The Next Generation of Software Intelligence: Leveraging AIOps

The Next Generation of Software Intelligence: Leveraging AIOps

What is AIOps and why will businesses need it sooner rather than later?

Ask any IT pro what the most exciting and potentially disruptive tech trends of the coming years will be, and artificial intelligence (AI) is likely to be near the top of the list. While people have been talking about the promise of this for decades, it's only in the last few years that processing power has advanced far enough to make it a practical reality for businesses.

Yet in many ways, AI is still a somewhat nebulous concept. While its potential is surely not in doubt, translating the principles into real-world results is easier said than done. And a major challenge for many enterprises will be how they can harness the power of AI and incorporate its capabilities into their operations to address emerging problems as IT departments scale up.

The answer to this is likely to be AIOps. In the coming years, more businesses are set to embrace this method of working in order to effectively integrate AI solutions into their business operations. But what exactly does this entail, and what can firms expect it to do?

An introduction to AIOps - what does it involve?

The term AIOps - a combination of artificial intelligence and IT operations - was coined by analyst firm Gartner a couple of years ago to reflect the evolution of emerging technology and how this is impacting business and IT operations.

"[utilizing] big data, modern machine learning and other advanced analytics technologies to directly and indirectly enhance IT operations" - Gartner’s official definition of AIOps

This includes activities such as monitoring, automation and service desk functions, offering more "proactive, personal and dynamic insight" into what is going on within the company.

In essence, AIOps looks to bring together three different areas of IT - service management, performance management, and automation - in order to provide IT departments with continuously-updated information on their operations and highlight where improvements can be made.

Why the need for AIOps?

One of the biggest challenges facing many IT departments at the moment is the huge complexity of today's environments. The scale of many firm's IT infrastructure, incorporating elements such as on-premises servers, managed and unmanaged cloud, mobile, and integrations with third parties, puts huge pressure on IT pros, and traditional methods of keeping their estate under control have proven ineffective at dealing with this complexity.

At the same time, and often as a direct result of this, the amount of data businesses have to deal with is growing exponentially. This means existing monitoring solutions may not be able to cope with the influx of data from new devices such as Internet of Things sensors.

Meanwhile, the rise of edge computing, in which the processing of data is done at the point of collection, rather than being sent back to centralized servers for analytics, makes it tougher for IT departments to retain control of all the information their company generates. With line of business units increasingly taking responsibility for their own data processing, vital data may not make it back to the IT team.

AIOps aims to address these issues by using automation to track operations throughout the network. Using existing sources of data such as traditional application and network performance anomalies, IT monitoring and log events, this is then processed using complex algorithms that can identify significant events without the need for manual intervention.

What is AIOps capable of?

By combining human intuition and observations with facts gathered from advanced algorithms using the latest data, AIOps aims to provide a much more complete picture of a business' operations and streamline how issues are dealt with.

As it is able to automate the intake and analysis of such a disparate range of data, it's able to filter out much of the noise and distractions that IT professionals normally have to wade through in order to uncover relevant and useful alerts and information.

AIOps' ability to bring together data from multiple sources also helps eliminate siloed systems that hinder many business' effectiveness and can offer a single, holistic view of the entire IT estate, encompassing compute, network, storage, physical, virtual, and cloud resources.

All this means that AIOps promises much faster issue diagnosis and resolution across all of a business' systems, allowing for reduced downtime and more productivity throughout the enterprise. Working at a speed and accuracy that human professionals alone cannot match, it should make IT operations smarter, more reliable and more streamlined.

Powering tomorrow's IT landscape

While AIOps is still in its relative infancy as a concept, its expected to make a much bigger impact in the coming years. As it uses advanced machine learning techniques to evaluate and process the data it inputs, such solutions are only set to get more accurate the more they interact with a business' systems.

While firms across a wide range of industries are likely to find themselves needing to rely on AIOps to maintain control as their systems continue to grow, companies that will be especially in need of this technology in the early stages will be those that are already generating huge amounts of data.

Financial services firms, manufacturers and pharmaceutical companies are among those that have been quick to embrace AI in order to manage their sprawling data environments, and firms such as telecommunications providers will also demand such solutions in the wake of 5G technology.

Potential use cases for AIOps may range from optimizing resources and planning for future capacity to spotting anomalies and security threats, and as artificial intelligence and machine learning technology continues to advance, it's likely that innovative businesses will find even more ways to take advantage of the technology to ensure their future operations can work as smoothly and effectively as possible.

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