Review der File Upload Funktionalität aus einer Story in der SAC

Introduction

Since the Q3 2024 release of SAP Analytics Cloud (SAC), Planning users can upload flat files directly from a story using a built-in story trigger. This feature allows business users to upload data in bulk, right from the story interface, without needing to go through the modeler. It streamlines the data update process and significantly reduces the dependency on administrators for routine data imports.

This blog will explain how to set up the data upload function in SAC, how to work with it in the SAC story and where we found room for improvement.

This is where you can find additional blogs that cover this topic:

Plan Data Upload Starter – Directly upload plan da… – SAP Community
New End User File Upload in SAP Analytics Cloud QR… – SAP Community
How to configure a dynamic file upload in SAP Anal… – SAP Community

Setting up the upload

In the first step, the administrator or modeler must create an Upload Job under the Data Management Tab. There you need to upload a template file (.csv/.xlsx and .txt are supported) where the modeler can create necessary transformations. As a last step, the columns from the template file need to be mapped to model dimensions and measures. You finalize the creation of an upload job by defining the Import Method (Update or Append) and the Reverse Sign by Account Type (Off or On) in the job settings:

Figure 1: Job settings for import job

Since release Q3/2025 you can set up if and from which hierarchy the system should make sure only leaf members are updated during the upload.

Figure 2: Job setting for import job - validation settings

In the Data Management view you find in the timeline each upload executed later by the planner with the option to download again the error report for the rejected rows.

Figure 3: New option for upload jobs definition in the data management tab with the protocol of uploads in the timeline

Once the Upload Job is created, the story designer needs to add the Upload Job Button to the Story. The button functions much like a data action widget, offering consistent user experience. When configuring the story, designers specify the model, choose the upload job that was created earlier, and apply default settings such as the target version or whether the data should be published automatically. These configurations can be either fixed or exposed as prompts, allowing end users some control during the upload process. If you use planning areas for big data volume you can finetune handling during the upload process. Once everything is set up, the story is ready to be used for uploads.

Figure 4: Settings within data upload starter

Upload Process

From the end user's perspective, the process is simple. When they open the story, they’ll see a button for uploading data, such as "Sales Plan Upload".
Clicking this button opens a prompt where they can select the source file and the target version, if applicable. Supported file formats include CSV, XLSX and TXT.

As previously mentioned, the modeler can choose either 'Update' or 'Append' as the import method when uploading data. Let's now examine how each of these methods behaves in the system during the data upload process.

Assume we have a file with actual data containing values as shown after an upload with ‘Update’ setting in the following picture:

Figure 5: Upload with update containing all shown data

If you repeat the upload with the update mode, values will always remain in SAC as in the file, values are updated.

If you use the append mode, data will be added, thus second upload of the same file will double the values.

What will happen if the file does not contain data for e.g. product A001 in an update step? Data from the file will be marked as updated, values for A001 remain unchanged as shown in the next picture:

Figure 6: Updated values after upload of a file without A001 and NON_PRD_SRV data

With the update mode you expect data to be replaced. Attention with missing lines, they will be untouched.

Now it is up to the user or designer, how to handle missing lines in a second ‘Update’ upload. If the user expects all data to be replaced, you must manually delete data. If you want to use the script function beforehand to handle the cancel option during the upload process (see below). SAP announced to improve this topic with the Q4/2025 update!

After each upload, you get notified if the data upload was successful or not:

Figure 7: Message after successful upload

Figure 8: Message after upload with rejected rows

If the upload is only partially successful, a rejection summary can be downloaded as a CSV file. This file provides an overview of the rejected rows along with the reasons for their rejection. Common causes include authorization issues, missing or incorrect master data, data locks, or validation rules defined in the system. In such cases, the planner must identify and correct the errors, then re-upload the data through the SAC story interface.

