AIOPS – A myth or a reality?

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AIOPS is very likely to become the fundamental building block/platform for sustaining, maintaining, and scaling the enterprises’ architecture to manage the exponential increase in data generated by underlying systems/platforms in large enterprises.

AIOPS is one of the most talked-about acronyms in the IT world these days. It is a crucial area that many CTOs will need to address in the present or, at the very least, in the near future to ensure that their enterprises can sustain the impending big data implosion. So, what does this term mean and is it relevant? Or is it just another example of technical jargon?

The term AIOPS was coined by Gartner and it was the acronym used for Algorithmic IT Operations back in the day. It was used to represent solutions that used machine-learning algorithms to solve unknown IT problems and intelligently predict and automate execution of ITOPS jobs. AIOPS has grown immensely over the past few years, with many vendors providing their own implementation of this concept. Gartner currently redefines this term as “Artificial Intelligence for IT Operations.”

AIOPS is the next evolution for its predecessor ITOA. ITOA or IT Operations Analytics is nothing but analysis of historical data to determine what went wrong and improve operations based on past learnings. AIOPS uses historical data however, not to analyze but rather train its models to predict future probability of system errors and prevent those from ever occurring in the first place. It aims to make sense of the vast amounts of data received from disparate sources/silos and identify inter-relationships between various components of the enterprise.

In order to scale their enterprises’ architecture proportionally with the exponential increase of data influx, most enterprises have ended up adopting many toolsets and platforms to assist them in making sense of and monitoring their applications, infrastructure, and data. While this has proved to be a good stop gap solution in the near term it has convoluted their enterprise architecture to the extent whereby it will no longer be feasible to sustain the same going forward. This is the problem statement that most AIOPS platforms aim to solve. The simplified AIOPS model can be illustrated by the diagram below

AIOPS model can be broadly classified by the following lifecycle:

  • Collection of data from different sources – systems, platforms, n/w, and cloud
  • Ingestion of data into a centralized data lake
  • Segregation of data into meaningful categories
  • Generation of the abstract enterprise graph from the historical data
  • Predicting future events from real time data
  • Acting based on predictions and learning from the same
  • Measuring accuracy and supplying feedback to the model to improve prediction and action accuracy.

In a nutshell, an AIOPS platform in its simplest avatar helps aggregate and make sense of disparate data sources, identify the link and interrelationships between different silos and help predict events, prescribe solutions, and act based on real-time data streams.

AIOPS is very likely to become the fundamental building block/platform for sustaining, maintaining, and scaling the enterprises’ architecture to manage the exponential increase in data generated by underlying systems/platforms in large enterprises. Hence, it’s imperative for enterprises to hop on the AIOPS bandwagon at the right time to ensure that they are in a timely position to efficiently handle the scale and manner at which data is being generated from the systems within the enterprises these days.

If you would like to know more about AIOPS or wish to collaborate with the Applied Innovation Exchange at Capgemini in this area for any business use case please email wesley.weel@capgemini.com

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