AIMS Analytics allow you to analyse the vast amount of live and historical data captured by AIMS




The AIMS Analytics is a canvas for analysis of the vast amount of data captured by AIMS

AIMS Analytics includes default report templates available for all users and a flexible set of analytics modules that allow flexible analytics of all data captured - for technical or business purposes - for live data and historical data - and across on premise and cloud infrastructure, applications and services.


Reports created in Analytics can be saved as private or public dashboards and also distributed at set frequencies to relevant stakeholders to empower insight into how IT performs.


Learn how it works below

Flexible charts with live and historical data

The chart module is very flexible and allows users to chart all data captured by AIMS in different time resolutions, with several metrics, with normal behavior patterns, as line, bar, area or a combination.  Rewind to historical data to analyze past issues or troubleshoot in real time with streaming data that shows current behavior.

Auto-detection show changes to code and deployments

Auto-detection is a key part of the machine learning power of AIMS.  Detecting new code, new databases, new web sites or new azure resources automatically not only ensure that you automatically enroll in monitoring but also allow you an audit trace of what is happening.

Unhealthy components

The unhealthy components module allow you to see all components with an unhealthy status.  In contrast to the systems overview in the dashboard unhealthy components shows which are associated with anomalies, errors or are stopped.  

Cake diagram

The cake diagram module allows you to present data in the cake layout.  Select any of the data collected by AIMS to present live, trailing periods or custom time periods.  The cake diagram is mostly used to understand percentage relationships in data for example where synchronous transactions are expected such as card payments and processing as illustrated in the image to the right.

Top error changes

The Top Error changes module is useful to analyze event log errors collected by AIMS.  A key capability of the module is to identify similar events and calculate the count which allows easy insight into changes in error count by type over different time periods but also to identify new unique errors which could be of importance.



The Analytics modules described above represent a selection of the modules available. 

To test out the Analytics capabilities sign up for the free Community Edition below and test it out with your own data.