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Artificial intelligence (AI) is finally coming into its own and beginning to demonstrate significant business value
By: Akhil Sahai
Jul. 31, 2016 11:45 AM
How Artificial Intelligence Is Transforming IT Operation Analytics
After many years of research, misfires and frightening Hollywood plotlines, artificial intelligence (AI) is finally coming into its own and beginning to demonstrate significant business value. The combined forces of big data, human expertise and AI are being used across industries as diverse as healthcare and manufacturing, as well as within all aspects of business. IT operations is one area that AI is beginning to contribute to enormously.
IT infrastructures are changing rapidly today, particularly hybrid cloud environments. While they are increasingly dynamic and agile, they are also extraordinarily complex. Humans are no longer able to sift through the variety, volume and velocity of Big Data streaming out of IT infrastructures in real time, making AI - especially machine learning - a powerful and necessary tool for automating analysis and decision making. By helping teams bridge the gap between Big Data and humans, and by capturing human domain knowledge, machine learning is able to provide the necessary operational intelligence to significantly relieve this burden of near real-time, informed decision-making. Industry analysts agree. In fact, Gartner named machine learning among the top 10 strategic technologies for 2016, noting "The explosion of data sources and complexity of information makes manual classification and analysis infeasible and uneconomical."
However, the current state of IT Ops is that the domain experts - typically IT administrators, IT operators for TechOps and Site Reliability Engineers (SRE) for DevOps - must manually gather this disparate information and apply their domain expertise in an attempt to make informed decisions. While these professionals are great at what they do, trying to analyze so much data from multiple tools leaves the door wide open for human error. On the other hand, analytics that are based on machine learning are quickly becoming a necessity to ensure the availability, reliability, performance and security of applications in today's digital, virtualized and hybrid-cloud network environments.
Historically, these domain experts have used multiple tools, each which monitored a specific element of the system and provided them with information about their network, virtual and physical infrastructure and application performance. While these tools provide pieces of the puzzle, they offer a narrow view of the IT infrastructure and, therefore, only one aspect of the tool chain. The other aspect is service desk tools that manage tickets and change management. Humans more often than not bridge this gap between the siloed monitoring tools of yesterday and service desk applications with their domain expertise.
What Modern Analytics Can Do
Real Monitoring Intelligence
react to incidents as well as trying to resolve them after they have spun out of control. Instead, AI provides technologies to help automate many of these tasks in order to handle incidents in advance. The whole notion of automating IT operational tasks, as well as preventing outages in the first place, and getting to the root cause quickly and in an automated way is the next frontier in remediating these issues.
Monitoring data is critical for identifying, predicting and preventing incidents - and it's something humans can no longer do. DevOps and TechOps teams already have so much on their plates that they cannot possibly devote the time needed to address every alert and analyze the masses of data constantly being generated. And today, they don't have to. Artificial intelligence is able to see past siloes for a deep view across the application stack to provide the analytics that help keep apps up and running at desired service levels.
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