About Augmenting Your Data

Augment your reports by choosing specific columns from various data stores (Business Intelligence view objects) of the Oracle Applications Cloud data sources.

You can augment your reports with datasets created by extending an existing entity or group of facts, by adding a new dimension in the target instance, and by adding a new fact in the target instance.

You can select the columns from various data stores, create an augmentation dataset, and use that dataset to create data pipelines for functional areas. This enables you to seamlessly extract and load data from additional Oracle Applications Cloud data stores and make it readily available in tables populated in the autonomous data warehouse. You can then use the data for visualization and analysis. To find the data stores that are available for extraction using augmentation, see Data Stores.

You can request for additional data stores to be made available for data augmentation. Create a service request for additional data stores for data augmentation and include the name of your tenancy along with the list of all the required data stores in the Description field.

Augment Your Data

You can supplement the data in your reports by using datasets that you create with specific columns from various data stores (Business Intelligence view objects) of the Oracle Applications Cloud data sources.

1. Sign in to your service.
2. In Oracle Fusion Analytics Warehouse, open the Navigator menu, click Console, and then click Data Configuration under Application Administration.
   You see the Data Configuration page.
3. On the Data Configuration page, under Global Configurations, click Data Augmentation.
4. On the Data Augmentation page, click Create.
   You see the data augmentation wizard with multiple steps. You can create the following augmentation types:

   • Create a dimension.
   • Create a fact.
   • Extend an entity.
5. In step 1 of the wizard, select an augmentation type. Each augmentation type requires you to complete certain tasks.
6. For the type of augmentation, if you select Create Dimension to add a new dimension in the target instance, follow these instructions:
   1. Select Supplemental Data (Regular) as the source dataset type.
   2. Select a product pillar; for example, Enterprise Resource Planning.
   3. Select a source table to which you want to add the new dimension.
   4. Click Next.
   5. In step 3 of the wizard, in the Available Attributes pane, select the check box for the attributes that you want in your new dimension, and then click Add Selected.

      You see the attributes that you had selected in the Target attributes pane and the recommended (defined) primary key. You can either accept this key or override it with your own primary key definition.

   6. Select Advanced to reorganize the order of columns that are marked as primary keys and specify a date or timestamp data type column as one of your incremental keys to use when determining the initial extract date.
   7. Click Next.
   8. In step 6 of the wizard, provide the following details and click Finish to save and schedule your data augmentation pipeline job:
      1. Name your augmentation pipeline job; for example, Customer Class Code.
      2. Enter a suffix for the target table name using underscore in place of spaces between words and don’t use special characters; for example, Customer_Class_D. The augmentation process automatically creates the target table name.
      3. Provide a description.
      4. Select the functional area and one or multiple subject areas in which you want to include this augmentation pipeline job.
      5. Specify the options to save the data augmentation pipeline job without executing it, or schedule the execution date and time, or execute it immediately.
7. For the type of augmentation, if you select Create Fact to add a new fact table in the target instance, then follow these instructions:
   1. Select Supplemental Data (Regular) as the source dataset type.
   2. Select a product pillar; for example, Enterprise Resource Planning.
   3. Select a source table to which you want to add the new fact table.
   4. Click Next.
   5. In step 3 of the wizard, in the Available Attributes pane, select the check box for the attributes that you want in your new fact table, and then click Add Selected.

      You see the attributes that you had selected in the Target attributes pane and the recommended (defined) primary key. You can either accept this key or override it with your own primary key definition.

      For any numeric columns, you must specify “Measure” as the entity type if you want any aggregation done on the attribute. If you want to join any columns such as "ID" with any other dimension, then you must specify “Dimension” as the entity type.

   6. Select Advanced to reorganize the order of columns that are marked as primary keys and specify a date or timestamp data type column as one of your incremental keys to use when determining the initial extract date.
   7. Click Next.
   8. In step 4 of the wizard, specify the dimension in the data warehouse that you want to map to the column that you identified as “Dimension” entity type and then click Next.
   9. In step 5 of the wizard, for the columns that you specified as “Measure” entity type, select an aggregation, and then click Next.
   10. In step 6 of the wizard, provide the following details and click Finish to save and schedule your data augmentation pipeline job:
       1. Name your augmentation pipeline job; for example, AP Distribution.
       2. Enter a suffix for the target table name using underscore in place of spaces between words and don’t use special characters; for example, AP_DISTRIBUTION_F. The augmentation process automatically creates the target table name.
       3. Provide a description.
       4. Select the functional area and one or multiple subject areas in which you want to include this augmentation pipeline job.
       5. Specify the options to save the data augmentation pipeline job without executing it, or schedule the execution date and time, or execute it immediately.
8. For the type of augmentation, if you select Extend Entity to extend a group of existing facts, then follow these instructions:
   1. Select Descriptive Flex Field (DFF) as the source dataset type.
   2. Select a product pillar; for example, Enterprise Resource Planning.
   3. Select a source table from the list of view objects that support descriptive flex fields.
   4. In step 2 of the wizard, select the entity or group of fact tables to extend. For example, if you select ARTransaction as the entity to extend, then this process joins the ready-to-use InvoiceID descriptive flex field using the “s_k_5000” primary join key with all the fact tables in the ARTransaction entity.
   5. Click Next.
   6. In step 6 of the wizard, provide the following details and click Finish to save and schedule your data augmentation pipeline job:
      1. Name your augmentation pipeline job; for example, AP Invoice Header.
      2. Enter a suffix for the target table name using underscore in place of spaces between words and don’t use special characters; for example, AP_Invoice_Header_DFF. The augmentation process automatically creates the target table name.
      3. Provide a description.
      4. Select the functional area and one or multiple subject areas in which you want to include this augmentation pipeline job.
      5. Specify the options to save the data augmentation pipeline job without executing it, or schedule the execution date and time, or execute it immediately.
9. For the type of augmentation, if you select Create Input Dataset to extend a group of existing facts, then follow these instructions:
   1. Select Custom Attribute as the source dataset type.
   2. Select a product pillar; for example, Enterprise Resource Planning.
   3. Select a source table from the list of view objects that support descriptive flex fields.
   4. In step 2 of the wizard, select the attributes to add to the target table from the source table.
   5. Click Next.
   6. In step 6 of the wizard, provide the augmentation name and description, and click Finish to save your input dataset.
10. For the type of augmentation, if you select Extend Transaction Entity to add an action type to the augmentation, then follow these instructions:
    1. Select Transformation as the source dataset type.
    2. Select a product pillar; for example, Enterprise Resource Planning.
    3. Select a source table from the list of view objects that support descriptive flex fields.
    4. In step 2 of the wizard, create the instructions for the transformation using the filters and joins and projections areas.
    5. Click Next.
    6. In step 3 of the wizard, select the attributes from the source table to add to the target table, and click Finish to save and schedule your data augmentation pipeline job:

You see the data augmentation pipeline jobs on the Data Augmentation page with one of these statuses:

• Activation in Progress - You can’t edit, delete, or schedule a data augmentation pipeline job while activation is in progress.
• Activation Completed - You can edit the data augmentation to add or delete VO attributes and save the changes. You can’t modify the schedule in this status.
• Activation Scheduled - You can edit the data augmentation to add VO attributes, save the changes while retaining the existing schedule, reschedule the execution date and time, or execute the plan immediately.

Note:

Later if you delete a data augmentation, then you must wait for the daily incremental run to complete to see the change in the reports, cards, and decks.