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Is AIOps the solution for noise reduction?

Is AIOps the solution for noise reduction?

By |2019-10-17T17:44:06+00:00August 28th, 2019|Blog|

ITOps teams are challenged today like never before, overwhelmed by IT noise and constantly fighting fires. And for the business this often means higher operating costs, performance and availability issues, and risks to enterprise digital initiatives. 

So what’s an IT Ops guy/gal gotta do? Well, lately there’s been talk about “a new sheriff in town” that claims to be able to solve this challenge, and its name is AIOps. Before we talk more about it – let’s discuss the problem a bit more.

So Much Noise

With the proliferation of IT monitoring tools, the volume of daily alerts a NOC has to deal with is often in the tens of thousands. But the problem is more than just seeing the forest for the trees. 

Overwhelming IT noise means that IT Ops and NOC teams are flooded with false positives (aka ‘symptom’ alerts) on an everyday basis, making the identification of  root cause nearly impossible. And, in order to deal with this overwhelming situation, organizations often filter alerts so that only those deemed high-severity (commonly known as P0 or P1 issues) reach the responding team. This creates a blind spot in the organization’s operational visibility, since low-severity alerts are often precursors to the high-severity ones, leading to what everyone hates –  reactive troubleshooting.

No Way Out? 

So what are enterprises doing, to deal with this challenge? Unfortunately, the means at their disposal are often either largely ineffective or create other problems:

  • Increasing headcount – this is a costly solution, and doesn’t scale in the long run. 
  • Relying on customers to report outages – this makes the enterprise more reactive, and creates unhappy users.
  • Turning off “noisy” tools – this ends up diminishing visibility and forces enterprises to become even more reactive! 

AIOps to the rescue

Thanks to advances in computing power and rapid decline in costs, AI and ML (Machine Learning) have started to make a tangible difference across many different industries, including IT Ops. 

Today, AI and ML can:

  • Cost-effectively, scalably and quickly process large datasets
  • Uncover insights that are hard for humans to uncover on their own
  • Learn from the data they process and become more effective and efficient over time.

These capabilities make AI and ML particularly adept at dealing with the overwhelming IT noise on a 24×7 basis. In fact, over the last two years, a number of vendors have started to offer IT Ops tools powered by AI/ML – or as they are often called – AIOps.

But not all AIOps tools are created equal

There are many ways to implement AI/ML-based IT Ops tools, some of which may result in certain negative outcomes. 

To mitigate the risk of unsuccessful AIOps projects, here are the questions you should be asking yourself, and your vendors, when evaluating AIOps tools:

  1. Time to value: How long does it take to train the tool and when do you start seeing value?
  2. Trust: Can your team trust the results generated by this tool?
  3. Adoption and usage: What does the learning curve look like, and how easy is it for your IT Ops and NOC teams to start using this tool?

Choosing the right AIOps tools is critical for answering this blog’s titular question: Is AIOps the solution for noise reduction? 

The answer is yes…but! There are choices that you need to make to ensure that you see rapid time to value on your AIOps investments, gain the full trust of your team and come with a short learning curve that maximizes adoption and usage. 

What are these choices? Read about them here in our latest executive brief. 

 

About the Author:

Mohan Kompella
Mohan is the VP of Product Marketing at BigPanda. He's spent 15 years in IT Operations and ~ 20 yrs in Enterprise IT. During this time, he's worked closely with F1000 and Global 5000 enterprises in a variety of engineering, consulting, architecture, pre-sales engineering, product marketing and strategy roles. Most recently, Mohan led Product Marketing for Lightbend.