By Shantanu Kaushik
IT operations drive the world today. Innovative business models and services are developed every day to provide a new and better digital experience for end users. Improved software delivery cycles and the inclusion of big data and artificial intelligence are sparking more intelligence into enterprises worldwide.
Innovation and evolution are fueled by enterprise infrastructures that provide an elastic and flexible approach towards handling your applications and services to make room for transformation. The architecture becomes more complex as different methods of handling IT operations evolve. Achieving success has become more challenging because of all the complexities.
With a giant tech space and a complex IT infrastructure, a solution has to be tailored for your needs to leverage any business intelligence from it. Moving from traditional practices and implementing a much-required digital reassessment is highly essential. There is no space for drill events and false alarms; a practice or pipeline must be efficient and self-sustaining, if not fully, at least to an extent.
IT operations are evolving rapidly. There are no unconditional practices or rebound pipelines tuned to utilize the same old predictive practices that included the traditional monitoring tools. These tools were effective on that scale, but while moving away from traditional practices, we have witnessed an explosion in scale and size of the IT infrastructure, with multiple services handling thousands, if not millions, of requests every day.
If the traditional methods of monitoring your IT Ops cannot make out if any issue at hand is a one-time event, it could lead to a full-blown disaster. Today, the requirement is an intelligent system that analyses data, metrics, and patterns to provide a suggestive yet spot-on prediction with possible and quick resolution to the issue at hand.
When talking about smart and intelligent systems, I need to mention the AI and Data Analytics used by Alibaba Cloud to process and entail the complexities of the solution. Any system should be smart enough to process data and extract patterns from it to diagnose problems and prevent similar issues in the future.
The first challenge is to match the speed and scale of digital transformation and the IT Ops evolution cycle. Adapting to the changing methodology associated with the industry-wide disruption is the key practice an enterprise must follow. We have previously talked about practices, such as AIOps, what they bring to the table, and why enterprises should adopt those.
IT operations management has to take the leap and step up its game to become smarter by driving more business intelligence into the mix. Data analytics and machine learning can help predict how your data, applications, and infrastructure will behave and the evolution patterns based on various factors.
Utilizing these predictions can enable you to diagnose issues, strategize, and evolve more quickly, efficiently, and precisely.
AIOps applies to a wide array of solutions. These solutions enable much simpler, high-performing, flexible, and highly efficient IT operations management (ITOM). AIOps is a mechanism that mixes the power of artificial intelligence (AI), machine learning, and big data analytics to enhance ITOM.
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% by 2023.”
We cannot stick to the statement that AIOps is the only answer. Practices, such as NoOps theorize scenarios where complete automation of operations workloads can enhance the application delivery cycle and cut down on O&M loads considerably. However, the practical application of any practice or methodology is highly affected by the core practices of a certain enterprise. An enterprise must have a structure with personnel that can adapt to cultural changes. Syncing between different operations is a required threshold for an organization to flourish.
Although IT operations management should be independent of these thresholds, a certain degree of association is always warranted. AIOps takes a dig at these thresholds and uniformly disintegrates dependencies related to the old traditional practices of operations management. Application performance monitoring can be transformed using AI and machine learning to evolve into a highly intelligent, pattern-centric system that enables application performance throttling to maximize efficiency.
Predictive patterns based on real-world service behavior assessment can help us draw and strategize a better practice. It is all about understanding which issues boil chaos into your practice and which ones are unlikely to repeat. This is cause prediction that enables us to nip the problem in its track and not let it become a full-blown issue. Let’s take a look at the transformational patterns associated with IT operations management and its key aspects. Here, we will discuss:
Setting up a robust IT Operations practice requires the inclusion of an intelligent operations solution like AIOps. With the changing requirement of businesses and the world, the way we deliver applications is also changing. We can no longer stick to a single cloud practice and improvise on application delivery techniques by tweaking the system here and there. We need a cloud-native approach with a hybrid cloud or multi-cloud as the base infrastructure and an application performance monitoring solution that can collect metrics and data with a multi-source and multi-point application delivery scenario.
Application performance monitoring can no longer be limited to monitoring service availability, registering system events, and providing metrics to upgrade the application features in the next update cycle. DevOps and Agile methodologies have already made a mark on the industry, and organizations are rapidly moving towards adopting DevOps and its culture.
The next generation of application integration solutions requires a much deeper performance monitoring and management portfolio. The solution must be capable enough to register processing events and micro-data related to key module performance monitoring. A hybrid approach to integrate an APM solution that can debug code (or detect code) anomalies should be the priority to enable and enhance the overall application experience.
Your infrastructure is a tool to support your business by providing your applications and services with a stable and robust platform. It is the applications and services that create value for your business. An end-user using your application is not concerned about how strong your infrastructure is or what architecture your solution is based on. You can enable a seamless user experience by implementing a smarter monitoring and management solution.
In the next article of this 2-part series, we will discuss how pattern recognition, behavioral assessment, intelligent log management, and event management based on predictive patterns can help you lead a seamless IT operations management practice.
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