By Raghav K.
Digital transformation has brought along various benefits for enterprises and organizations. These benefits include accelerated application delivery, O&M costs, increased IT infrastructure scalability, and increased data storage and computing resource availability on the fly. The large-scale expansion of web-based services using multi-cloud or hybrid cloud environments has also created numerous challenges for IT teams. Strategists have to create an effective mechanism that ensures security and operational efficiency for IT systems.
AIOps is a mechanism that mixes the power of Artificial Intelligence (AI), machine learning techniques, and big data analytics to enhance IT operations. AIOps brings out the best in IT today and provides precise and precedented data to form specific methods and methodologies to deliver value-filled solutions.
Gartner predicts, “Large enterprise exclusive use of AIOps and digital experience monitoring tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023.”
By utilizing artificial intelligence and when I write artificial intelligence, I mean the concept in terms of AI + ML + Big Data to form an AI-driven solution. You can expect capabilities, such as:
AIOps automates your system by replacing the toolset for different manual IT operations with an automated IT operations platform. You are well equipped to handle any performance issues and system failures more quickly.
AIOps implements artificial intelligence with machine learning and big data analytics to implement practices with automation that is based on real-world data. AIOps can present aggregate data that may contain:
Based on the collected data, AIOps can help you automate several processes to enable a more efficient system. You can leverage the machine learning benefits to automate with precision.
With a machine learning model in place, you can use AIOps to automate responses on event triggers. You can supply an automated O&M response or send a critical analysis and recommended response to the concerned team to resolve any outage or slowdown issues. Over time, this model will automatically learn and give the best-suited responses or steps to ensure the maximum uptime for a service. This level of automation helps the organization identify the root causes of issues that reoccur and fix them once and for all.
Gartner states, “By 2024, 60% of the data used for the development of AI and analytics solutions will be synthetically-generated.”
AIOps can become extremely efficient at extracting valuable data in terms of event alerts from the huge amounts of data the system generates. Analytics metrics, such as rule application and pattern matching, can be used to filter the IT operations data to separate the valuable data from the whole stack.
This learning ability can help the system cope with changes to the environment or different resource scheduling levels. You can independently train different models to enable application depending on different scenarios.
Organizations are going through a rigorous transition from traditional architecture to cloud-native. The cloud setup that enterprises are pursuing includes hybrid cloud or multi-cloud. These architectures provide a well-balanced mix of on-premise, private, and public cloud features to provide a flexible and scalable environment. Business applications are designed with the cloud-native architecture to support a large volume of user requests.
These scenarios give birth to extensive data traversing over a short time, and this requirement is continuously growing.
Gartner’s research states, “By 2025, 70% of organizations not adopting a service/product orientation will be unable to support their business. I&O engineers must engage with consumers and software developers; integrate people, processes and technology; and deliver services and products to all to support a solid infrastructure on which applications reside.”
The traditional IT operations mechanism does not stand a chance with severe data-based workload demands to extract significant value out of a solution. Newer methods to revolutionize the operations pipeline are required. AIOps is the answer. The detailed insight provided with AIOps helps organizations make strategic reforms and informed decisions.
DevOps accelerates the development to release cycle by enabling the development teams to leverage infrastructure elastically and efficiently. However, infrastructure management is still complex. Here comes AIOps. It provides the strategy and automation required within IT operations to support DevOps without additional management hassle.
Digital transformation using AIOps is a vital link that can tackle complexities by design. A well-thought-out AIOps solution can give an organization the flexibility to transform based on strategic business goals using concrete data provided by machine learning with AI without burdening the IT operations with unnecessary service practices.
Mean Time to Resolution (MTTR) can be cut down significantly using AIOps. It considers data from multiple sources and extracts valuable information by comparing it with different patterns and historical data to provide information to solve issues faster.
The cloud-native architecture has enabled the true potential extraction from applications. AIOps is a solution that works to enable highly efficient IT operations by including security, analytics, and artificial intelligence with machine learning. IT organizations can aggregate large volumes of data and make decisions based on real-time data. This will enable them to deliver crucial security and operational data to optimize the customer experience further and bring their business to new heights.
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