3Subject Areas for Analytics

This chapter contains the following:

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

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.

Financials Data Structure for Analytics

The BI repository contains metadata that defines which columns (or slices of data) are available to be included in analyses. The repository also shows where the 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 Icon for Column Type

Fact

Provides a measure of values that are numbers.

Credit Amount on Journal Line

Ruler

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.

Approval Status Code on a journal entry.

Piece of paper

Hierarchy

Holds data values that are organized in a hierarchical manner.

Accounting Period

  • Year

  • Quarter

  • Month

Column: Hierarchy of squares

Sublevel: Single square

Subject Areas

When you create an analysis, you:

  • First select a subject area which contains columns related to a specific business object or business area. For example, General Ledger - Balances Real Time.

  • Then open folders within the subject area to find the columns to include in your analysis. For example, you can open the Approval Status folder and select the columns within it.

Folders

Each subject area has one fact folder and a number of dimension folders. Folders can have subfolders. For example, the Journal Lines folder in General Ledger - Journals Real Time subject area has multiple subfolders like Account, Line Details, and Lines.

The definitions of Fact and Dimension folders are:

  • Fact: A measure or metric. A fact consists of numbers. A report more often than not contains at least one fact and not more than a few facts.

  • Dimension: Provides the context for the fact. A dimension is descriptive.

Note: Facts and dimensions make up the report columns.
  • Fact folders:

    • Contain fact columns.

    • Are usually last in 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 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 becomes.

    • 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:

      • Must include columns only from dimension folders that are common to all those subject areas. At least one such column is required.

      • Must include one column from the fact folder in each of those subject areas.

Note: For more information, see Financials Cloud OTBI Release 11: Subject Area Document on Customer Connect.at:https://appsconnect.oracle.com/files/8164acf4e9/FINS_OTBI_Subject_Area_Documentation_R_11_FINAL.pdf