About Augmenting Your Data

Enhance the data used in your analytics with additional data, various calculations, and combinations to enable comprehensive analytics and multi-faceted visualizations. By augmenting the data, you can reduce or even eliminate the manual intervention in developing meaningful insight of the business data.

Data augmentation enables you to augment the data you bring from Oracle Fusion Cloud Applications and other sources that you can connect to using the Oracle Fusion Data Intelligence connectors. See the Connectors section in Preview Features. You can add data to your reports from various data stores (Business Intelligence view objects) of the Oracle Fusion Cloud Applications data sources.

Select the columns from data stores, create an augmentation dataset, and use that dataset to create data pipelines for functional areas. Using an augmentation dataset enables you to seamlessly extract and load data from additional Oracle Fusion Cloud Applications data stores and make the data available to tables in the 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 the Data Stores section in Reference for Oracle Fusion SCM Analytics, Reference for Oracle Fusion HCM Analytics, and Reference for Oracle Fusion ERP Analytics. Although there is no technical limit, you can create a maximum of hundred data augmentations for a single tenant to ensure optimal performance of all data pipelines. Contact Oracle Support if you have further questions.

SME Options for Data Augmentation - The SME Options for Data Augmentation feature is available from the Generally Available Features tab on the Enable Features page.

Note:

This function is no longer recommended for use and may be removed in a future release.

For optimal performance and stability, Oracle recommends that you don't enable this. Instead, manually configure your semantic model with User Extensions, rather than having the data augmentation automatically generate the system semantic model extensions. The reason for this recommendation is any changes to the underlying data augmentation, that remain referenced in the semantic model, cause your semantic model extensions to become out-of-sync and fail. The recommended practice is to separate the Data Augmentation and Semantic Model Extension process, which provides greater flexibly for complex modelling and prevents errors. Going forward, when creating data augmentations, always use the Dataset type and define your own user semantic model extensions. Don't create any new data augmentations (of type Dimension, Fact, or Extend Entity) that create the system semantic model extensions. If feasible, convert all your existing data augmentations to datasets and user semantic model extensions.

Here are a few use cases when augmenting your Oracle Fusion Cloud Applications data with data from several data stores enables in-depth and focused insights:
  • Product sales – Add similar product information from different data sources to create a report that compares similar products in a specific region.
  • Average of expense invoices – Add various expense invoices to create an average of periodic expenses.