Power Platform Analytics for Microsoft Dynamics 365 CE Part 2 - Common Data Service Analytics Part 1

The Common Data Service Analytics tab shows all common data service call related statistics for further analysis. Using this, developers can get a better idea about the following areas.

  • Performance of plugins/APIs/workflows
  • Failure or pass rate of plugins/APIs/workflows
  • Entity usage of dynamics CE
  • User operations
  • User access modes
  • Mailbox usage

I will describe each component in the related section.

In the home section, you will be able to see a brief summary on active users, API calls, API pass rate and total executions. If you wish to see more details on these components, it is easily available in its relevant section.

However, you will first need to set the correct date range for analysis. This can be done by adjusting the filters including the environment filter. This will apply to all other tabs as well and will show historical data according to the filter settings.

Active Users Analytics

In this analytics group, you will be able to see how many active users are present in your system. To get more details on the graphs, I have filtered the data to a single day.

The first graph will illustrate how many users are logged in to the given environment. If you select a single day, it will display the data hourly or it will scale up based on the filters you have added (e.g. if you select a month it will show active users by day).

The following graph shows different operations performed by the user based on a time distribution. This graph also shows the exact operation count by time and operation. In essence, it helps get an idea about most performed operations by time.

The following graph will show the most active users based on operations performed by the user. This will help administrators to identify usage of CRM and which of the users are most active. In addition, it will help to get a better idea on which of the operations are mostly performed and to adjust the CRM performance based on operations. According to this example, we have a considerable amount of data reads in comparison to creates. We can optimize our read queries with “nolock” after some analysis on entities and data.

The total operations graph helps to identify peak hours in which the system executes a large number of operations. This will help administrators to modify resource allocation plans dynamically. If you adjust the filter criteria to reflect years, you will be able to observe which time periods generate the most traffic.

The total page requests graph shows how much page requests are generated by the system/users in forms/reports/dashboards. At the moment, the environment has not generated such a request. However, in live environments, you can expect data to be populated to this graph as well.

Gihan Lakmal

Tech Lead