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Performance Statistics and Analysis

My previous blog post on “SAP Analytics Cloud Backend Runtime Analysis and Statistics” introduced the Model SAC_STATISTICS_MDS_QUERY_PERF and the Story Backend Application Performance.

2020.Q4 QRC and Wave 2020.17 rename the Story in the SAC Content to Performance Statistics and Analysis. The Model SAC_USER_FRIENDLY_PERF_ACTION has been added to the Story and provides new information on Log On Numbers and Durations as well as Story Load Times.

The Story helps you now not only to answer Backend, it also lightens the dark of some SAC Frontend related Information. And this again in a sample Story that can be adjusted to your needs.

The Overview Page has been modified to show you KPIs on Number of Logons, Average Logon Duration, and the Average Story Load Times. In addition we sum up the Top 5 Users by Logon Time and the Top 5 Stories by Load Time.

Besides the Timeframe you are interested in, you will be able to filter for a specific User or Story.

We help you to get an idea of the Usage, Adoption and Performance of specific Stories.

The Frontend Statistics Page contains two charts. The first chart shows the Number of Logon in contrast to the Average Logon Duration whereas the second chart provides a history on Story Load Times. This can be helpful to identify a specific date or event since the performance increased or decreased.

The Frontend Analysis Page contains the same filter possibilities as the Frontend Statistics Page and provides two chart and table combinations. The first one highlights the 10 Users with the highest Total Logon Duration and highest Average Logon Duration. This helps to identify whether there are single problems or whether the whole user community suffers. The chart provides also a history on Logon Times to identify events or dates that led to an improvement or a degradation. The table on the left to the chart is linked with it to get information only for a specific user and its history.

The second Table and Chart combination provides the same information for the Top 10 Stories by Highest Average Open Duration.

Some customers have already been testing this Model and Story, as a Proof of Concept. Now we are happy to release it to the full customer community! Stay tuned for further enhancements and updates.

 

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