How can AI and data analytics help you extract business and application intelligence?

Slick dashboards and the occasional snapshot detail are no longer enough to pinpoint the often subtle and interdependent causes of application performance issues. Today’s IT performance investigator needs to be equipped with better and more complete diagnostic data, and with a variety of analytics tools to pivot and visualize the data from a number of different perspectives.

With AI and data analytics applied to high definition APM based on big data, you can quickly surface insights and proactively resolve issues before business is impacted. Intuitive queries, machine learning algorithms, and innovative visualizations, are a few ways that AI and data analytics can get you two clicks from an answer to virtually any performance question.

How does APM use AI and machine learning?

With AI and machine learning, next generation APM solutions can:

  • Leverage high definition metrics and complete diagnostics data for accurate analysis
  • Be proactive with automated anomaly detection that alerts you to unusual performance behavior before end user SLAs are breached
  • Surface unsuspected issues with pattern recognition based on performance “clusters” and “correlations” to identify groups of related transactions and metrics, and quickly find the needle in the haystack
Can APM deliver business-relevant insights?

Yes! With big data APM you can use simple queries applied to transaction records with full user and business payload detail, indexed across billions of transactions, to quickly generate business-relevant reports, compare historical performance, or drill down for further analysis.

Armed with this, DevOps teams can:

  • Prioritize the development efforts that will have the most overall business impact by identifying the backend components implicated in the most important transactions, by processing time, volume or financial value.
  • Enhance troubleshooting with fully integrated log analysis that allows you to easily search across application log messages, immediately see which methods generated the errors or exceptions, and identify every business transaction and user that was impacted.
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