Why a Platform Approach is Best for AIOps
Will Cappelli | February 27, 2020

IT operations management vendors are adding AI capabilities to their wares, but central AIOps platforms deliver the most value by coordinating all those domain-specific tools.

IT operations management vendors are adding AI capabilities to their wares, but central AIOps platforms deliver the most value by coordinating all those domain-specific tools.

AIOps’ success at taming the dizzying operational complexity of modular and dynamic IT environments has prompted IT operations management vendors to add AI features to their products.

This begs the question: Should AIOps be delivered as a function of these domain-specific products, or instead as an independent platform that organizes those other technologies, and orchestrates their interaction?

The superior option is unequivocally the latter. Here’s why.

An abridged history of IT Operations

In 2000, the modern IT world began to take shape, as the Internet first linked together computing devices across the globe. At this point, these devices generated an exabyte of self descriptive data about their operations, half of which was either irrelevant or duplicated. 

Four big vendors — IBM, CA, HP and BMC — provided large clunky software to manage this operational data via systems management, network management, asset management, and service desk tools.

Five years later, virtualization emerged as a layer on top of the Internet, while self describing data had increased by an order of magnitude to a zettabyte — 10 with 19 zeros after it. The quality of this data had worsened, with 60% being noise or duplicated. Struggling to innovate, the big four IT management vendors nurtured a venture capital community to promote the creation of startups with modern technology ... that they could then acquire.

By 2010, cloud computing emerged, and self descriptive data ballooned to a yottabyte, with 70% being noise or duplicated. At this point, the big four vendors were displaced by specialized vendors organized into eight submarkets: 

  • Event Correlation
  • Smart Alerting
  • Service Desk & CMDB
  • Process Automation
  • Topology Analytics
  • Time Series Databases
  • Log Management
  • Monitoring (including end user, application, infrastructure and network monitoring)

This specialization was made necessary by the complexity that the cloud, virtualization, and the Internet created in the global computing infrastructure.

Fast forward to 2015. Complexity had worsened with the atomization and hyper-modularization of the global compute infrastructure as a result of the adoption of technologies like microservices. Self describing data reached a brontobyte, 80% of which was noise and duplicated. 

To make matters worse, the number of products in the eight submarkets had ballooned, and they didn’t interoperate with each other. At this point it was virtually impossible for enterprises to coordinate the operations of these eight different types of technology. It became obvious that we needed a capability for automated observation and analytical response to coordinate what those eight subtypes of technology were doing. As a result, the idea for AIOps became well articulated.

In 2020, we’ll be up to a gegobyte of self-descriptive data, with a whopping 90% of it being noise or duplicated. Complexity will worsen due to the adoption of continuous integration / continuous delivery (CI/CD) software pipelines. For CI/CD environments to succeed, we need a third element: CA, or continuous assurance of the process that generates digital services, and AIOps plays a central role in that. 

What we need from AIOps

To deal with this gegobyte of highly suspect data and manage increasingly complex IT environments, organizations need 5 different types of AIOps algorithms, or 5 dimensions of functionality: 

  • data selection
  • pattern discovery
  • inference
  • communication
  • robotics (automation)

In the real-world, here’s how it works. 

An AIOps workflow ingests heterogeneous data from many different sources. Using entropy algorithms, it removes noise and duplication, which can amount to more than 90% of the data, and selects only the truly relevant data. It then correlates this relevant information using various criteria, like text, time and topology. 

Next, it discovers patterns in the data, and infers which data items signify causes, and which signify events. It then communicates the result of that analysis to a collaborative environment, which will support automated responses to what has been discovered.

At almost every step along the way in that workflow —  from the initial ingestion of the data to the ultimate response to what has been discovered — it’s necessary to have automated observation, analysis, and automated response to what has been found.

In defense of independent AIOps platforms

Vendors in the eight submarkets only provide partial AI capabilities, often relying mostly on antiquated and inefficient rules-based correlation, and semi-automated statistical analysis. But even in cases where the AI capabilities are legitimate and valuable, these products are still all  domain specific and don’t interconnect with each other, which is a central requirement of fully-functioning and true AIOps, and one of a platform soution’s core values.

Indeed, a key reason why AIOps became an important factor in the market was precisely to link these different types of technologies together. Although these vendors claim to offer AIOps, their respective solutions are somewhat myopic.

All of them stay away from that role of organizing and integrating what they do with all of the other pieces of the puzzle. It’s precisely in that integration that AIOps platforms deliver their core value. In other words, it’s only through the intelligent integration of all these different functionalities that we can manage that gegobyte of self descriptive but highly corrupt data.


The complexity and importance of modern IT environments is such that making IT ops monitoring and management products better and smarter with AI isn’t enough. AI has a vital role to play. It should act as the brain, as the central nervous system that brings together all these disparate areas of IT operations management. It should be a coordinating layer where AI sits at the heart of it and acts as a central smart switch. That’s what AIOps platforms such as Moogsoft AIOps do.

Moogsoft AIOps is a unified, collaborative platform powered by 50+ patented AI algorithms that streamlines, automates and accelerates, from end to end, your incident management workflow, helping you attain continuous service assurance. Acting as a real-time, central system of engagement, it adds a critical layer of intelligence between performance monitoring and ITSM systems.

Moogsoft AIOps ingests data from all your IT ops monitoring and management tools, sharply reduces event noise, and correlates and groups important alerts. It then pinpoints root causes, and facilitates cross-team collaboration. With Moogsoft AIOps, you detect and resolve IT problems early and quickly, before they turn into outages and affect your customers. 

As such, it’s a perfect example of the superior value that AIOps platforms provide over domain-specific tools with AI capabilities.

Moogsoft is the AI-driven observability leader that provides intelligent monitoring solutions for smart DevOps. Moogsoft delivers the most advanced cloud-native, self-service platform for software engineers, developers and operators to instantly see everything, know what’s wrong and fix things faster.

About the author


Will Cappelli

Will studied math and philosophy at university, has been involved in the IT industry for over 30 years, and for most of his professional life has focused on both AI and IT operations management technology and practises. As an analyst at Gartner he is widely credited for having been the first to define the AIOps market before joining Moogsoft as Field CTO. In his spare time, he dabbles in ancient languages.

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