite impatient; they don’t have much time to wait for a problem to be resolved. He wants an immediate solution and IT staff need to gain speed to meet his expectations.
The benefits provided by edge computing, such as advanced processing and analytics, aligned with the ease of cloud infrastructure and third-party services, have urged companies to build their own IT solutions and applications.
Developers now have more application-level monitoring influence and responsibility. However, it also generated more responsibility for IT operations such as overall ecosystem integrity and application interaction. The more complex digital businesses become, the more responsibilities arise for IT.
William Hill, one of the world’s largest betting & gaming company is also making use of AIOps to handle their extensive data.
They aggregate all their data in regards to different functional department at one place, which helps in maintaining consistent data. Therefore, all the complexity of data which is observed in several units of an organization is handled efficiently. And, so every department will be in a better position to understand the throughput of their work and what value it holds across the entire company.
Processing of this accumulated data via several AI & machine learning puts forth a simplified version. Thereafter, you can set up some alerts, cluster similar data and analyze several other aspects as well very easily.
AIOps is helping breakdown DevOps silos and is impacting the future of IT Ops at KPN. Since 2017 they’ve been using machine learning and AI in the customer service process which helped them analyze data more accurately and faster.
Like with any large organization that deals with a multitude of data, employees at U.S. Bank too have to deal with analyzing a lot of data. Availability of AIOps service will help them handle & automate this vast amount of data, which can improve the overall efficiency by enhancing the experience for customer & saving up a lot of time invested in redundant jobs.
As we can see, AIOps solutions can be of great benefit. Innovative organizations are already applying their efforts in combining various machine learning models, artificial intelligence algorithm, and DevOps systems to deliver cutting-edge monitoring solutions. Following are some of the significant AIOps vendors:
CA Technologies is the leading player in the AIOps industry. Powered by new automation capabilities, innovative AI and machine learning, it makes self-healing applications a reality and enables IT teams to eliminate and automate key tasks.
Moogsoft AIOps is the cutting-edge AI platform that identifies the root cause of problems and enables cross-team collaboration for quicker resolution. It improves the signal to noise ratio by decreasing and then correlating alerts together.
While Botmetric uses AIOps to deliver Security Compliance Management, Cloud Cost Management, and CloudOps Automation. BMC AIOps provide operational analytics for incident management. It focuses on business-critical issues to improve performance and reduces event noise.
IT operations suffer from too much noise because there are so many events and too many processes and it is humanly impossible to keep up with everything that goes on. The solution is to achieve more efficient management at reduced costs.
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