What is AIOps & What’s Driving the AIOps Revolution?
So, What Is AIOps?
When the acronym AIOps was first introduced by Gartner, it was originally intended to stand for “Algorithmic IT for Operations.” But, with the increased maturity and adoption of artificial intelligence systems, the more accepted term is now “Artificial Intelligence for IT Operations.”
AIOps products are now entering their revolution phase. According to a Gartner report released in late 2018, “by 2023, 30% of large enterprises will be using AIOps platforms and digital experience monitoring technology exclusively to monitor the non-legacy segments of their IT estates, up from 2% in 2018.”
This is the result of an evolution in the tools, techniques and mindset that IT operations teams must now adopt in order to deal with the increasingly complex infrastructure that supports the delivery of IT services and applications.
Behind the AIOps Product Revolution
Today’s global businesses are undergoing a digital transformation. When it comes to satisfying their needs, traditional approaches to managing IT infrastructure have come up short. A variety of factors have contributed:
Digital transformation requires a delivery infrastructure that consists of hybrids of in-house and third-party systems, and on-prem and cloud systems – each with their own management and monitoring needs. This leads to tool sprawl and overlapping functionality.
The exponential growth of generated events:
The growth in complexity brings with it an onslaught of event information generated by infrastructure elements. Not all events are equal – it is essential to cut through the chaff and rapidly triage these events to focus on material information. Legacy IT management systems struggle to cope with this information overload. When problems occur, the sheer volume of data makes it difficult to ascertain what happened.
The need for a predictive approach:
It is no longer enough to find and fix incidents after they occur. With the digital transformation comes the increased dependency on the IT service delivery infrastructure. IT organizations need a predictive approach toward getting ahead of problems before they manifest themselves.
The siloed nature of traditional IT service management:
The ITIL model has served IT operational needs very well for the past few decades. It has done a great job at blanketing the design, delivery and growth needs of typical IT organizations. As part of the operational aspect it has classified functional areas around Incident, Event and Problem Management. However, this has led to the increased isolation of functional teams. Today, each team has its own set tools and goals. There is a lack of collaboration and sharing of information. This leads to losses in efficiency when attempting to manage the infrastructure as a whole.
What Does AIOps Bring to the Table?
The primary benefit of AIOps is increased operational efficiency of the service delivery infrastructure. This is driven by the ability to glean insights from large volumes of data – and then transforming these insights into action.
To realize these benefits, AIOps platforms leverage advances in data science and machine learning to manage the ingestion and manipulation of data.
By consolidating and aggregating data from across the spectrum, AIOps platforms offer the ability to look at the greater picture. For instance, they can provide a top-to-bottom and end-to-end view of the entire supply chain that drives a particular service or business function.
Both real-time and historical data is aggregated. The information can be from across performance, application and infrastructure monitoring systems. Typically, a unified form of storage is used to collect and disseminate this data. A key part of this process is to enrich the information as it is ingested.
Overlaying topology, time series and meta-data information are commonly used enrichment techniques. They add tangible value when subjected to deeper analysis.
To help answer the question “What is AIOps?” and illustrate how it is transforming the world of IT operations management, let’s look at an AIOps case study:
Predictive IT Operations:
Performance data can be used in real-time to build dynamic thresholds indicating what constitutes normal behavior. Peer system analysis further reinforces this by providing comparable baseline information.
Today, some of the best AIOps tools take this to the next level by performing trend analysis in real time. With this intelligence, when a service is approaching levels where performance will be compromised, an event is launched. The event can then be acted upon, either by channeling it to the proper remediation expert, or by triggering automation to orchestrate actions.
This results in truly predictive and proactive IT operations management. AIOps makes it possible to enable incident response prior to a service degradation or outage scenario.
Business Impact Analysis:
The complexity of the delivery infrastructure often makes it very difficult to understand the true impact of issues in the infrastructure – for example, which services will be affected.
Some AIOps products have the ability to bridge this gap by tying the business services delivered to the exact set of network and infrastructure elements that provide the service, along with their relationships. With this dependency model, AIOps products can provide a true picture of the topology of a business service.
This makes it possible to prioritize and address issues quickly based on their impact to critical business services. In addition, the ability to provide on-demand provisioning and capacity planning for a particular business service is greatly enhanced.
And there lies the reason why you should be asking “What is AIOps?” – it is on the cusp of a revolution as it has been proven that properly applying AIOps technologies to IT infrastructures that support revenue-generating services and applications will provide measurable improvements in business outcomes.