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AIOps: So Much Buzz. But For What?

AIOps: So Much Buzz. But For What?

  • AIOps deliver a primary and single window for analysis across all domains, reducing the need for multiple tools for analysis.
AIOps

IBM recently announced the acquisition of Turbonomic, a Boston-based Application Resource Management (ARM) and Network Performance Management (NPM) software provider. The acquisition will help IBM boost its full-stack application observability and management efforts to ensure better performance and minimise cost using AIOps. 

AIOps is a continuous integration and deployment (CI/CD) for core IT functions. It has two main components–machine learning and big data. It denotes a shift from siloed data to a more dynamic business environment for digital transformation.

As per a Gartner report, in 2021, 50 percent of the organisations will use AIOps with application performance monitoring to gain insights into mission-critical applications and other operations. Another study showed the enterprise adoption of AIOps had seen an 83 percent increase since 2018.

Further, the global AIOps market is expected to reach a valuation of $3127.44 million by 2025 at a CAGR of 43.7%.

IBM and AIOps

In an official communique, IBM said the Turbonomic deal would complement its earlier acquisition of Instana, an application performance management (APM) service provider, in addition to its IBM Cloud Pak for Watson AIOps and be integrated with Red Hat OpenShift. By combining Turbonomic’s ARM and Instana’s APM capabilities, IBM can now automate and optimise its underlying IT infrastructure and ensure better performance across applications. “By acquiring Turbonomic, IBM is the only company that will be able to provide customers with AI-powered automation capabilities that span from AIOps (the use of AI to automate IT Operations) to application and infrastructure observability – all built on Red Hat OpenShift to run across any hybrid cloud environment,” the company said in a statement.

Notably, at its annual Think Digital event in 2020, IBM had launched Watson AIOps for its IT operation management services. It leverages machine learning, natural language understanding, explainable AI etc to automate IT operations. Watson AIOps helps companies with cost and personnel efficiencies, improving resilience across the company’s information architecture, and fasten issue resolution. It extends beyond traditional structured sources of data and combines metrics and alerts with semi- and unstructured data like logs and tickets

The rise & rise of AIOps 

Many organisations find it hard to manage large chunks of data. AIOps helps enterprises break the data silos while getting complete visibility across the IT environments.

Credit: Adroit

For an IT operations team, operational noise is among the top concerns. It leads to higher operating costs, performance and availability issues, and challenges to the digital enterprise initiatives. AIOps helps reduce this IT noise and eliminate it by creating correlated incidents that point to the potential root cause.

The use of a wide range of monitoring tools is complex as it requires the team to arrive at the results, quickly correlate, and analyse multiple application performance metrics. AIOps deliver a primary and single window for analysis across all domains, reducing the need for multiple tools for analysis.

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If and when properly implemented, AIOps platforms save the time IT professionals spend on mundane tasks, routines, and daily alerts. By training the AIOps platform, the IT team can improve system behaviour and effectiveness.

It also offers other business advantages such as–process automation, accessing accurate data for business collaboration, and simplified and unified IT operations management.

The shift towards cloud computing and digital transformation has created more complex IT operations management (ITOM). It has increased repetitive manual activity, resulting in a challenging environment for IT operations to keep up with the ever-increasing pace and volume of demands. AIOps helps enterprises operate their ITOM with the level of speed and agility as expected by the end-user.

The major AIOps players include AppDynamics, BigPanda, Devo, Elastic, Logz.io, Splunk, FixStream, Dynatrace, etc. Several established companies are introducing new versions of AIOps with digital and automation solutions. Few examples include:

  • In 2019, DynaTrace introduced Davis, which automatically identifies anomalies within huge cloud deployments. Using deterministic AI, Davis is also capable of pinpointing the exact cause of the problem.
  • BMC introduced BMC Helix in 2019, an AI/ML-powered end-to-end ITSM platform that monitors events and anomalies in the IT space.
  • In 2020, managed network services company Hughes announced the commercial availability of its AIOps solution for the enterprise WAN.

AIOps is well on its way to becoming the next big thing in IT management and AI-led correlation.

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