The modern IT estate is a complicated place. With distributed architecture structures, data streaming between public and private clouds, a Kubernetes orchestration layer automating application deployments, servers spinning up and spinning down upon demand, and open-source software trying to work seamlessly with commercially licensed software, there is obviously the need for a tool that ensures the system is working most productively. This is where AIOps fits in. AIOps helps IT departments to automate routine practices so that issues can be resolved quickly and efficiently, thereby reducing demands on already over-taxed IT departments.
A portmanteau of Artificial Intelligence (AI) and operations (Ops), AIOps evolved from earlier system operations technologies like Application Performance Monitoring (APM), Business Process Management (BPM), and IT Operations Analytics (ITOA), adding an element of AI and machine learning (ML) to the process.
An AIOps application can consume many more data types than an APM, BPM, or ITOA system and it accumulates and analyzes system data, learning about a company’s day-to-day operation along the way, then proactively fixes any potential operational issue. AIOps enhances an IT operation by providing end-to-end visibility into a company’s applications and infrastructure, acting proactively when needed, and alerting others when necessary.
AIOps reduces system noise, breaks down data silos, accelerates the root cause analysis of problems that arise in the daily operation of a business. It can proactively self-heal an IT system as well as help with employee collaboration, while reducing IT service ticket volumes.
AIOps uses pattern matching to discover problems that might arise in the future. It then isolates the issue and develops a process to alleviate the problem going forward. Utilizing ML’s speed and power, an AIOps system can discover and correlate important patterns in IT data that humans would miss. Since an ML system can constantly take in new data and evolve with this new data, ML algorithms underlying the AIOps system can continually adapt to new issues discovered in the data.
A typical IT estate is filled with an array of applications that do everything from track customer sales, host company websites, manage a company’s marketing and social media channels, as well as monitor and secure the entire IT and customer system. As application developers create increasingly complicated apps, drawing upon more and more system resources, it’s no longer acceptable to just monitor for system availability, while also tracking errors after they occur, IT now has to be proactive.
It has to ensure data is being utilized properly, that applications are running effectively, alerts are routed correctly, and the system is optimized for its current usage. AIOps can successfully differentiate between alerts that require immediate action and ones that can be sidelined for another time, which can result in considerable savings in time, money, and employee resources.
AIOps can provide dynamic baselining that helps uncover critical problems with customer-facing apps or services. AIOps reduces false or unimportant alerts that often clutter inboxes and cause false alarms. It diminishes the noise associated with the multiple events running on a system. AIOps baselines the environment in both slow and busy times. It can then utilize this knowledge to understand the alerting systems, differentiating between alerts that are insignificant to ones that portend bigger, system-affecting problems.
AIOps administers operational data, captures relationships between volume drivers and resource utilization while tracking ongoing user and system behavior. It also identifies important correlations that drive application constraints and creates statistical summaries on multiple levels of the organization’s data. Ultimately, it generates models of application behavior the system must follow down-the-line. However, AIOps is not static, it evolves with the system over time, constantly adding information to the system’s body of knowledge and data dictionary.
AIOps can build models that proactively alert system admins about upcoming issues. This type of predictive alerting reduces customer service requests, thereby limiting service outages that might impact customers. An ounce of prevention is worth a pound of cure as the saying goes, but, in this case, proactive problem solving can save considerable money and a lot of wasted employee time. Customer service is also improved as IT becomes a proactive partner rather than a reactive one.
Technology never stops evolving. AIOps provides companies with a single pane of glass view into their data, systems, and processes, helping reduce or even avoid downtime. An AIOps solution creates a powerful data schema that can help users break out of organizational silos hampering data use today. AIOps’s ability to structure, organize, and metatag data makes it even more secure, consistent, and useful.
Having evolved from tools like APM, BPM, and ITOA, AIOps is the latest in a generation of business process tools designed to make the IT department run more efficiently. Technology evolves and, unquestionably, AIOps will grow into something faster, better, and much more effective. The IT estate has become so complicated, with so many pieces of software and hardware trying to work in concert together that AIOps is a necessary tool for every IT department.
