Multi Actions Chain – Clean – Import – Predict – Publish – Data Locking
SAP Analytics Cloud (SAC) Multi Actions has gained a lot of attention recently because of its ability to orchestrate multiple Data, Predictive and Planning operations. Plus, it helps end user to save time when there are multiple planning model and versions are involved. In this blog, lets see how to run multiple planning operations using Multi Actions.
Multi Action History
SAC introduced Multi Actions in the release of 2021.Q3, then integrated Predictive Scenario into Multi Actions in the release of 2022.Q1. In the release of 2022.Q3, Post that SAC released Integrating Data Import Job into Multi Actions Now, in 2023.Q2, SAC has introduced Data Locking into Multi Actions.
In this blog, you will learn to trigger Clean(Data Actions), Import(Data Loading), Predict(Predictive Scenario), Publish(Version Management) and Data locking at single click using Multi Actions
Preparation before Multi Actions
Firstly, you need to create a Data Action which will delete model data or perform any desired action on the model.
Secondly, create data import job in Data Management of a model. I assume you are familiar with the creation of data import job. If no, refer to Import Data to you Model.
Third, for Data Locking step, make sure Data Locking toggle is on for Model under Model Preferences > Access and Privacy
Lastly, you need to create a predictive scenario. If you are not familiar with predictive scenario, refer to Predictive Scenario
Creating Multi Actions Chain
1#Create Multi Actions either from the Side Menu bar
Or create it from File Repository
Multi Actions Option 2
2#Clean Model data using Data Action step
Add a Data Action step which will delete all Forecast version data from the selected model
3#Import Data to Model using Data Import Step
Add Data Import steps to import Master and Model Data.
4#Add Predictive and Version Management Step
For Predictive step, please refer to blog. Here, I will predict Net Income for 2022 and 2023 till Q2
Add Data Locking step to lock data for previous years i.e. 2020, 2021 and 2022
You can also add API Step to integrate Remote Application. Please refer blog to add API step
Your Multi Actions chain is ready now!!
To test this Multi Actions , create a story and add a table with required columns and rows. Add Multi Action trigger. According to the steps , first it will clear data for Forecast Version, then import master and model data from already setup data import jobs, followed by prediction scenario to predict few months ahead and version management to publish changes and at the end locking previous years with Data locking step.
Before Multi Actions Execution:
After Multi Actions Execution:
Multi actions help you orchestrate a set of data, planning and predictive operations across multiple planning models and versions in SAP Analytics Cloud. They link together a sequence of steps such as data actions, data import, version management steps, predictive steps data locking and even API integration, which all run from a single planning trigger.
See the list of existing Multi Action blogs
Thanks for the read! Hope you find this blog helpful. Please share your thoughts and questions via comments. I request you to follow for future interesting blogs on SAP Analytics Cloud
Thank you for sharing this informative post about multi-actions, data locking, and other features in SAP Analytics Cloud. I found the explanations and examples very helpful in understanding how to use these features effectively.
As someone who frequently works with data in SAP Analytics Cloud, I appreciate the tips and best practices you've provided for using multi-actions to chain together multiple steps and automate data processing. The section on data locking was also particularly useful, as it clarified some of the common scenarios in which data locking can be useful.
Overall, this post has given me a better understanding of how to use some of the more advanced features in SAP Analytics Cloud. Thank you for taking the time to create such a valuable resource and sharing it with us.
Thank you Poorna!
I am happy to see this blog was considered helpful.