IntelliMagic automatically applies embedded expert knowledge to analyze IT operational measurement data. It relates knowledge on physical hardware capabilities to actual workload measurements, rates all metrics based on the context, and identifies exceptions across the IT datacenter infrastructure. The result is applied Artificial Intelligence.
Fundamental Understanding
IntelliMagic software has a built-in fundamental understanding of how workloads, logical concepts and physical hardware interact. By embedding human expertise into software to create artifical intelligence, the full potential of the data is unlocked. This modernized, intelligent interpretation:
- detects risks before issues impact production,
- uncovers true root causes,
- identifies optimization opportunities,
- enables IT staff to deliver a higher level of application service reliability at optimal cost.
Artificial Intelligence helps Human Decision Making
IntelliMagic helps IT teams make better decisions. Many enterprises still rely on a decades old process to analyze IT operations data, while a modernized way is already available.
| Decision Stage | Classic Reporting | Modernized Process using A.I. |
| Problem Identification | Mostly Reactive: Detect issues when fully developed, Firefighting |
Proactive and Strategic: Analyze environment to predict upcoming problems before applications are impacted |
| Collect Information | Slow and Incomplete: Static reports, custom programming |
Fast, Deep, Interconnected: Dynamically generated intelligence, measurements are interpreted and rated, Root cause detection |
| Design and Choose Solutions |
Harder and Slower: Manual work, Error-prone |
Safer, Faster, using applied Artificial Intelligence: Dependable assessments, accurate recommendations |
Artificial Intelligence for IT Operations Analytics (AIOps)
In recent years, a new class of products initially called IT Operations Analytics (ITOA) have come on the market with the design objective of providing a single interface into all the data generated from disparate devices, and more importantly, helping interpret what it really means for performance, availability, and efficiency.
The idea is to employ the computer to do more of the work of deriving meaningful intelligence out of all the data. If designed correctly, this is a type of artificial intelligence which is done by the machine and enables human IT operations teams to be more effective. In 2017 Gartner coined the term Artificial Intelligence for IT Operations Analytics (AIOps) which is a nice nomenclature for the capability.
Related Resources
When to Use Machine Learning for RMF/SMF Performance Analysis
This webinar is designed to help you evaluate various Artificial Intelligence-based design approaches to enable the machine to derive better RMF/SMF performance and availability intelligence on behalf of the human analysts.
Seismographs and Storage Performance
Just like a seismograph, the right change detection algorithms can automatically show you when your performance deviates from normal, enabling you to address the changes before they become an issue.
Is this z/OS workload change normal?
Change Detection allows analysts to automatically detect workload and application changes, saving them countless hours of manual labor and intensive scrutiny while trying to determine the significance of changes