Enterprise Performance Strategies Announces Pivotor’s Outlier Detection and Reporting
- dyost256
- Apr 22, 2024
- 2 min read
Today Enterprise Performance Strategies announced Pivotor’s newest feature: Outlier Detection and Reporting. Ever wish you could look across dozens of metrics to see which measurements were unusual during a problem time? Do you wonder if there might be batch jobs or address spaces that have started to consume more CPU or execute more I/O or just run longer? Can you tell if any important system metrics changed? Do you wish you could find such anomalies faster and easier?
Pivotor's Outlier Detection and Reporting harnesses machine learning to flag unusual data while avoiding many of the pitfalls exhibited by traditional anomaly detection techniques. Whereas traditional techniques often required careful setup and tuning, Pivotor’s ML-based process requires no arduous configuration and automatically scales hundreds of metrics, thousands of entities, and millions of data points. This makes it uniquely well-suited to analyze the vast trove of mainframe performance details recorded in the SMF data.
Finding anomalous measurements is important, but the real value is in making those findings available to the z/OS performance analyst in a useful and easy-to-use format. The outliers are surfaced in a familiar Pivotor reportset which includes new summary views that help the analyst find outliers relevant to specific incident times and areas. Analysts can also triage and explore outliers so that small problems can be found before they become large ones. Investigation is facilitated by showing outliers in context of the metric’s recent values over the past few weeks.
Now in its 20th year, Pivotor helps businesses understand and optimize the performance of their z/OS systems. Pivotor’s easy-to-use browser-based interface lets technicians focus on performance analysis instead of the mechanics of finding and reporting on complicated SMF data with arcane tools and languages. Mainframe managers appreciate Pivotor’s fair and simple pricing structure.
Visit https://www.pivotor.com/cursoryReview.html for more information and to sign up for a free cursory review with your data.
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The ML-based approach to flagging outliers without arduous tuning could save hours of manual analysis. I wonder if a similar automated detection method could be applied to audio processing, like using an ai music extender to identify and extend segments that feel incomplete. That would be a creative crossover between enterprise performance monitoring and music production.
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