CIOs are being pushed to find ways to streamline their operations and free up money and capital for innovation.
We’re well into the hype cycle for the next big thing in artificial intelligence — AI for IT operations (AIOps), writes Bill Talbot, Vice President of Solutions Marketing at BMC Software.
Firstly, let’s define what AIOps is. AIOps refers to multi-layered technology platforms which automate and enhance IT operations by using analytics and machine learning to analyze big data collected from various IT operations tools and devices, in order to automatically spot and react to operational issues in real time.
In essence, AIOps melds machine learning and big data to improve IT operations by automatically spotting issues and in some cases fixing them in real time, supporting a business’s need for speed, agility, increased efficiency, and improved customer experience.
Why Now? Key Trends Driving AIOps
There are several reasons driving the need for AIOps. First, traditional approaches to managing complex IT issues don’t work in dynamic, elastic, and cloud-native environments. Tracking and managing this complexity through manual, human oversight is no longer possible.
Secondly, the amount of data IT operations needs to retain is exponentially increasing. Subsequently, performance monitoring is generating exponentially larger numbers of individual events and alerts. Once again, running IT is simply becoming too complex for manual reporting and analysis.
Furthermore, even the simplest of infrastructure problems now require responses at ever-increasing speeds. As organizations digitise their business, user expectations for all industries have evolved due to the increasing ‘consumerisation’ of technology.
The reactions to IT events whether they are real or perceived need to occur immediately, especially when the end user experience is impacted. At the end of the day, nobody wants disgruntled and unproductive customers – both internal and external.
While IT teams are busy managing a wide range of infrastructure issues, line of business (LOB) functions and developers now feel more empowered than ever to build their own infrastructures and applications thanks to the ease in which the cloud and third-party services can be adopted today.
Not only is more computing power being added from outside core IT, IT teams often do not have visibility into these new systems to ensure its performance, security, or reliability. Despite all this, accountability for the overall health of the IT ecosystem and the general ‘big picture’ still sits with IT Ops.
As digital business requirements and infrastructures become more complex, it’s clear that IT teams must leverage AIOps to overcome legacy tool and human limitations – starting with everyday tasks that keep the business running.
Putting AIOps to Work: Why Practicality Will Help IT Get the Most Out of AIOps
You may have heard all kinds of predictions about the impending AIOps revolution: Unleashing a few quick-and-dirty algorithms or AIOps analyzing your worldwide IT operations and tell you to shut down two data centers to save tens of millions of dollars.
Unwarranted hype like that leads to disillusionment. Companies abandon technologies that don’t live up to overblown promises. In the case of AIOps, that would be too bad, because the reality of AIOps outstrips the hype. It can automate daily tasks, help resolve problems faster and more importantly, look ahead to detect and fix issues problems before they occur, improving customer experience and freeing your smart people to work on innovative money-making IT projects.
Let’s take a look at some real-world examples where AIOps has paid off, starting with Google. The company has a vast need for electric power, especially for cooling, at its data centers around the globe. The bill for cooling alone costs the company millions of dollars a year.
So Google put AIOps to work. Data centers generate millions of data points a day from thousands of sensors, yet that data has typically only been used for monitoring, says Jim Gao, a Google data center engineer. Therefore Google mined all that data about temperature, power usage, cooling systems and more, and used machine learning to figure out ways to cut cooling costs. The result: a 40 percent reduction in the power required to cool data centers, and an overall drop of 15 percent in data center power use. Those are real-life savings, not pie-in-the-sky promises.
The power of machine learning applied to big data can be put to work by IT Ops teams every day to drive benefits. AIOps provides insights that can significantly reduce the cost of operations.
Customer call center software company NICE inContact was spending millions on IT infrastructure to sustain its fast-growing business providing its software globally. Through employing AIOps, machine learning and advanced analytics, inContact was able to correlate IT infrastructure usage data with business growth projections to determine what IT resources to buy and when.
With these insights, it managed to eliminate IT capital purchases for five consecutive quarters and was able to plan effectively for future needs. The significant savings yielded while ensuring the company could deliver the service that over 200,000 call center agents rely on daily.
At any company, AIOps will provide insights and automate actions to manage straightforward everyday tasks that take up IT’s time and money. This can include automatically responding to an alert that a server disk is almost full and freeing up disk space by deleting log files. Or predicting a performance issue, pinpointing the problem and then automatically remediating.
Instead of spending hours determining the root cause of a performance problem and more time recovering from it, enterprises can detect and address problems quickly or even avert them altogether. When IT is already overburdened with managing complex infrastructure issues, adopting AIOps to alleviate these everyday issues becomes even more imperative.
In addition, CIOs are being pushed to find ways to streamline their operations and free up money and capital for innovation. That is why many CIOs say AIOps will be central to their plans in the coming years. A Gartner report concluded 40 percent of large enterprises will be using AIOps by 2022, up from just five percent today.
The reality of AIOps is better than the hype. By handling IT’s everyday problems with actionable insights from machine learning and automation, AIOps can avert downtime, cut costs, improve customer experience and free IT staff to work on the innovations your company desperately needs.