AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.
The term AIOps was first coined by global research and advisory company Gartner in 2016.
AIOps uses big data and machine learning capabilities to enhance IT operations. It enables businesses to:
- Identify significant events and patterns related to system performance and availability.
- Diagnose and report root causes swiftly for either human or machine intervention and resolution.
- Aggregate large volumes of IT operations data relating to applications, analytics tools, and infrastructure components.
In each of the above examples, AIOps replaces multiple and sometimes convoluted manual IT operations with a single, intelligent AI platform. As a result, teams can respond to issues quickly and proactively. In some cases, human teams may not need to respond at all.
AIOps also seeks to bridge the gap between an increasingly dynamic IT environment and user expectations around application performance and availability. In the next section, we will take a closer look at how this gap is being bridged in more detail.
How does AIOps bridge the gap?
It should be noted that AIOps is not a panacea to increased efficiency and performance. Businesses will realize the most value from AIOps by using it as an independent platform incorporating data from all IT monitoring sources.
Data is digested via algorithms that streamline and automate IT operations monitoring. There are five types:
- Data selection – here, algorithms are used to filter through vast amounts of superfluous data to find elements indicating a problem. In most businesses, AIOps uses entropy algorithms to filter data from networks, infrastructure, applications, cloud, and storage components.
- Pattern discovery – are there relationships or correlations between selected data elements? What are the causes and the subsequent events? How can they be grouped for further analysis using text, time, and topology?
- Inference – or identifying the root causes of problems or other recurring issues to immediately rectify them.
- Collaboration – how can an algorithm apply the insights gleaned from problem resolution for future incidents? That is, can the problem-solving process be accelerated or better still, can problems be identified before they occur? Results are shared in a virtual collaborative environment which is particularly important for problems that transcend boundaries associated with technology, department, or skill level.
- Automation – wherever possible, response and remediation should be automated to make solutions more precise, timely, and cost-effective. Improved workflows can be triggered with or without human intervention.
In the final section, let’s take a brief look at some of the interesting and exciting ways AIOps is helping real-world businesses.
Schaeffler Group is a German company that manufactures precision components for various machines in the automotive, aerospace, and industrial sectors.
The company uses storage systems from many different manufacturers, so centralized monitoring of performance and various service level agreements (SLAs) helps it remain agile and responsive.
IBM Cloud Pak for Watson AIOps is a platform that enables businesses to reduce operational costs and deploy advanced, explainable artificial intelligence across the IT operations toolchain.
Watson AIOps are trained to make connections across data sources and common IT tools in real-time, which means the incident management and remediation process is more efficient.
Core features of this AIOps platform include:
- Reduced event noise – IBM’s platform uses artificial intelligence to automatically consolidate and group events into smarter, more actionable incident datasets. This reduces the prevalence of manual processes.
- ChatOps – recommended fixes and points of automation and delivered to teams in addition to other alerts and insights.
- Toolchain integration – the platform is compatible with over 100 IT operations tools from some of the most popular vendors in the industry. These include Slack, Azure, GitHub, AWS, SAP, and Oracle.
The ServiceNow Now Platform empowers businesses and people with more optimized processes and the ability to connect silos for a more seamless experience.
The Now Platform also offers these benefits:
- More engaging experiences – intuitive, omnichannel experiences that are as simple to use as common consumer apps and increase user satisfaction.
- Increased productivity – in a single, configurable workspace, teams can solve issues more quickly with purpose-built tools. They can also increase efficiency via the utilization of context-driven information and the ability to create engaging experiences.
- Automation – the Now Platform is about working smarter and faster. Artificial intelligence and analytics automate menial tasks and make predictions which frees up teams to focus on more important work.
- Innovation – any individual across the enterprise can automate, extend, or build workflow apps under a sole, unified platform.
The company’s platform modernizes IT portfolios by:
- Using predictive analytics and machine learning to prevent downtime and reduce customer impact.
- Streamlining incident management to reduce complexity and noise, and
- Correlating metric, trace, and event data for 360-degree visibility.
Predictive analytics, which is driven by machine learning algorithms and historical service-health data, can predict future incidents 30 minutes ahead of time.
Splunk’s service dashboards also enable teams to identify problem root causes at the code level.
Before implementing AIOps, the company had expensive and disparate IT operations tools.
Troubleshooting was a laborious, ad-hoc process where problems were solved via the process of elimination.
What’s more, there was little to no prioritization of tasks.
The end result was IT staff spending hours on the phone resolving issues.
Using Splunk, the company was able to reduce its mean time to repair (MTTR) by 63% and the number of IT incidents by 80%. Many of Molina’s antiquated tools were decommissioned in favor of the AIOps solution that was automated, scalable, and easier to use.
- AIOps uses big data and machine learning capabilities in the application of artificial intelligence to IT operations. The term was first coined by research company Gartner in 2016.
- AIOps replaces multiple and somewhat convoluted manual processes with a single, intelligent solution. More generally speaking, it helps businesses meet user expectations in the face of increasingly dynamic IT operations.
- AIOps uses algorithms to streamline and automate operations monitoring by way of data selection, pattern discovery, inference, collaboration, and automation.
Other examples merging development with internal operational departments
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