AIOps

What The Heck Is AIOps?

Nastel® Technologies
Comments: 0

A quick primer on the new technology rewiring IT—and why you need to get onboard.

AIOps – Consider the cloud.

The term has been with us since the 1990s, but perhaps first entered the public consciousness back in 2010, when Microsoft launched its “We’re All In” ad campaign to raise awareness of its cloud computing services.

Back then, there was a distinct sense that the cloud was overhyped. That it was yet another marketing buzzword. Now, the cloud is a given.

There’s a similar discussion about AIOps today. Once again, some people are skeptical. But I predict that AIOps is destined to follow the trajectory of the cloud. Organizations will quickly learn that they can’t remain competitive without it.

Just like the cloud, AIOps is going to rewire IT. And now it the time to get onboard.

AIOps puts IT in the lead

Just to make sure were on the same page, let’s define AIOps.

AIOps stands for “artificial intelligence for IT operations,” and it exists to make IT operations efficient and fast by taking advantage of machine learning and big data. That’s the opposite of how many IT operations departments work today, struggling with manual processes and heavily siloed tools that don’t share data.

Why is AIOps so important? Because, powered by the cloud, businesses are digitizing at unprecedented speed. The pace and scale of change is staggering. And IT operations just can’t keep up. There’s too much complexity and too much to manage. With SaaS apps and cloud services, business units are bypassing IT altogether—and this has been going on for years.

AIOps flips the script, giving IT the speed it needs not only to keep up, but to lead from the front.

With AIOps, your IT operations team doesn’t have to dig through thousands of events from siloed monitoring tools, spending hours correlating disconnected data to identify impacted services and the root cause of failures. Instead, they immediately see a small number of actionable alerts and impacted services on a single console.

Teams can quickly access appropriate historical and real-time context, providing valuable shortcuts to issue diagnosis and resolution. They can also see how similar issues were remediated in the past and can even remediate issues automatically.

Taken together, these AIOps capabilities significantly lower MTTR, reduce the number of major incidents, and increase operational efficiency.

AIOps is ready for prime time

While the transformative promise of AI has yet to materialize in many parts of the business, AIOps is different.

AIOps doesn’t need a staff of data scientists or AI translators. It doesn’t need a major reorganization like many other large-scale AI initiatives. In fact, AIOps is the perfect pilot for other organizational AI initiatives, building sponsorship beyond IT as it transforms IT operations—which in turn transforms the business.

Here at ServiceNow, we’ve seen great results with AIOps. For example, we used to have a huge number of VPN outages. Given our remote workforce around the world, this caused major employee frustration. Once we implemented AIOps, we reduced our VPN outages by 900 hours a year and reclaimed 1,000 hours in employee productivity—reducing lost time from 1,400 hours to just 400 hours.

Results like this explain why the global market for AIOps tools is expected to grow from $2.5 billion in 2018 to more than $11 billion in 2023, according to a study by ReportBuyer.

This article originally appeared on forbes.com To read the full article and see the images, click here.

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics 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, a global leader in integration infrastructure (i2) and transaction management for mission-critical applications, helps companies achieve flawless delivery of digital services.

 

Nastel delivers Integration Infrastructure Management (i2M), 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.
The Nastel Platform delivers:

  • Integration Infrastructure Management (i2M)
  • Predictive and Proactive anomaly detection that virtually eliminates war room scenarios and improves root cause analysis
  • Self-service for DevOps and CI: CD teams to achieve their speed to market goals
  • Advanced reporting and alerting for business, IT, compliance, and security purposes
  • Decision Support (DSS) for business and IT
  • Visualization of end-to-end user experiences through the entire application stack
  • Innovative Machine Learning AI to compare real-time to the historical record and discover and remediate events before they are critical
  • Large scale, high-performance complex event processing that delivers tracing, tracking, and stitching of all forms of machine data
  • And much more

Comments

  • Dave
    December 21, 2021
    Fascinating piece of research, but what is unclear from this summary of this study is if the AI is actually more or less reliable than human analysis. 5 radiologists is quite a small sample, and the range of accuracy of fake detection is so wide that the results of the AI actually fit inside that range. Are the volumes of tests enough to be statistically viable? And of course the real issue is that compromised images could take many forms, some could be complete real images replacing the image to be tested, while others could have artifacts added or removed. So is the issue AI or the ability to secure the way an image is managed from creation to analysis. That then becomes a integration infrastructure management (i2M) problem.
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