AIOps Use Cases To Optimise IT Operations

When new technology comes into the world and is adopted by businesses, it's most often for operational benefits. These benefits can be seen in the form of cost savings, increased efficiency, and a more streamlined process that reduces man hours spent on activities that don't drive value for the business. 

The enterprise IT landscape is experiencing many changes influenced by key market trends such as:

•Hybrid / Multi-cloud Adoption

•Rapid Growth of Microservices and Function as a Service-Based Application Architectures

•Best of Breed Approach for Cloud Products and Services Consumption

•Hybrid Working Patterns 

•Work from Anywhere and Anywhere Operations

These trends are resulting in increased operational complexity due to the proliferation of tools, technologies, and large data sets that are to be managed by the IT organization: 

•Hybrid/Multi-cloud Environments with multiple toolsets generating large amounts of data

•Broken CMDB implementations trigger complexities in resolving IT operational issues leading to downtimes

•Lack of data correlation and actionable insights for the CxOs and operations teams

•Lack of application & asset life cycle context

•High rate of alerts leads to noise in IT operations and cost escalations

•Poor Digital Experience monitoring leads to customer churn

•Collaboration issues between multiple teams’ impact turnaround time, thereby impacting End User Experience

Therefore, enterprises are looking for solutions that can reduce the Total Cost of Ownership (TCO), improve infrastructure and application uptime, and enhance User Experience and productivity to ensure that the IT operations are agile and aligned to the organisation's business strategies. Traditional management tools will not be sufficient to address the complexity posed by a combination of on-premises and cloud-based infrastructure and applications. 

Artificial Intelligence for IT operations, i.e., AIOps, is a term coined by Gartner to refer to the application of Artificial Intelligence (AI) such as machine learning models and natural language processing to optimize, streamline and automate operational workflows. AI in operations is built on machine learning, which allows computers to analyse data, find patterns and make predictions. AI in operations can automate tasks, like scheduling and inventory management, and use predictive analytics to look at future challenges and recommend solutions.

AIOps tools and platforms address the gaps in the IT environment by providing an ability to ingest both structured and unstructured data from application and infrastructure sources. It uses advanced analytics to provide data enrichment, event correlation, Machine Learning based contextualisation of alerts, and a powerful visualisation with intuitive and interactive interfaces to deliver user-configurable dashboards. 

AIOps entails analytics, big data, and capabilities to gather data by an array of IT infrastructure components. It eliminates duplication of work and preserves only relevant data using algorithms. This kind of heavy filtering of heterogenous data helps manage redundant tickets by cutting down the number of alerts IT Ops teams must otherwise handle. 

AIOps can intelligently infer data items, correlate necessary information, and diagnose specific causes to signal IT and DevOps in a collaborative environment for quick resolution or remediation of issues without human intervention.

Why use AI in Operations?

Operations are one of the most common functions for AI in businesses today. Operational Artificial Intelligence (AI) is what it sounds like: an artificial version of AI that focuses on operations instead of other functions like discovery, data, or analysis. It can be implemented to help your business with efficiency, optimization, and even collaboration when needed. As you explore implementing AI in your IT organisation, some of the best practices are outlined below. 

With AIOps tools maturing over the last few years, enterprises with distributed infrastructure and application landscapes can thus benefit through the following use cases as tabulated below:

Girish Chandangoudar

Guest Author Girish is the VP, Head of Infrastructure services, Happiest Minds Technologies