Three Levels of AIOps
Use cases for AIOps
Tasks include performance monitoring, early fault and failure detection, and predictive maintenance to provide continuous fixes and improvements to IT systems such as networks, compute hardware, telecom towers, and supply chain systems. AIOps helps with a wide variety of use cases including resource capacity and usage forecasting, root cause analysis, energy usage optimization, performance diagnostics and remediation, predictive maintenance, and telco data analytics.
Telecommunications Network Analysis
Telecom data analytics requires integrating many different types of network interface data and contextual information. AIOps can fully optimize operator networks, improving the quality of service for higher customer satisfaction and reduced customer churn. AIOps can also help with capacity planning, and when combined with geospatial analytics is highly useful for tasks as diverse as automated call re-routing and new cell tower location planning.
See how Sysmech combined as many as 75 network interfaces to provide a self-analyzing network with 80 – 90% reduction in false alarms.
Smart Hardware / Network Optimization
AIOps helps compute hardware, network, and data center providers add a cutting-edge extra bit of optimal power to their products. By collecting sensor data from arrays, netflow data, traces, logs, etc., hardware providers can provide AI-driven predictive intelligence that makes sure hardware is always-on and always-fast. AIOps doesn’t just reduce MTTD (Mean Time to Detect) issues and MTTR (Mean Time to Repair), it can proactively detect potential problems. Armed with this information many simple problems can be automatically fixed before the customer ever knows there’s a problem. More complex issues are quickly resolved by technicians with the essential root cause analysis at their fingertips.
Energy Usage Optimization
Modern electric vehicles and smart buildings all seek to use energy as efficiently as possible. Whether your company makes the skyscrapers smarter, or you’re trying to make sure the electric car you manufacture will stay on the road a few more miles before needing a charge, you know that analysis of every sensor and every single data point can put you far ahead of the competition. AIOps can analyze energy usage, and optimize regeneration of power to improve designs and eke out every ounce of power.
Jaguar TCS Racing, for example, analyzes billions of data points every race, with as little as 0.5% of the battery power left at the end of each race that makes the difference between a trophy and a stalled car.
Predictive maintenance is one of the most popular applications of AIOps. Finding problems before they affect the robots that manufacture multi-million dollar chip sets, or the MRI and CT scanners that hold human lives in their hands, or the engine parts of a passenger airplane can mean life or death for a company, a person, or hundreds of people. AIOps helps companies approach zero downtime for all these essential systems.
Optimal+, now owned by National Instruments, manufactures the chips that make self-driving cars possible. AIOps monitors each robot in the manufacturing line with edge analytics designed to shut down the machine in milliseconds if a potential problem is detected, saving millions each time.
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