How Does AIOps Integrate AI and Machine Learning into IT Operations?

by June 29, 2020 0 comments

AIOP

AIOps restructures the modern IT ecosystems to leverage the best in AI and ML for Business Transformation

Data is everywhere growing across variety and velocity in both structured and unstructured formats. Leveraging this chaotic data generated at ever-increasing speeds is often a mammoth task. Even powerful AI and machine learning capabilities lose their accuracy if they don’t have the right data to support them. The rise in data complexity, makes it challenging for IT operations to get the best from Artificial Intelligence and ML algorithms for digital transformation.

The secret lies in acknowledging this data, to use its explosion as an opportunity to drive intelligence, automation, effectiveness and productivity with Artificial intelligence for IT operations (AIOps). In simple words, AIOps refers to the automation of IT operations artificial intelligence (AI), freeing enterprise IT operations by inputs of operational data to achieve the ultimate data automation goals.

 

Pillars of AIOps

AIOps of any enterprise stands firmly on four pillars, collectively referred to as the key dimensions of IT operations monitoring:

· Data Selection & Filtering

Modern IT environments create noisy IT data, collating this data and filtering for Excel, AI and ML models is a tedious task. Taking massive amounts of redundant data selecting data elements of interest often means filtering out up to 99% of data.

· Discovering Data Patterns

Unearthing data patterns implies to collate filtered data to establish meaningful relationships between the selected data groups for further analysis.

· Data Collaboration

Data analysis fosters collaboration among interdisciplinary teams across global enterprises, besides preserving valuable data intelligence that can accelerate future synergies within the enterprise.

· Solution Automation

This dimension relates to automating data responses and remediation, in a bid to more precise solutions achieved at a quicker TAT.

 

Unleashing AIOps for IT Analytics

A responsible AIOps platform combines AI, machine learning and big data with a mature understanding of IT operations. It makes way to assimilate real-time and historical data from any source for cutting edge AI and ML capabilities. This makes it possible for enterprises to get a hold of problems before they even happen by leveraging on clustering, anomaly detection, prediction, statistical thresholding, predictive analytics, forecasting, and more.

 

Driving AIOps trends for the Modern Business

IT environments have broken silos and currently exceeding the realms of the manual human scale of operations. Traditional approaches to managing IT find redundancy over the dynamic environments governed by technology.

1. Data pipelines that ITOps need to retain is exponentially increasing encompassing a larger number of events and alerts. With the introduction of APIs, digital or machine users, mobile applications, and IoT devices, modern enterprises receive higher service ticket volumes. A trend that is becoming too complex for manual reporting and analysis.

2. As organizations walk on the digital transformation path, seamless ITOps becomes indispensable. The accessibility of technology has changed user expectations across industries and vertices. This calls for an immediate reaction to IT events especially when an issue impacts user experience.

3. The introduction of edge computing and cloud infrastructure empowers the line of business (LOB) functions to build and host their own IT solutions and applications over the cloud to be accessed anytime anywhere. This calls for an increase in budgetary allocation increase and more computing power (that can be leveraged) to be added from outside core IT.

 

AIOps bridges the gap between service management, performance management, and automation within the IT eco-system to accomplish the continuous goal of IT operation improvements. AIOps creates a game plan that delivers within the new accelerated IT environments, to identify patterns in monitoring, service desk, capacity addition and data automation across hybrid on-premises and multi-cloud environments.

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