IBM analytics in modern day IT operations
How IBM Watson AIOps and Turbonomic slash costs, boost ITOps performance.
AI and machine learning are dramatically improving IT operations, with IBM’s Watson AIOps and Turbonomic delivering measurable cost savings and efficiencies.
This is according to Donovan Marais, IT Service Management Architect at IBM South Africa, who says AI and machine learning in IT are no longer just a concept, but determine the future and everything that it holds.
“With the explosion in IT complexity and accelerating innovations, companies are finding it hard to keep pace with all of these changes. In addition, companies are confronted with managing the evolution to cloud and cloud native technologies with compute workloads being run on various platforms across technologies all over the world,” Marais says.
“The tricky balancing act faced when driving innovation while contending with traditional infrastructures has forced companies to look at more dynamic and diverse architectures that provide speed of delivery, efficiency as well as tremendous breakthroughs in the way business is done.”
Marais notes that IT operations teams are now required to deliver visibility into performance data and dependencies across environments that embrace the changing operations landscape, while alerting IT staff to the problems and their root causes, all the while recommending solutions to the aspects pertaining to the problems being seen, whether they are network, application or any other service driven.
With all this in mind IBM has brought to market IBM Watson AIOps and Turbonomic, which not only combine a set of capabilities such as predicting, communicating and resolving events before they happen, but also have the ability to manage all facets of the AIOps life cycle from model training to execution in traditional, cloud or hybrid environments.
Says Marais: “AI operations can reduce their cloud spend between 30%-50% and reduce cloud waste by 100%. Turbonomic will increase availability of business-critical applications by 20% and support innovation by increasing speed to market by 40%.”
He says the meticulous collection of information from data sources like logs, metrics and events, as well as topology and application information by Watson AIOps, helps CIOs and SREs alike by uncovering hidden insights and taking proactive steps to not only resolve but also pre-empt failures in the environment. While Watson AIOps and its functionality can be categorised into a set of capabilities like event management, incident diagnosis, incident resolution and insight delivery, at its core it has five major components, which are AI manager, event manager, metric manager, topology and application discovery.
For digital teams, Turbonomic provides application resource management that provides “cruise control” for all applications, anywhere. Turbonomic ensures all applications – both containerised and traditional – always get the resources they need to perform, while continuously maintaining compliance with business policies and constraints.
Insights can be delivered directly, in near real-time, into team collaboration tools such as Microsoft Teams and Slack, allowing group insights into the environment and the underlying problems that could occur.
Utilising algorithms, Watson AIOps allows the user the ability to cut through the noise generated by correlating operations data from multiple sources to identify root cause analytics and propose solutions faster and more accurately than is possible by human interaction, achieving faster mean time to resolution (MTTR).
Marais says: “Due to Watson AIOps’ ability to never stop learning, it keeps getting better at identifying less urgent alerts or signals that correlate with more urgent situations, providing teams with the ability to address potential problems before they can lead to slow-down or complete failures.
Being bombarded with an array of alerts from every environment means a team’s focus is spread across various systems. But with IBM Watson AIOps, operations teams only receive specific alerts pertaining to specific service level thresholds or parameters, complete with all the context required to make the best possible diagnosis and take the fastest corrective action, using natural language processing from unstructured sources like logs, tickets and chats, allowing the ability to extract the most informative texts to enrich predictive alerts gained from analysing your operational data.
“Therefore, IBM is able to help teams to 'keep the lights on' with less human effort, greater efficiency and accuracy, and allow them to focus on tasks that provide greater strategic value to their business and industry,” he says.
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