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AIOps: What Is It and Where Are We Now?

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AIOps: What Is It and Where Are We Now?

Take a look at what AIOps is as well as where we are now in regards to it. Also explore the benefits and components of AIOps.

· AI Zone ·
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IT systems (hardware and software) are becoming more efficient and sophisticated. At the same time, they are growing more complex. Virtualization and containerization are vitally important technologies, but their complexity creates challenges for the IT Ops department.

Hiring more staff or using automation tools to respond to this increasing complexity is not the answer—Limited ability and resources make it difficult to simplify complex IT systems.

So what is the answer?

AIOps

In recent years, Artificial Intelligence (AI) has entered IT Ops departments. Artificial Intelligence for IT Operations (AIOps) is now offered as a solution to manage ever-increasing IT complexity.

AIOps completes IT tasks by combining Machine Learning, Big Data, and automated decision-making. It enables process automation without the help of significant manual intervention, making it independent of manual resources.

The Evolution of AIOps

AI, Machine Learning, data analytics and IT Operations (Ops) have existed separately for years. What makes AIOps innovative is the bringing together of data-driven insights from Analytics and the practicality of IT Ops.

Benefits of AIOps

AIOps offers two important features that benefit the IT Ops team:

It provides IT Ops teams with access to tools that can make advanced decisions and perform automated actions by collecting and analyzing data.

It helps traditional IT Ops admins transition into site reliability engineer (SRE) roles and supports more scalable workflows that align with business needs.

Now we know what AIOps is, and we know more about its evolution and benefits. But what is required to enable AIOps?

AIOps Components

To make AIOps work, you need to implement four components.

Data collection. This is the first step in enabling AIOps.

You may need to collect data from different sources. This data has to be transformed and aggregated to usable data with enough quality to drive data analytics and Machine Learning.

Data analytics. Once data has been appropriately collected and transformed, statistical analytics is performed to draw out insights from the data.

Machine Learning. This is the process of using the insights deduced from your data analytics to enable automated decisions by means of algorithms.

Artificial Intelligence (AI). This refers to the broader category of automated decision-making, of which Machine Learning is only one component.

AIOps Use Cases

When the four components are implemented correctly, AIOps can be used in the following use cases:

  • Anomaly detection
  • Causal analysis
  • Prediction
  • Alarm management
  • Intelligent remediation

The Status Quo of AIOps

Current AIOps adoption rates are not yet available, but Gartner estimated in 2017 that “25% of global enterprises will have strategically implemented an AIOps platform supporting two or more major IT operations functions” by 2019. In addition, according to TechValidate, 97% of surveyed IT organizations agreed that AIOps-enabled solutions deliver actionable insights that will help automate and enhance overall IT Operations functions.

AIOps is already seeing early adoption by enterprises, although it will likely take some time before a majority of businesses have deployed AIOps platforms.

The Hurdles of AIOps

Greater AIOps adoption is currently difficult due to businesses’ uncertainty over whether AIOps reflects true innovation or mere hype. Businesses also find it challenging to collect high-quality data, and they lack the best practices to implement the solutions enabled by AIOps. But these hurdles can be overcome with sufficient research and planning.

Conclusion

AIOps is the solution for managing the complexity of IT systems — it combines the data-driven insights of Analytics and the practicality of IT Ops. Yet, while AIOps holds great promise and is already seeing early adoption by enterprises, it will likely take some time before a majority of businesses have deployed AIOps platforms. Change is on the way, but it won’t happen overnight.

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Topics:
aiops ,ai ,machine learning

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