WHY IT MUST ENFORCE AIOPS FRAMEWORK TO MAXIMIZE BENEFITS OF AI DATA? Posted on : Oct 06 - 2020

AIOps: Is it worth the hype or a necessary cog in IT?

AIOps may be a new buzzword, but it is an advanced version of IT Ops that deals with big data operations along with Artificial Intelligence, machine learning, and data analytics. In simple words, AIOps refers to the automation of IT operations artificial intelligence (AI), freeing enterprise IT operations by inputs of operational data to achieve the ultimate data automation goals. It aims to help IT run more efficient operations, make better decisions, and support business productivity. Also, it plays a pivotal role in determining the relationship between the thousands of alerts that all the elements of an IT environment can now generate. This is because most AIOps models offer IT teams with more context, and actionable intelligence.

Due to the current pandemic infected market, the hunger for AIOps in the IT landscape has risen. According to a report by Adroit, the global AIOps market is expected to grow to US$237 billion by 2025. This implies that the IT sector strives to move beyond the static and predictable physical systems that have dominated the space for decades to a software-defined resource environment that changes and reconfigures with market demands. Even analyst firm Gartner asserts exponential boom with businesses scrambling to understand and meet this new requirement. In fact, Gartner estimates that the average enterprise IT infrastructure generates two to three times more IT operations data every year.

The advantage of leveraging the AIOps platform is invaluable. AIOps can analyze data about the current IT processes in the DevOps workflow and extracts significant events related to slow-downs or outages, using AI and machine learning. It detects inefficient patterns in the development or deployment workflow. It also helps DevOps teams to manage the growing need for DevOps services within an organization. Furthermore, IBM says that AIOps provides visibility into performance data and dependencies across all environments, identifies error patterns proactively, and automatically alerts IT, staff to problems, their root causes, and recommended solutions. AIOps also removes noise and distractions, which enables IT personnel to focus on essential issues rather than distractions from irrelevant alerts.

Because of these, IT heads must that any AIOps solutions should have the capability of higher data ingestion, data aggregation from IT infrastructure monitoring (ITIM), network performance monitoring and diagnostics (NPMD), digital experience monitoring (DEM), and application performance monitoring (APM), and lastly, data enrichment by applying metadata and tags to provide context for search in indexing. These three capabilities are essential as AIOps can empower businesses with proficiency in real-time data correlation. It must provide effective handling, with timely automated remediation. It should also have the ability to map out the dependencies across numerous domains, provide predictive maintenance, capacity, and event management. However, one must be careful while implementing AIOps. It neither works in isolation and nor does it replace existing approaches. Instead, it is a crucial part of an AI-powered platform, where AIOps is fully integrated with application performance, business analytics, user experience, and infrastructure monitoring capabilities. Source