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What AIOps Do You Need Today?

Nastel Technologies®
May 15, 2021

Gartner coined the term AIOps a few years ago, essentially defining it as implementing artificial intelligence (AI) and machine learning (ML) into IT operations. Roughly five years later, AIOps has become an important buzzword in the tech industry, driving many business decisions. That said, AIOps has also become a very broad term, and today’s vendors use it to describe highly distinct kinds of offerings.

An effective way to think about AIOps solutions is to group them into three essential categories: data generators related to generating operational data, data analyzers that turn the data into insight and action, and collaboration platforms that facilitate teamwork on top of those insights and actions.

Here, we will dig deeper into each of those three categories to give you an idea of the AIOps solutions that today’s modern businesses need.

1. The Data Generators

In AIOps, data generators are normally log-management tools, infrastructure monitoring tools and application monitoring tools. Most vendors in this category position themselves as observability platforms. What they do well is instrument your infrastructure and your applications, collect the resulting operational data, and centralize it in one place. They also make it easy to query and visualize the collected data.

The AI component in these tools automatically identifies anomalies in the data. For example, if a metric goes up or down in a way that’s different than usual, it triggers an event or warning that says there’s an anomaly. Similarly, if a certain type of logline is being generated at a nonstandard frequency, they will signal an anomaly.

2. The Data Analyzers

The second category is tools that convert data into insight and action. When you have all these data generators, you end up with very high volumes of heterogeneous data. Unless you can turn it into something actionable, the data is essentially noise for most purposes.

Data analyzers consume data from all of your different data generators and aggregate the data, normalize it, correlate it and produce various kinds of insights on top of it. They then enable you to intelligently trigger actions and automation based on the produced insights. So this layer uses machine learning to separate signal from noise and algorithmically answer questions around root cause and impact.

3. Collaboration Platforms

The third category of AIOps is all about enabling the human component of IT Operations and DevOps. This includes applications such as chat tools, on-call rotation tools or ticketing tools. They’re effective in facilitating collaboration and interaction between teams, as well as making sure that work adheres to the correct processes.

Machine learning is employed by these applications to analyze human-entered data and turn it into structured information. As a very basic example, if somebody enters free text into a description field, the technology will parse it using natural language processing and feed the extracted elements into predefined fields.

A Framework for AIOps Strategy

All of this begs the question, which of these AIOps categories do organizations need? If you’re looking at the entire incident management life cycle from the moment the data is generated to being acted upon, you really need all three. But how do they know where to start?

In my opinion, many businesses have a “tail wagging the dog” situation. Instead of starting with the problem statement and thinking about how to solve it, they start with the solution. They already have a vendor in place who says, “Hey, we also have these AIOps capabilities. Why don’t you use them?” The organization then ends up driving their entire AIOps strategy based on the particular vendor’s capabilities, as opposed to focusing on the actual needs of the business.

What I encourage people I talk to in the industry to do is always start with this three-category framework. Try to understand where you’re strong, where you’re weak and where you have the most urgent gaps. Once you have that analysis, you can understand what category of AIOps will move the needle the most for your business. Only after you have identified the area you want to improve, you can then look into specific vendors and tools and see which is best suited to solve the problem for you.

For example, an organization might have a mature log management implementation and good coverage around infrastructure monitoring. However, the same organization might have significant gaps where visibility on application performance is concerned. What is the best way to overcome this? In this case, the team will want to research and ultimately acquire another data generator, an application performance monitoring tool that is the best for providing visibility on application health. They should leverage the AIOps capabilities of that tool to quickly identify anomalies in application performance.

By contrast, another organization might be content with the visibility they have across their applications and infrastructure. Their challenge could be around data overload and how to act quickly on operational signals. This organization would need to invest in a “data analyzer,” such as an event correlation tool. A tool of this type can consume the data generated by their monitoring tools and employ machine learning to clean it up and automate incident response.

In the end, the segment of AIOps that an organization needs varies based on the systems it already has in place and the gaps that remain. But the first step is to recognize that there are three distinct categories in AIOps and that you ultimately need to be strong in all three for the best chance at long-term success.

This article originally appeared on, to read the full article and see the images, click here.

Nastel Technologies helps companies achieve flawless delivery of digital services powered by middleware. Nastel delivers Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate. To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s Navigator X fuses:

  • Advanced predictive anomaly detection, Bayesian Classification, and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards

Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering tools for Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT, IBM Cloud Pak for Integration and many more.


The Nastel i2M Platform provides:


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