The Most Important Elements of AIOps
The Most Important Elements of AIOps
Learn about the most important elements of AIOps such as time management, use cases, and more.
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With increasing efficiency and sophistication, the IT environment is becoming extremely complex too. The recent shift to microservices and containers has further added to the already large number of components that go into a single application, which means the challenge is equally big when it comes to orchestrating all of them.
The ability of IT Ops teams to handle such complexities is fairly limited and hiring more resources to configure, deploy, and manage them is not very cost-effective.
This is where artificial intelligence for IT Operations (AIOps) comes into play. None come close to AIOps when it comes to leveraging Big Data, data analytics, and machine learning to offer a high level of customization along with invaluable insights necessary to cater to modern infrastructure.
Here’s what you should know if you are contemplating moving towards AIOps.
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As automated tools entered the scene, IT Ops teams realized that despite improved efficiency, these tools were incapable of making automated decisions based on data, and therefore required considerable manual effort even then.
AIOps presented a more refined way of integrating data analytics into IT Ops, supporting more scalable workflows aligned with organizational goals.
AIOps Platform Technology Components
Use Cases for AIOps
Anomaly detection — This is definitely the most basic one since you can trigger a remedial action only after detecting anomalies within data.
Causal analysis — Root cause analysis is required for issues to be resolved quickly and effectively. AIOps plays a pivotal role here.
Prediction — Automated predictions about the future can be made using AIOps powered tools. For instance, you can find out how and when user traffic can possibly change and then react to address it.
Alarm management — Intelligent remediation, closed-loop remediation, is kicked in without necessitating human intervention.
Drawing Parallels Between AIOps and DevOps
DevOps had brought about a cultural shift in organizations, and in that sense, AIOps is pretty similar in effect and impact. AIOps is helping enterprises discover holistic insights from connected and disparate data to bring about decision-automation to make them better and more agile.
It is important for enterprises to break free from traditional silos as data should be generated and used keeping the ‘observability’ aspect in mind for the entire company, not just one department.
Thanks to AIOps, typical IT Ops admins are now transitioning into the role of Site Reliability Engineers helping them utilize information more efficiently and tackle issues in a more effective manner.
While both AIOps and DevOps share the same goal of making organizations better and more productive, AIOps can make DevOps practices more effective by reducing the noise that gets in the way of productivity. For example, AIOps streamlines the alerts and notifications from various platforms so that it becomes easier for DevOps engineers to address them. It would be safe to assume that AIOps complements the goals of DevOps engineers and enterprises effortlessly.
AIOps and Time Management
No matter what the team size, organizations will always struggle with the most common issue of having too much to do in too little time.
Luckily, there’s a lot AIOps can do for you in this regard. From helping you create a machine learning model to processing data to make it flexible enough to accommodate new information, AIOps can be just the value add-on you need.
Those who have been using AIOps would know the role of a well-trained machine learning algorithm in attaining and maintaining the high quality of data. Also, ‘real-time’ is the buzz word here since most use cases require real-time data processing.
So for instance, if the use case in question is detecting anomalies, then it is important to get information quickly so that you can prevent a security breach. The same applies for all use cases where the rationale is to get to a problem and resolve it in the fastest possible manner.
High-quality data, therefore, remains extremely important and AIOps makes it possible despite the complexities. Enterprises understand the importance of data analysis in principle, but find it difficult to trust and rely on it. As indicated by KPMG’s survey, 67% of CEOs agreed to have ignored the insights offered by computer-driven models or data analysis largely because they were not in line with their own thinking or experience.
The Growing Popularity of AIOps
Having data is one thing, and being able to be able to use it effectively is another. While machine learning holds a lot of promise, organizations need to employ resilient applications and stronger automation platforms.
MarketsandMarkets predicts a 34% combined annual growth rate for AIOps platforms giving a sneak peek into its rising demand. The fact that AIOps helps businesses be more flexible and responsive without putting a burden on resources is fast making it a must-have in this highly digitized era.
Getting Started With AIOps
As enterprises transition towards a state of enlightenment with respect to the incredible benefits of AIOps, the question that needs to be addressed is how to embrace it in a way that it aligns with your business needs. Here are a few things that should help you:
Understand the basics of artificial intelligence and machine learning so that you are better equipped to adopt it.
Identify the most time-consuming tasks that your people undertake and how AIOps intervention would help to alleviate this load. Particularly look for repetitive tasks that could be effectively dealt with automation.
Avoid taking on too many things at once. Start small and begin with high-priority tasks. Once you get good feedback, assess how this technology can be best leveraged to address other areas and tasks.
Employ AIOps for all kinds of data. No doubt this may take longer than you thought but you need to look at the bigger picture. Also, look at the metrics you want to evaluate and the parameters you want to define your success on. The rationale is to ensure that your efforts are aligned perfectly with your organizational objectives.
From the adoption and maturity perspective
IT leaders are keen on automating arduous tasks within incidents while bringing down costs of alerts which can be significant. Service disruptions and downtime costs have been major factors of concern for most organizations.
IT organizations can vary in their objectives when it comes to AIOps adoption but what they are looking for in general is overall visibility into their systems to get a better handle on operational efficiency and the production environment.
Let us look at a five-stage maturity model that can help organizations gauge where they stand in terms of their monitoring and automation journey.
AIOps is for those who have long-term goals and perceive it as the change that is needed to drive modern applications using microservices. It will ensure a fluid flow of information and rather than merely improving processes may even change them to match the current perspectives and architectures of organizations.
They need to rethink how they are going to perceive the full stack rather than seeing it only from an application perspective or the perspective of a cloud team or architecture team. This is particularly important for applications that are built using microservices. Enterprises need to understand what the infrastructure does at the app layer by retooling the capabilities for operations thereby providing necessary insights to app developers with the right flow of data.
All you need is a willingness to look at it without prejudice and think of the myriad ways it can help augment your business goals.
Although AIOps is witnessing early adoption by enterprises, there are enterprises that are still unsure about the hype surrounding it and are wondering if it’s indeed wise to go the AIOps way. AIOps, however, is perhaps the only way to unlock your full potential.
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