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Artificial Intelligence for IT Operations (AIOps)

Algorithmic IT Operations (AIOps) Platforms for Unique Business Insights

Algorithmic IT Operations (AIOps) Platforms for Unique Business Insights

Executive Summary

Modern IT infrastructures are typically very complex with combined physical, virtual and cloud components. Most of the IT organizations are using DevOps or Agile approaches to enable continuous development which adds, even more complexity. On top of that, the variety and volume of the data generated by the management and monitoring tools make it difficult to comprehend and take value add business decisions.

IT organizations become more proactive by applying sophisticated IT Operations Analytics to these massive quantities of IT operational data. ITOA has evolved from uncovering issues an hour or two before they affect operations, to providing predictive capabilities well into the future. The complexity of IT complexity and the demand for increased performance demands has led to the creation of Algorithmic IT Operations (AIOps) platform. AIOps, a term recently coined by Gartner, refer to solutions that use artificial intelligence and machine learning to automate tasks and processes that have traditionally required human intervention.

AIOps platforms analyze large volumes of IT telemetry from disparate sources and apply various forms of algorithms to the data in real time. By levering AIOps platforms, IT organizations can automate and enhance IT operational practices and access continuous insight into the performance of their business services.

Algorithms as Competitive Analytical Assets

The working principle of a Predictive Analytics model is that algorithms are applied to the disparate data sources to generate predictive models that allow you to make predictions or statements about the application or process. An algorithm is part of the overall analytics process. It is the processing step that allows you to take business-driven decisions that add value to the enterprises. They can leverage this information to make an application perform better, and drive a better customer experience.

Companies have recognized the importance of investing in algorithms to improve their position. Algorithms, powered by data, have enabled businesses to gain a competitive edge and act as a better way to bring about change in their business operations.

Unravelling Algorithmic IT Operations (AIOps) Platforms

Although Algorithmic IT Operations platform (AIOps) evolved from IT operations analytics (ITOA), the major differentiator is the way the algorithms are applied to the data sources. While ITOA involves monitoring and analyzing historical operational data collected from multiple sources, AIOps deals with real time data from various network conditions.

Using a variety of algorithms, AIOps allow you to visualize and analyze network conditions in real time, isolate problems and automatically address the issues. AIOps can analyze data, performance, and activity from disparate sources and provide continuous insight into both the business processes and IT operations.

Enterprises have noticed these capabilities and use cases and are adopting AIOps. Gartner estimate that about half of global enterprises will be actively using  AIOps by 2020, compared to less than 10 percent today.

The key components of an AIOps platform

Monitoring Ecosystem – It provides visibility and creates accessibility across the physical and virtual stack. These monitoring tools are crucial for maintaining high service quality, and performance.

Engagement System – Monitoring the ecosystem also generates extraneous noise. This component reduces noise and delivers service insights to the right people in real-time. It’s the first place that operations teams should look when something breaks. By applying Machine Learning capabilities and analytics, it enables early problem detection and pre-emptive measures to be applied.

System of Record – Manages service requests, trouble tickets and stores this information for future references. It can be improved based on the unknown relationships that are discovered over time.

System of Automation – This automatically runs resolution scripts and streamlines repetitive tasks from common incidents.

Data Lake – It acts as a repository for all the forensic diagnostics, ad-hoc reporting, and business dashboards.

AIOps uses artificial intelligence to enrich and group events and to archive incidents based on the nature of disruption. These data are used to determine the best possible solution. Organic machine learning capabilities feed all identified symptom and diagnoses into the artificial intelligence engine to enrich future use and reduce operational expenses. The time series database use analytics to extract meaningful data characteristics and statistics. Time series analysis also identifies outliers used to automatically create events, while time series forecasting, predict future values.

Leverage AIOps Platform Benefits

Greater visibility into the business, IT, network, and operations infrastructure.

Real-time analysis and diagnosis of the issues and provide automated solutions.

Alerts and notifications for potential problems or maintenance schedules, to prevent significant performance drain on the IT infrastructure.

Conduct causal analysis and apply analytics to a broad set of data to identify and compare probable root causes of issues.

 • Provide data-driven recommendations based upon both real-time and historical data to inform the decision-making process.

IT monitoring including automated behavior predictions.

Identify new opportunities and revenue streams by analyzing both IT and business data to determine the best business outcomes.
AIOps tools can also detect, diagnose, fix, and optimize events that make IT less efficient.

AIOps Platform–Next Generation Solution for IT Operation

IT telemetry and complexity will continue to increase at an exponential pace, while human capability will remain the same. Therefore, IT operations need to strategically leverage AIOps platforms to complete certain tasks. Both AIOps and ITOA vendors must drive technology to provide the best possible solutions to reduce workloads so that they can focus on what really matters – customer experience. They should address the inherent IT complexity and the high-performance demands of the industries.

Any number of software vendors offer IT operations analytics and AIOps products today help them diagnose and fix networking issues, which leads to to better response times and more efficient use of the IT Infrastructure resources.

Enterprises have noticed these capabilities and use cases and are adopting AIOps. Gartner estimates that about half of global enterprises will be actively using AIOps by 2020, compared to less than 10 percent today.