AIOps or Artificial Intelligence for IT Operations is the latest buzzword in IT operations today, promising to save organisations precious time and efforts taken to identify and resolve issues across their increasingly complex estates. AIOps is a term coined by Gartner to describe how AI capabilities can be deployed to address IT operations by identifying and reacting to issues automatically.
AIOps is the next generation of IT operations analytics, assisting organizations to address IT challenges on a number of topics including-
• Siloed IT operations
• Digital business transformation
• Exponential growth of uncorrelated data
Are AIOps and ITOA similar technologies?
AIOps is a big shift from the traditional ITOA platforms which was a precursor to the AIOps evolution. ITOA was focused more on the unification for historical data analysis across multiple domains to resolve problems with observational data. A step ahead to ITOA, AIOps leverages big data and machine learning techniques to deliver proactive and predictive insights into problems to automate remedial actions helping businesses with proactive planning and identification of business-impacting issues before they occur.
AIOps Adoption and Scalability
Research major Gartner recommends implementing AIOps in phases. Early AIOps adopters typically start by applying machine learning to monitoring, operations and infrastructure data, before working ahead to deploy deep neural networks for their service offerings and desk automation. The success of AIOps lies in identifying the tactical and strategic use cases to evaluate the vendors and tools that fit these needs. Industry heads eco the opinion that phased approaches of AIOps work out to be most successful.
The first phase in AIOps is machine learning of structured data trends which form the entry point into embracing this technology. The secondary phase of implementing AIOps will comprise of neural networks, language-orientated data, and behavioural analysis around the automation of service desks.
AIOps and the Digital Business Transformation
Enterprises across the globe are leveraging the benefits of digital technology to transform their business offerings. These efforts provide better experiences to their prospects, customers, suppliers, and internal stakeholders. To embrace digital business transformation and succeed as digital companies, businesses need to rethink their entire IT and operational strategies. There is an urgent need to substantiate these efforts with business-first considerations which should include how organisations think about application and network uptime. There is an expense involved in per outage which organisations have to incur; market estimates point this figure to be on an average of $300,000 per outage if no revenue is at stake. If the outage impacts revenues then organizations lose an average of $72,000 per minute or lose a whopping of $5.6 million per outage.
AIOps and the IT Operations Management
The general processes by which AIOps platforms and solutions operate integrate to three steps:
The first process needs to observe the nature of data and its underlying behaviour, which involves information collection through data discovery. AIOps data discovery needs to support big data scale, which can address the volume of data from different sources and IT domains. Those sources may include a new container, virtualized environment elements, hybrid cloud or legacy infrastructure.
Whatever is the data or source, speed is the key to the successful implementation of the process. The data must be collected in near real-time for pattern detection. Information related to performance and related information is collected from hundreds of sources through an agent or an agentless model. Successful AIOps platforms bring together a combination of mechanisms to collect data from a multi-vendor and multi-domain environment, which may include an array of hypervisors, network and storage solutions, containers, public cloud, and other technologies and architectures. A successful AIOps platform additionally brings together the power of machine learning and big data with domain knowledge to identify a multitude of data relationships.
An AIOps platform assimilates orchestration across key IT operations domains, the IT Service Management (ITSM) being the key service area. ITSM activities such as incident management and change management have traditionally been manual and heavily dependent upon the Configuration Management Database (CMDBs) which are highly unreliable for environments involving frequent changes.
The AIOps platform uses analytics to make ITSM tasks reliable and more automated. For example, AIOps can update CMDBs with a basic knowledge of the environment, state, and changes through observing hybrid environments on an end-to-end basis; which ensures CMDB data is relevant and reliable. This process allows for faster automation and more accurate incident management which minimizes risks that might otherwise happen due to a human error.
Automation or closed loop function forms the core of an AIOps platform. Automating critical IT operations with the help of machine learning is a new territory for most organizations, making IT leadership going comfortable with it before they fully embrace automation. The new state-of-the-art automation using advanced human inputs and machine learning is showing a maturity, where organizations have begun to deploy automation in both simple and more complex jobs. Latest examples include employing automation to clean log files to free up space to use it to restart an application.
Delivering Value through AIOps
Enterprises that have deployed AIOps solutions have experienced transformational benefits including revenue growth, improved customer experience, better customer retention, and enhanced performance at lower costs.
Leveraging AIOps, operational teams have been able to achieve unprecedented heights, detailed as under:
End to End Application Assurance & Adaptability, which includes:
• Managing an integrated set of operational and business metrics
• Predicting and preventing outages to reduce the Mean Time to Detect and Meantime taken to Repair
• Lower the number of IT FTEs dedicated to troubleshooting and thereby decrease the operational noise and alerts
Reducing IT costs by Optimisation
• Replacing older, silo-focused IT monitoring tools to auto-discover complex, heterogeneous topologies
• Gaining visibility into the hybrid IT environment and accelerating migration to the hybrid cloud
• Expediting the adoption of micro-services architecture and hyper-convergence.
• Risk reduction by migrating and consolidating data centers
Resource redeployment for Innovation
• Reducing the cost of audits and compliance with Automation
• Simplifying IT processes by breaking down silos across IT teams
• Enabling resources to become more productive across departments.
AIOps represents the best of both worlds of an organisational structure it combines the ability of Machine Learning to handle enormous datasets on a live basis and develop a deeper understanding of the ultimate business goals of their organization and behaviour of their systems. The right AIOps solution provider is aware of the complexities associated with customer adoption and how to respond to them, keeping the balance between automation and manual oversight.