Automation Analytics: UX enhancements

March 9, 2020 by Sean Cavanaugh

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Red Hat Ansible Automation Platform now includes hosted service offerings, one of which is Automation Analytics.  This application provides a visual dashboard, health notifications and organization statistics for your Ansible Automation.  If you are new to Automation Analytics and want more background information please refer to my previous blog Getting Started with Automation Analytics.

 

Enhanced Filtering 

On the Automation Analytics dashboard there is the ability to filter by Red Hat Ansible Tower cluster and by date.  Red Hat recommends having a cluster local to where the automation is happening, for example if you had a data center in Japan, Germany and the United States you would most likely have an Ansible Tower Cluster in each geographic region.  This allows users to perform drill down filtering to individual clusters to get data relevant for a specific physical site or group. This is helpful if, for example, a company's team in Japan only cared about data relevant to them in the drop down menu.

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Previously, this only affected the top graph, but not sub cards such as Top Templates or Top Modules.  In the following video you can now see that all cards are updated simultaneously.

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Now, drill down filtering for the individual cluster level also filters Job Templates and top modules that are being used as well as only showing notifications unique to that cluster.  This is more intuitive to the user and also works on the Organizational Statistics page.  

By looking at top module uses you can quickly identify the use of non-approved, non-ideal modules such as the shell or command modules.  This might warrant an auditing to make sure folks are on the right track with their automation. This information can also quickly identify high utilization of automation that is unique for a specific vendor, like Cisco, when it was unknown at the business level this was a use-case.  

 

Additional Job Stats

Clicking on a Job Template now pulls up additional information.  The number of total runs, total time, average time, success rate (if any task fails, the Job Template counts as a failure) and the most failed task (listed by name) and the failure percentage of that task.  You can quickly scan Job Templates and concentrate efforts on fixing Job Templates with low success rates that are highly utilized.

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Organizational Statistics Improvements 

Within organizational statistics we have added additional filters to help compare automation adoption between organizations.  Organizations between multiple Ansible Tower clusters are aggregated when they share the same name. You can now filter by the top and bottom five organizations based on total job template use.

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The bottom two cards, Job Runs by Organizations and Usage by Organization (Tasks), will also now reflect the filters that the top graph has chosen, rather than filtering by individual cards.  This makes a more clear and intuitive experience for users so that all information being presented is for the same filters and time period.

 

Where to go next?

I hope that this blog illustrated how we are continually adding enhancements and listening to customer feedback.  We want to make sure Automation Analytics will help our customers gain additional information and data on their automation usage and help them measure success.  Here is some additional information that can help you on your journey:

Check out previous blogs on Automation Analytics 

Outside of the blog please check out the following:

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Topics:
Ansible Automation Platform, Automation Analytics


 

Sean Cavanaugh

Sean is a Principal Product Marketing Manager, Ansible, where he brings over 10 years of experience building and automating computer networks. Sean previously worked for both Cumulus Networks and Cisco Systems where he helped customers deploy, manage and automate their network infrastructure. He resides in Chapel Hill, NC with his wife and children and tweets from @IPvSean.


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