Exploring Data Actions

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You are a planner, working with your team to create a forecast income statement for your company. The business scenario that we use in this course when learning about data actions involves creating a detailed labor forecast.

What are data actions? A data action is a flexible planning tool for making structured changes to planning data in SAP Analytics Cloud. You can run them to carry out complex or repetitive operations, such as copying data, running scripted calculations, doing driver-based allocations, or publishing versions, more quickly and accurately.

Modelers design the data actions and planners run them in stories using starters or schedule them as tasks in the calendar. A data action is created based on a planning model and consists of one or more steps that are carried out on a public or private version.

Data actions can be executed in multiple ways:

• From a story using a starter.
• From an advanced story using scripting.
• From the SAP Analytics Cloud calendar as a task.
• From the SAP Analytics Cloud, Add-in for Microsoft Excel.

Data actions are used to process mass amounts of data. Some use cases for data actions include the following:

• Carry out planning tasks automatically.
• Copy by model and across models.
• Allocations.
• Currency translation.
• Calculations.

Some of the features of data actions allow you to:

• Sequence data action steps.
• Embed data actions (just like an include).
• Schedule or ad-hoc execution.
• Include prompts for flexibility.
• Integrate prompts and story filters.

Recently created and accessed data actions can be seen in the Data Actions application from the side navigation.

You can also access your data actions fromFiles and the Recent Files card of your home screen (you may have to add the card using Customization).

The following types of steps are available:

• The copy step copies data within a model based on a set of rules, filters, and aggregation settings. For example, you can use it to copy data from 2018 to 2019 or actual data to budget data. With the copy action, you copy a value from only one dimension member, but you can copy to multiple dimension members.
• The cross-model copy step allows you to copy data from a different source planning model based on a set of filters and automatic or manual mapping between dimension members.
• An allocation creates a new allocation or use an existing allocation step.
• A conversion step is used for models with measures. Conversion steps let you copy between measures while applying currency conversion.
• A fact deletion step allows you to delete facts, delete zeros from facts, or set facts to zero. However, your user role must have the Maintain privilege for models assigned to it.
• An embedded data action step runs another data action as part of the one you’re working on. Combining these steps with dynamic parameters lets you reuse data actions with different source or target members.
• Each step of an advanced formulas step allows you to create calculations to apply to the data. For example, you can use scripting to calculate opening and closing balances of headcount based on hires and terminations, looping through the data by time.

• Steps in a data action are executed sequentially.
• The result of one step is available for the subsequent steps.
• For each step, the execution scope is defined via filters.

Let's take a closer look at some of the setting options that are available to you when adding data actions to your stories.

1. Data Action: Select the data action to run. You will only be able to select a data action where you have Read permission granted.
2. Default strategy if target version isn't in edit mode: If the target version isn't in edit mode, you can choose how you want to define the planning area.
   • Empty Planning Area (No Version Data): The planning area will extend automatically as the data action runs.
   • Recommended Planning Area: The planning area is based on data locking, data access control, or both. If no recommended planning area is configured in model preferences, then this option will include all the version data. The planning area is an important tool for optimizing planning performance and can be configured in the model preferences dialog.

     For more information on customizing a planning area can be found in Configuring and Using a Planning Area lesson in the Optimizing Planning Performance in SAP Analytics Cloud course.

   • All Version Data: The target version will contain all the version data.
3. Publish to target version after execution: You can select the a desired behavior for publishing your changes. The options include:
   • Do not publish: Changes will not be published and the version stays in public edit mode. These changes can be published manually or published as part of another process.
   • Publish and fail if there are warnings: All of your changes will automatically be published. If any restrictions such as data locks apply to the data you’re trying to publish, then the publish will fail.
   • Publish and ignore warnings: All of your changes that are not affected by restrictions will automatically be published. If any restrictions such as data locks apply to some of the data you’re trying to publish, then the affected data will be discarded.
   Note

   • All of your unpublished changes to the target version will be published, even if they weren’t part of the data action.
   • This option only applies to public versions. If the data action runs on a private version or on a BPC write-back model, you’ll need to publish the data manually.
4. Open in Designer: Opens the data action selected for the starter in the designer so that you can review, maintain, or add steps to it.

Setting parameters results in a user receiving a prompt dialog when they run the data action in a story. Prompts are like variables, in that they make the data actions flexible and dynamic.

In the following example, when the Copy Hours data action is executed from a story, the parameters related to the data action prompt you for target, source, and year.

When you are in the story, you can customize the various parameter input and values for the data action starter from the builder panel.

Parameter Values are determined by:

• Member selection in the story.
• Default values in the data action parameter definition.
• A story filter.
• An input control.

The Parameters Type that you see in the builder panel in a story, such as Target Version, Source Version, and Year Month, as shown in the previous example, are created in the data action itself. They can be based on members or numbers:

• Selecting Member/Measure creates a parameter that represents a dimension member. For example, this type of parameter lets you change the year that you're copying data to or from.
• Selecting Number creates a parameter that has a numeric value. For example, this type of parameter is commonly added to advanced formulas, and can let you prompt users for values such as growth rate or gross margin percentage.

The Default Model assignment is the model that was selected during the creation of the data action. Most steps will use this model as the data source and target. However, for cross-model copy steps, this model will be the target, and you can pick a different source model. You can also read and copy data from different models in advanced formulas steps.

Using the calendar, you can schedule a data action. This feature is helpful with data actions that take a few minutes to run, as you can schedule it to run at night or over a weekend.

If you decide to use a data action task to schedule a data action, then any prompts must have assigned parameter values.

If you select + Add Recurrence when creating the task, a separate dialog allows you to configure the recurrence. Once the task is created, the recurrence can be edited for each task, or for the series.

Data action statuses in the calendar:

• Before the data action runs, its status will be Open.
• After it runs with no issues, the status will be Successful.
• If something goes wrong, the status will be Failed.

Data actions can be used in other data actions that belong to the same model.

For example, you create a data action that calculates subscription revenue based on booking data, with a dynamic parameter for the product type. Using embedded data action steps, you can reuse that data action multiple times in another data action and you can specify a different product type each time.

This lowers the cost of development and maintenance.

Here are some other features of embedded data actions:

• Parameters of outer data actions can be linked against required parameters of inner data actions.
• Parameters of inner data actions aren’t prompted on execution; each must be assigned a value in the designer.
• You can’t use a data action within itself.

When data actions are scheduled from the SAP Analytics Cloud calendar or executed from a story, you can use the Job Monitor - Data Actions to check the status.

The Job Monitor shows:

• When the data action was triggered.
• Who triggered the data action.
• Where the data action was triggered.
• Which values were used as parameters.
• Number of records and duration.
• The data action job history will remain based on a retention period of 30 days (default).