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Fusion Analytics Capabilities Explorer

Extend analyses with additional data sources

Every organization has unique data needs, which require analyses from multiple sources of data beyond what resides in Oracle Cloud Applications. Oracle Fusion Analytics provides a variety of ways to capture this data, from self-service methods to a more governed, curated approach.

Include additional data sources in a variety of ways

Oracle Fusion Analytics provides the following ways to extend with additional data sources.

1. Descriptive flexfield extensions in Oracle Cloud Applications are automatically extend to the Oracle Fusion Analytics data model.

2. Load external data into the same data model as your Oracle Cloud Applications data using Fusion Analytics’ data augmentation connectors* or any data integration tool of your choice. Use the Fusion Analytics’ extension framework to customize the semantic model.

3. Connect to external data sources via native connectors or to data files leveraging the self-service capabilities of the underlying Oracle Analytics Cloud platform.

*See product documentation for supported data sources.

Load external data into the data model

The prebuilt data model and semantic model can be extended and preserved across Oracle Fusion Analytics upgrades.

Figure 1 Fusion Analytics prebuilt and extensibility architecture

Load additional data sources using Fusion Analytics data augmentation connectors

Fusion Analytics provides data augmentation connectors to extract and load supported data sources (Salesforce, Oracle E-Business Suite, PeopleSoft, Shopify) into the same data repository as your Oracle Cloud Applications data.

See product documentation for supported data sources.

Figure 2 Managed Data Pipeline capabilities

Extending the data model

While the prebuilt data model (star schema) for the Oracle Cloud Applications data is read-only to ensure all prebuilt KPIs and analyses are never broken, the data model is easily extended by adding external data sources into custom-built database schemas in the same embedded Oracle Autonomous Data Warehouse service. Fusion Analytics supports any data movement tool for loading data, such as Oracle Data Integration, any third-party tools, or even plain SQL.

Extending the semantic model

The semantic model can be extended using a simple, wizard-driven interface, supporting a multi-user development and publishing process. The following customizations are available:

  • Add a dimension to existing subject area
  • Add a fact table to existing subject area
  • Add a hierarchy to a dimension table in an existing subject area
  • Add session variables that you can include in the analysis
  • Extend a dimension with additional attributes from another data source
  • Add derived columns to an existing subject area
  • Create a subject area
  • Modify a subject area
Figure 3 Semantic model extensibility capabilities

All semantic model changes follow a test-to-production, version-controlled publishing process. Data engineers/IT can perform extensibility and testing tasks in the provided test environment. Once changes are ready, they can be published to the production environment. All customizations are preserved across Oracle Fusion Analytics upgrades and patches.

Connect to data sources and files

There are a variety of ways to include additional data sources to analyses via self-service.

Native connectors

Oracle Fusion Analytics supports more than 50 native connectors to various sources, such as Oracle Autonomous Database, Oracle Fusion Cloud EPM, Google Big Query, Salesforce, and Snowflake. You can also connect to any Java Database Connectivity (JDBC)-based data source. Get real-time data from Oracle Cloud Applications using the Oracle Cloud Applications connector.

Figure 4 Example of native self-service connectors

Personal and third-party datasets and files

Upload personal datasets, such as spreadsheets and comma separated value (CSV) files. Analyse these datasets alone or combine with the prebuilt subject areas of your Oracle Cloud Application data.

Figure 5 Example of an uploaded spreadsheet during data preparation

Self-service data preparation and transformation

Perform all necessary last-mile data preparation and enrichment tasks for analytics with the code-free capabilities of self-service dataflows. Connect multiple data sources, whether in the cloud, on-premises, or personal data extracts, into cohesive datasets on the cloud. Results can be saved in the embedded Oracle Autonomous Data Warehouse, Oracle Analytics storage, any connected RDBMS, or Oracle Essbase.

Figure 6 Example of self-service data preparation