Data Structure for Analytics

The BI repository defines which columns (or slices of data) are available for you to include in analyses, and where data for each column comes from.

The repository is organized into subject areas which contain folders with columns. You can also use the BI repository as a data source for reports.

Columns

The following table describes the three types of columns.

Column Type	Description	Example
Fact	Provides a measure of values that are numbers.	Credit Amount in Contract Currency
Attribute	Represents information about a business object with values that are dates, IDs, or text. Attribute columns can be flexfield segments imported into the BI repository.	Contract Association Level
Hierarchy	Holds data values that are organized in a hierarchical manner.	Date - Week To Year hierarchy contains: Total Week To Year Week Day Detail

Subject Areas

To create an analysis:

1. Select a subject area which contains columns related to a specific business object or business area. For example, Project Billing - Invoices Real Time.

2. Open folders within the subject area to find the columns to include in your analysis. For example, you can open the Project folder and select the Project Ledger Currency column within it.

Folders

Each subject area has one fact folder and a number of dimension folders. Folders can have subfolders. For example, the Project folder in Project Billing - Invoices Real Time subject area has multiple subfolders like Project Classification, Project Data Attributes, Project Record Information, and so on.

• Fact folders:

  • Contain fact columns.

  • Are usually at the bottom of the 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 Currency attribute from a dimension folder, you see a currency column in the results. If you also add the Total fact, then your analysis includes only those records that have both currency and total amount columns populated. Adding certain columns can reduce the query set for your analysis depending on how you joined them in the query.

    Note: If you add attributes that don't have a value, you see them as null in the results if you joined them appropriately in the query.

  • Can be common folders or contain common dimensions that appear in more than one subject area. If your analysis has columns from multiple subject areas then you must include:

    • Columns from dimension folders that are common to all those subject areas. At least one such column is mandatory.

    • One column from the fact folder in each of those subject areas.