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.
If you enable the SME Options for Data Augmentation under the Generally Available Features tab on the Enable Features page, then 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. When you run these data augmentation pipeline jobs, they publish these datasets to the semantic model. However, this isn’t the recommended practice. The recommended method is not to enable the SME Options for Data Augmentation feature and use the default Dataset augmentation type to bring varied data into the warehouse. When you run the Dataset data augmentation pipeline job, it doesn’t publish anything to the semantic model. You can then use the semantic model extensions to create your own semantic model. This method supports complex semantic modelling to meet your business requirements. Use the Data augmentation capability to bring data into the warehouse and then use the Semantic Model Extensibility capability to create the joins and expose that data to the subject areas that you want. This enables flexibility and better performance of both the capabilities. Additionally, this method allows better lifecycle management. For example, if you need to make any adjustments to the semantic model, then you can make the changes directly in the semantic model. You don’t need to adjust the data augmentation that brought the data into the warehouse.
- 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.