How Data Is Structured for Analytics

The business intelligence (BI) repository contains the metadata that defines which columns you can include in analyses, and the source of that data. The repository is organized into subject areas, which contain folders with the columns.

Note: You can also use the BI repository as a data source for reports.

Columns

This table describes the three types of columns available when you create or edit analyses.

Column Type	Description	Example
Fact	Provides a measure of something, meaning that the values are numbers.	Total
Attribute	Represents a piece of information about a business object, with values that are dates, IDs, or text. Note: Attribute columns can be flexfield segments imported into the BI repository.	Start Date
Hierarchy	Holds data values that are organized in a hierarchical manner.	Time, with sublevels: Year Quarter Month

Column Type

Description

Example

Fact

Provides a measure of something, meaning that the values are numbers.

Total

Attribute

Represents a piece of information about a business object, with values that are dates, IDs, or text.

Note: Attribute columns can be flexfield segments imported into the BI repository.

Start Date

Hierarchy

Holds data values that are organized in a hierarchical manner.

Time, with sublevels:

Year
Quarter
Month

Subject Areas

When you create an analysis, you first select a subject area, which contains columns related to a specific business object or area. Then, open folders within the subject area to find the columns to include.

For product families that use Application Composer, you can create custom subject areas to accommodate custom objects or to add new facts for analysis.

Folders

Each subject area has one fact folder and a number of dimension folders. Folders can have subfolders.

Fact folders:
- Contain fact columns.
- Are usually the last in a list of folders and are usually named after the subject area.
Dimension folders:
- Contain attribute and hierarchical columns.
- Are joined to the fact folder within a subject area.
  
  For example, if your analysis has the Currency attribute from a dimension folder, you see currencies in the results. If you also add the Total fact, then your analysis includes only records with both a currency and a total amount. The more columns you add, the smaller the query set for your analysis.
- Can be common folders, or common dimensions, that appear in more than one subject area.
  
  If your analysis has columns from multiple subject areas, then you:
  - Should include columns only from dimension folders that are common to all of those subject areas. At least one such column is required.
  - Must include one column from the fact folder in each of those subject areas.