“Only AIOps Technologies Are Equipped To Monitor The Modern Data And Analytics Platforms” - HPC ASIA
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“Only AIOps Technologies Are Equipped To Monitor The Modern Data And Analytics Platforms”

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Pankaj-Prasad GartnerArtificial intelligence and machine learning are the latest technologies that HPC, Cloud and IoT experts are talking about. The scenario is such that these two components are being adopted by many forward thinking organizations to automate manual processes within IT operations. In fact, AI and Machine Learning will ensure that tedious tasks that take hours to accomplish can be done faster and with more precision without any errors.

Leading analyst firm Gartner Research has put a major focus on this paradigm shift in the big data analytics scenario. With the rising complexity in the digital transformation processes of an enterprise Gartner has coined a new term that is aimed at explaining the change that is taking place within the IT operations of an organization. It is called AIOps – Algorithmic IT Operations – coined by Gartner Research analyst Colin Fletcher.

So ahead of the research firm’s Gartner IT Infrastructure, Operations and Data Center Summit in Mumbai to be held in May, Gartner India’s principal research analyst Pankaj Prasad explains what AIOps is all about and how relevant is it in the Indian scenario, and what the enterprises looking digital transformation should focus on right now. Prasad covers data center availability and performance management and is the primary analyst for IT infrastructure monitoring (ITIM) at Gartner. In addition, he covers IT event correlation and analysis (ECA), IT operations analytics (ITOA), algorithmic IT operations (AIOps), business service management (BSM), and IT service alerting or IT notification.

Why did Gartner coin AIOps and how did it evolve from IT operations analytics (ITOA)?

Early technology adoption of big data and technologies focused on IT operations management (ITOM) toolsets, purely around data-centric monitoring and analysis was called “IT operations analytics” (ITOA). IT end users within an organization would call up the network managers and service desks to complain about network issues. The network managers would use ITOA to check the data or the event after it has taken place. The impact that this early adoption has created on the availability and performance discipline is now reshaping ITOM as a whole and beyond. This broader trend is what we now call “algorithmic IT operations” (AIOps). It has an interesting and important interplay with all disciplines under IT operations and the potential for creating an orchestration across various ITOM toolsets.

So what is Algorithmic IT operations (AIOps)?

AIOps has evolved from a set of technologies that were under IT Operations Analytics. With AIOps, the managers can look at the IT infrastructure functioning real time and automate a fix. The industry is moving from ITOA to AIOPs. While ITOA is more focused on analyzing IT operational data, the AIOps platform technologies comprise of multiple layers that address data collection, storage, analytical engines and visualization. They enable integration with other applications via application programming interfaces (APIs) allowing for a vendor-agnostic data ingestion capability. AIOps platforms can thus seamlessly interact with IT operations management (ITOM) toolsets because of the ability to deal with data from any tool irrespective of the data type.

How different is AIOps from Big Data Analysis?

A. Over the past 4-5 years IT organization teams involved in availability and performance management have made increasing use of big data and machine assisted analytics for improving diagnostic and troubleshooting capabilities of their teams.

While the broader data analytics is concerned with use-cases and models that will be based on an organization’s unique needs, AIOps platform technologies are designed for typical IT operations use case. AIOps involves, post-processing of events streams that come from monitoring tools, bi-directional interaction with IT service management tools, and possible integration with automation toolsets for implementing the prescriptive information provided by the platform.

How is AIOps relevant to business?

By 2019, 25% of global enterprises will have strategically implemented an AIOps platform supporting two or more major IT operations functions. The traditional role of analytics is shifting from merely supporting decision making, towards increasingly driving business processes by not only recommending the best possible actions, but triggering those actions in an automated manner. In addition, analytics is being used to predict preferences of customers to drive better and more engaging customer experience.

All of this means, analytics platforms are becoming central drivers of modern business and I&O teams cannot look at analytics platforms like a lab environment anymore. Consistency in performance of data and analytics platforms is key, and only AIOps technologies are equipped to monitor the modern data and analytics platforms. So as organizations move towards complete digital transformation, they will experience a change in scale which can be managed by AIOps to remain cost effective.

What are the challenges inhibiting adoption?

The main challenge is separating the marketing jargon from the actual capability and assessing the effort needed by the technology user. Machine learning – which is a key component in analytics platforms – needs huge amounts of data and more importantly, interactions with humans in a real-world scenario. These two components are critical to extracting value from AIOps platforms, and they take time. Any investment into the toolsets needs to account for the investment in terms of data, human-machine interaction and time.

How is AIOps relevant to India?

India cannot remain immune to the technological developments if it has to stay competitive. There is an increase in adoption of new and emerging technologies like the Internet of Things (IoT), containers and microservices, and it is only a matter of time before these technologies see increased adoption in production environments. Existing ITOM tools and IT operations are ill-equipped to deal with the high-volumes of data, varying data types and speed of correlation needed to deal with these new technologies. CIOs in India will need to keep themselves abreast of AIOps to ensure they leverage the right technology which will future-proof their organizations and give their IT teams the advantage to stay competitive.

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