Early adoptions for AIOps: Obtain Holistic View and Predictive Analytics

There’s strong interest in applying AIOps, which stands for Artificial Intelligence for IT Operations, as the forces of digital business transformation weigh in on large corporations. Today, many IT professionals believe that AIOps will lead to several strategic benefits. These include increased efficiency, faster remediation, reduced operational complexity, and importantly, improved user experience.

AIOps is the application of machine learning algorithms, data science, analytics and big data. And when these technologies are implemented well together, the promise is that they establish proactive, automated remediation capabilities that help companies deliver superior customer experiences, while offering fundamental breakthroughs in scale and efficiency.

Our recent interviews with several IT professionals and business leaders at large organizations reveal the motivations behind why companies are preparing to adopt AIOps.

Missing Holistic View from many, domain-based monitoring tools
Today companies have deployed many cloud, distributed and hybrid applications which have introduced more complexity and monitoring challenges. Monitoring tools are domain specific and disparate, which makes it extremely difficult for organizations to obtain end-to-end visibility across the entire business operation. It has become practically impossible to solve complex and emerging problems before they impact end-user experiences.

Collecting data is the first step in enabling AIOps, and this data must be collected and correlated from disparate sources in order to be effectively analyzed. Optimal customer experiences can be attained with a holistic view of these end-to-end insights across the entire application system, from back-end infrastructure to customer behavior and performance.

IT professionals say the reason is that it is painstakingly difficult and time-consuming to manually correlate and analyze multiple application performance metrics. They are unable to correlate the tens of thousands of alerts from the monitoring tools they use. They have difficulty discovering the bigger problems that exist and knowing what to prioritize, so they tend to be reactive to customer complaints. As a result, they have great difficulty collaborating across teams to address problems efficiently and proactively.

A holistic view of problem discovery and recommended remediations is missing when monitoring tools today are limited to providing insight into issues within their own domain. With the enormous amounts of monthly alerts that need to be manually analyzed, coupled with declining manpower resources, companies are recognizing that the use of AI and machine learning is becoming a necessity. IT professionals agree that AIOps will help correlate the disparate pieces of performance metrics, alerts and information from these monitoring tools. AIOps will better uncover primary problems and make it faster and easier to get to the root cause.

Predictive Analytics is considered the most important AIOps capability today
In the digital economy, companies realize that poor user experience from their products and services is just a click away from losing their customers. Increasingly, businesses are making it a company-wide imperative to ensure exceptional customer experiences. Business leaders say that predicting user experience *before* they create end-user problems is paramount in ensuring their business objectives can be met.

Predictive analytics is the capability in AIOps to predict probable future events and problems that may impact availability and performance of the businesses’ products and services. Predictive analytics helps to automate discoveries of problems and provides teams to proactively remediate those before they negatively impact their customers. Predictive analytics go beyond what IT professionals can do and makes the case for adopting AIOps.   

The role of IT operations has become more challenging due to the demands of the digital economy and the increasing complexity of modern cloud and hybrid application architectures. AI and machine learning have emerged as the means to relieve the time-consuming and manual interventions that hurt businesses today.

At Omny IQ our focus is to help enterprises “reinvent” their connected products and services with a state-of-the-art, AI-driven connected experience platform and tools that enable our customers’ AIOps transformations.  The Omny IQ Preventive Care(TM) application for end-user connected experiences enables a holistic view, predictive analytics and problem discoveries using Statistical Relational Learning (SRL) techniques for mobile and broadband internet operators, connected device companies and edge IOT services. Something as conceptually simple as making a quick actionable decision on whether to push a fix to a segment of end-users’ products or services is painstakingly complex to uncover and time consuming to execute today. With effective machine learning tools to correlate and analyze data and obtain a holistic and predictive view of potential problems, companies get the time and efficiency to respond and resolve the larger issues before they impact their customers.
​
We are happy to discuss your needs to start assessing and implementing an AIOps solution that continuously drives superior customer experiences for your connected products and services.

Vivek Pathela - chief instigator at Omny IQ

Categories

All
AI Machine Learning
Market Dynamics
Product
Wi-Fi Analytics

RSS Feed