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Product and Topic Expert
Product and Topic Expert

Dear customers and partners,

When encountering performances issues,

what can you do before contacting the support of SAP or your Customer Success Partner?

Start by checking your local PC resources, your network speed, number of Chrome extensions and set power mode on battery on “best performance” for more detailed information see System Requirements and Technical Prerequisites - SAP Help Portal

During the User Acceptance Tests process, use the role configured with most accesses and not the Administrator role that isn't relevant to your planning data collection process

Once you're in the optimal conditions for your tests, assess what is slow, describe items perceived as slow, current time and expected time

This comprehensive KBA will help you 2511489 - Troubleshooting performance issues in SAP Analytics Cloud (Collective KBA)

For more "developer" profiles, open Google Chrome Developer Tools and analyze what is slow:

✓ Query data at backed
✓ Calculation on the fly
✓ Render and display data


Once you have localized the areas where you need performances improvements,

please find below some examples of features of SAP Analytics Cloud Planning that you could leverage in a short term period.


Implement security in the model and its dimensions (thus reducing the Private version "playground" of the end user)


    • Configure the Data Access Control in dimensions having the largest amount of dimension members


For more detail about the configuration, please watch this video Implement security in the model and its dimensions

    • Optionally, decrease the number of driving dimensions in the Data Locks option of the model


Readapt "performances costly" data actions

    • Check security in user’s role (note that the data action triggers only the entities the user is entitled to)


    • Check the scope of the calculations and reduced it if needed


    • Use member set selection



    • Use parameters of the data action to narrow the scope of the execution especially during a cross model copy process


    • Use function “Aggregate_write to” when cross copying from one model to another


    • Check the amount of data to be published in the details of the version in the story



Review your data collection stories 


    • Define a Summary page and Use hyperlinks to go to detailed information (more levels if needed)


    • Keep the fluid data entry mode, activated by default.


    • Activate the recommended Planning Area (check the option Auto-Generate based on the table context)


    • Filter as much as you can, implement filters in documents rather than creating generic documents without selection possibilities in dimensions as drill-down to all details can be expensive


    • Take care at drill state – filter as many dimensions as possible


    • Limit the number of charts per page – max 6-8 parallel queries can be run by browser (depending by version)


    • Don't create tables based on very large combination of unbooked data of several dimensions, filter 


    • Split big tables in multiple tables with fewer accounts


    • Propose to the end users to try “Add member” instead of configuring tables with “Unbooked data”


    • Less is more – request only what you need


    • Limit the numbers of descriptive columns into the table – easy to be read


    • Avoid formatting rules for tables with large datasets


    • Reduce as much as possible the cell references


    • Use Restricted measures instead of Conditional Aggregations in the model


Review your reporting stories 

    • Filter first (avoid loading all and filter after)


    • Split the information in more level of details:

        • Define a Summary page with global indicators – use conditional formatting to highlight a wrong result

        • Use hyperlinks to go to detailed information (more levels if needed)

        • Limit the number of charts per page – max 6-8 parallel queries can be run by browser (depending by version)


    • Limit as much as possible the linked data sources


    • Use page filters instead of individual filters per chart


    • Use cascading effect in case of multiple filters or input controls


    • Enable “unbooked members” only if needed


    • Rebuild the story in using “lazy loading” approach (tables one after another)


    • If your performances issues are related to the front end of SAP Analytics Cloud, try out the Optimized Design Experience more details in this blog and please be aware of the current limitations Optimized View Mode

On a mid-term basis, you may also reconsider the configuration of the Planning model with the following recommendations


Size of the model and its dimensions combinations

Often customers and partners mix up the concept of dimensions and properties

Check if

✓ all dimensions are required to define the collected information, here an illustrated exemple with the gender in this blog about HR Planning SAP Analytics Cloud Planning and SuccessFactor – Building a robust Planning Model – Some best practi...

✓ some dimensions can be transformed into properties of other dimensions

✓  try to extrapolate the number of records of your model based on the existing Actual data in the current sources and check the existing limitations of SAP Analytics Cloud System Sizing, Tuning, and Limits

Model calculations

✓ Try to find the right balance between on the fly calculated measures in the model and calculation done by data actions
✓ Use hierarchies instead of calculations
✓ In the classic model avoid accounts formulas, lookup formulas, exception on aggregation, try to initialize data with Data management import
✓ Replace Conditional aggregations with Restricted measures in stories


Useful link to troubleshoot performances issues Optimize System Performance with the Analysis Tool | SAP Help Portal