The rejection summary is a useful tool for gaining an initial overview of which lines were rejected and why. However, in practice, it can be quite time-consuming to pinpoint exactly which data caused the issue. While the summary indicates which rows contain errors, it does not specify which master data entries were incorrect, or whether the problem lies in a single dimension or across multiple dimensions.

For example, if you upload a dataset with 10 dimensions and 200 rows, and half of it is rejected due to master data issues, identifying the specific dimensions causing the errors can be a laborious task. More detailed insights into the rejected data would significantly improve usability. Benutzerfreundlichkeit deutlich verbessern.

Script options

For the data upload job, users can leverage scripting through onBeforeExecute and onAfterExecute hooks. The onBeforeExecute script is triggered before the upload popup is shown, while the onAfterExecute script runs after the upload job is completed.

As a designer, it's important to note that these scripts are executed even if the upload was canceled by the user, was only partially successful or completely failed. To ensure that a data action is executed only after a successful upload, the following line of code can be used within the onAfterExecute script (or on the other status Warning, Error respectively):

if (status === DataUploadExecutionResponseStatus.Success ) {

…;

}

This status is not known in the script onBeforeExecute! Pay attention to revert changes done in the onBeforeExecute if the user canceled the upload or warnings were issued with lines rejected.

Unfortunately, the rejection information is not accessible within the script.

Recent improvements - Release Q3/2025

Data Upload: control visibility to rejected records in end-user data upload and data load jobs

Users cannot download rejected records for data they are not allowed to see and not nodes.

Plan Entry: enforce loading to leaf members during data file upload

Make sure, only leaf members of an attribute can be changed during a file upload.

Better error handling

Error handling is cumbersome, possibility to edit wrong lines in a popup would be nice: Popup-Fenster korrigieren zu können:

https://influence.sap.com/sap/ino/#/idea/221946

Example file structure

Users must know the file structure for the upload. You must share with them an example file, e.g. by adding a link to tour story. SAC does not provide the option to generate, and example file out of the definition of the upload job.

No master data upload

In the current solution only transaction data can be changed during the upload. Masterdata must be maintained upfront in a different process. For the moment only option to add to a story you must implement adding master data via script functionality. SAP acknowledge that they want to add this function at a later stage in blog.

New End User File Upload in SAP Analytics Cloud QRC3 2024

Only fix columns possible in the file

If you want to upload data, for example for a rolling forecast, the plan and upload columns will represent planning quarters. However, these quarters shift over time. In the current solution, column headers can only be static.

This is explained in blog with a workaround:

How to configure a dynamic file upload in SAP Analytics Cloud

Besides the option explained in this blog, you could work as well with an intermediate data model, containing e.g. four planning quarters and copy in the script after execution from the intermediate data model to the final model with the correct periods.

Improvement request:

Data Upload Starter with updating Local Dimension Member Option

Import Job – pivoting on Date dimension doesn’t allow dynamic values

Deleting data during update upload

Better handling of deleting data missing in the upload file during update as already mentioned:

Data Upload Starter Clean/Replace and Clean/Replace Subset not available

This is planned for Q4/2025:

Plan Entry: clean and replace for data file upload

Conclusion

The upload activity is fully traceable, providing transparency and accountability for all data changes made by end users. Administrators can view the history of data uploads within the Data Management tab, while the Activity Log offers detailed tracking of changes introduced through the upload process. Right now, it’s not possible to track the specific cell that caused the issue. This would help users identify the source of rejections more easily and improve the overall troubleshooting process.

Finally, the rejection summary does not allow for direct data corrections. Users must manually update the data in a new file and re-upload it through the SAC story interface.

All in all, the new built-in story trigger for flat file uploads in SAC improves the planning workflow by enabling planners to upload data directly from stories without relying on modelers or administrators. It streamlines the process, supports flexible configuration, and ensures traceability through upload history and activity logs. However, the rejection summary lacks detailed insights into master data errors, making troubleshooting time-consuming for complex datasets.

Created in August 2025