AIOps in the Real World
Artificial intelligence for IT operations (AIOps) is challenging us to rethink how we invest in and develop IT organizations, and that’s a good thing.
In This Article
Lately there has been significant chatter and buzz around yet another IT acronym — this one being AIOps. Sounds interesting, but what exactly does it mean and how does it apply to you? Let's break it down (in the real world), but first we need to clarify a few things.
An introduction to AIOps
AI (artificial intelligence) and Ops (operations) are the genesis of this idea. Combining the two seems counter intuitive in some ways, yet when you actually understand what the goal is, it starts to make more sense.
In a traditional data center or IT environment, the Network Operations Center (NOC) is the command center for running operations top to bottom. A well-run and traditional NOC has lots of people, monitors, computer systems, monitoring platforms/tools and costs.
Those platforms, tools and systems are complex and create a ton of actionable insight to outages and threats, as well as being the first line of defense for an organization's data centric well-being. They also must be fine-tuned to minimize the “noise” they make and create useful data.
The people are the brains to the NOC. They take all of the noise that the tools make, compile it together, analyze what’s going on ("Is that router actually down or is the circuit feeding it down?"), identify the root cause of an issue and find the repair to bring things online or thwart a potential threat. It's complex and frequently requires a team of people to gather in a war room to dissect the data and manage resulting issues.
For anyone that has ever been through this exercise, it can be painful and slow. The goal of all of this is to minimize disruption and provide better mean time to identification (MTTI) and mean time to resolution (MTTR). The ultimate goal is to minimize IT disruption to an organization and allow them to focus on the core business. Drop times to identify an issue and resolve that issue, and you have a better run organization.
Where AI comes in
Artificial intelligence is a wondrous and phenomenal technology that takes advantage of what machines do better than anything: crunch data. They are faster than any human mind at ingesting information, cross correlating that information and providing basic process driven tasks such as rebooting a server, clearing cache, etc.
Additionally, when it comes to these kinds of tasks, they tend to minimize mistakes when it comes to raw numbers driven data. All the “noise” that a traditional NOC ingests can be broken down in an AI engine and correlated to understand what a likely root cause is using the AI’s ability to crunch data. People are not eliminated by this process; they are elevated to make more informed decisions with greater accuracy all in the name of MTTI and MTTR.
The output in a proper AIOps environment is like liquid gold, minimizing disruption and securing a company’s data which leads to happier customers, better end user outcomes and savings (which can easily amount to tens or hundreds of millions of dollars for large organizations). Customer experience, brand loyalty and investor relations are all deeply affected when an AIOps environment is built correctly, and IT operations having great, usable information leads to great outcomes.
Putting it all together
Pulling AIOps together, as you can imagine, is the most difficult part of the whole puzzle. Traditional IT silos within an organization (network, storage, compute, application development, etc.) need to be operationally aligned internally to ensure success. They need to be incentivized to work together instead of feeling like the have to protect themselves in order to claim innocence when an issue arises.
This is all about a team effort for the good of an organization where it matters most — their core business. Once that alignment has been done and the people and process are a true team, the next technology can be chosen.
Often times organizations have too many systems, tools and platforms that don’t integrate well together. Too many tools is in some ways worse than not enough. Making useful noise, not just more noise, is the goal here. Selecting the right platforms and systems and correlating the information in a way that the AI platform can ingest and crunch that data in a way that the organization needs to support their business leads to beneficial outcomes.
When teams are enabled to be proactive instead of reactive, customers experience better outcomes, uptime increases and overall, a more strategic IT organization emerges. A harmonized team working with the right alignment, tools and desired outcomes is the beauty of AIOps in the real world.
Want to learn more? Schedule a demo with our AIOps experts to learn more.