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AIOps: The Future of IT Ops

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AIOps: The Future of IT Ops

By 2019, 1 in 4 global enterprises will have strategically implemented AIOps platforms supporting major IT ops functions. It’s time to start using IT ops that strategically leverage AIOps platforms.

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As your business continues to grow, IT infrastructure will experience a massive change in scale. If your company is on a growth trajectory, it’s time to start thinking about how intelligent automation with AIOps can play a crucial role in your IT operations. If you are new to AIOps, this article is a perfect place to start. Know what AIOps is, why you should use it, and how it will help your business.

What Is AIOps?

Coined by Gartner, AIOps or Algorithmic IT Operations, is a solution that uses smart algorithms (powered by AI and ML) in which machines solve known IT issues and intelligently automate repetitive and mundane jobs. It refers to a multi-layered technology platform that automates and enhances IT operations.

AIOps uses analytics and machine learning in order to analyze big data from various IT operation tools and devices. As a result, it is able to automatically spot and, more importantly, react to issues in real time.

Think of AIOps as a continuous integration and continuous deployment for core IT functions. It is important to understand that AIOps has two main components: big data and machine learning.

What is AIOps

AIOps requires a move away from siloed IT data. It uses a comprehensive analytics and machine learning strategy against the combined data. The resulting outcomes are intelligent insights that use automation to provide continuous improvements and fixes. It’s as simple as that!

Why AIOps? 

AIOps is not just an option anymore but a necessity for businesses with dynamic and complex IT environments. With the rise of cloud, distributed architectures, containers, and microservices, a rise in data overload is visible. With growing amounts of DevOps processes, alerts, repeated mundane jobs, etc. are bound to induce fatigue in your work. It’s only a matter of time until you’ll need AIOps. Here are some reasons why.

Digital Transformation

Through digital transformation, you can add more business value by saving tons of time and effort, and spend more time in innovation. AIOps adoption can help your organization to get end-to-end visibility into infrastructure and applications. Get better MTTD and MTTR as a bonus.

Smart DevOps and CloudOps Automation

As a DevOps engineer, for every CI/CD that you build today, you will eventually need to deploy an automated trigger as a part of your CI/CD pipeline. With smart automation of codes, you can monitor the deployment of health metrics and invoke issue detection rollbacks quickly.

Faster Deployment

Almost half of the organizations dealing with IT monitoring issues, spend on an average 1 hour repairing performance issues per incident. With AIOps, you can deploy automated actions (response mechanisms) for known events with embedded business logic. AIOps makes life easier for you. Say adios to midnight wake up calls!

Reduced MTTD and Faster MTTR

AIOps reduces the Mean Time to Detect (MTTD) by 60%. It does so by improving business agility through identifying intelligent management layers. With AIOps adoption, you can also reduce MTTR (Mean Time to Repair) by more than 50%. So say goodbye to excessive stress and fatigue. With faster MTTR, you can eliminate alerts, repetitive events, and respond quickly to production incidents.

Greater Visibility

Get greater visibility into your enterprise, information, network, and operations infrastructure. AIOps makes sure that you have the optimal visibility that you need for your business.

Real-Time Analysis

AIOps platforms analyze vast volumes of IT telemetry from disparate sources, by applying various types of algorithms to the data in real time. Get used to getting real-time analysis and diagnosis of issues and actionable insights.

Alerts and Notifications

AIOps reduces alert noise and by filtering and prioritizing important notifications. You can reduce operational noise across your production stack with AIOps.

Causal Analysis and Apply Analytics

With AIOps, you can conduct causal analysis and apply analytics to a broad and wide set of data. This will help you to easily identify and compare probable root causes of problematic issues.

Data-Driven Recommendations

AIOps provides you with data-driven recommendations. These recommendations are based on both real-time as well as historical data to help you to carry out an informed decision-making process.

Adds Value

Some of the key areas that AIOps adds value to include complete alert management, automation, machine learning, correlation, microservices monitoring, data management, advanced real-time analytics, and full-stack data monitoring.

Some of the other AIOps benefits include reduction of a major portion of alert noise, elimination of human errors, better-centralized governance, significant cost savings, wide data collection, modern user experience, and rapidly increased time to value.

AIOps Is Not ITOA!

If you think you don’t need AIOps because you have ITOA, think again. AIOps and ITOA are not the same. The major differentiator is in the way that the algorithms are applied to the data sources.

AIOps vs OTA

DevOps vs. AIOps

Many consider AIOps to be an evolution of DevOps. Let’s take a closer look at the comparison between DevOps and AIOps:

AIOps vs DevOps

The Future

Gartner says that by 2019, a quarter of global enterprises will have strategically implemented an AIOps platform supporting two or more major IT operation functions. It’s time to start using IT operations that strategically leverage AIOps platforms. Afterall, the end goal of AIOps solutions is to make life better for you engineers out there. In the journey towards digital transformation, you can be a pioneer by using advanced methods to scale up DevOps. It’s time to think about what’s best for your business. Will AIOps benefit you? Are you ready to plunge into AIOps? These are the questions you need to ask yourself right now.

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aiops ,ai ,big data analytics ,machine learning ,algorithms ,itops

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