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Many challenges performance analysts face in trying to monitor their z/OS Systems infrastructure come from using outdated methods, multiple tools, or solutions that are reactive rather than proactive.

IntelliMagic Vision can export data into a Splunk-ready format, after which it can be imported into Splunk. This means not much data is ingested into Splunk, which can be a significant cost-saver.

Processors are expensive, and that is why it is crucial to have an accurate understanding of how much capacity you are using and have left before you run out.

By being able to compare any chart or dashboard with any previous time interval, performance analysts can quickly determine whether a certain workload peak is normal or unusual.

Machine intelligence designed correctly has a deep understanding of the system and can do all the computing, analyzing, normalizing, and prioritizing automatically.

Machine intelligence can quickly determine and show when metrics are out of the expected range and quickly identify service level violations.

Statistical approaches help save CPU time and MSU’s by being able to quickly see when something new comes online or when new application versions are less efficient.

White box analysis uses built-in expert knowledge about z/OS metrics and the z/OS infrastructure to detect absolute issue violations and derive true intelligence for predictive and prescriptive insights.