How Artificial Intelligence Is Transforming IT Operation Analytics Posted on : Jul 02 - 2016

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

Because today's TechOps and DevOps environments are so complex, there is a need to automate, learn and make intelligent, informed decisions based on real-time analysis of Big Data arising out of the entire application infrastructure stack. Following are key analytics for IT operations:

Behavior Profiling - This type of analytics understands the behavior profile of each and every metric, how that flows into the object behavior and then how the object behaviors relate to other object behaviors across the hybrid cloud environment. It is a multi-dimensional problem, and understanding and adapting to "normal" behavior is extremely important.

Anomaly Detection - This is the bedrock of what is typically referred to as diagnostic analytics. Best-of-breed machine learning algorithms should be able to look at contextual, historical and sudden changes in the behavior of objects to detect anomalies. Understanding when there is a real anomaly and more importantly, when there is not, is critical to avoid generating false alarms.

Topology Analysis - Topology is something every IT administrator or SRE should be aware of. This is the understanding of the hierarchal, peer-to-peer and temporal relationship between hybrid cloud elements. This type of analysis should be able to self-learn the inter-relationships of objects and the impact of their performance on one another. Learning those relationships and maintaining that understanding in order to spot trouble in time is extremely important for both TechOps and DevOps environments.

Root Cause - With the ability to zero in on the cause and impact of an incident, root-cause analysis fast-tracks the resolution and reduces mean time to repair substantially.

Predictive - As the name implies, analytics of this kind help operators identify early indicators and provide insights into looming problems that may eventually lead to performance degradation and outages.  Predictive analytics are also good at providing early insights into anomalies to better plan for what's ahead.

Prescriptive - These analytics provide insight-driven recommendations to remediate an incident. These recommendations should capture tribal knowledge gathered over the years in the organization, best practices in the industry and may even be crowd-sourced to capture state-of-the-art knowledge. These analytics provide the opportunity to finally close the loop in automated IT Operations Management.

Real Monitoring Intelligence

The modern IT environment has gone far past the point of staff being able to effectively

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. Source