Analytics Terminology and Structure

Owned by Jennifer Koerber (Unlicensed)
Last updated: Aug 05, 2024 by Allison Powers
2 min read

Analytics Terminology & Structure

Data in Analytics is structured into Subject Areas, which are made up of Dimensions (tables), which are made up of Columns (also known as fields).

Subject Area: Content-specific data. Examples of frequently used Subjects Areas are Fulfillment, Physical Items, Requests, Funds Expenditure, E-Inventory.

Dimension: Dimensions are tables of data that describe sub-topics within the subject; they're used to organize columns into related areas

Shared Dimension: Dimensions that are included in multiple Subject Areas. Shared dimensions include:

    • Bibliographic Details
    • PO Line
    • Library Unit
    • LC Classifications
    • Dewey Classifications
    • User Details
    • Institution

Fact Table: A Dimension that functions as the central table of a Subject Area. The Fact Table contains measurement columns and joins to all other Dimensions in the Subject Area. 

Columns are made up of the fields that you can add to your analysis, broken into three different data types:

Measurement column: Contains data values that can be counted or aggregated, such as Transaction Amount, Number of Loans, Number of Items, Fund Transaction Amount.

Description column: Contains text values such as Fine Fee Status, Fine Fee ID, Library Name, Order Number, Title.

Hierarchical column: Contains data values that are organized using both named levels and parent-child relationships. Hierarchical columns are displayed using a tree-like structure. Individual members are shown in an outline, with lower-level members rolling into higher-level members. Dimensions with hierarchical columns include LC Classification, Fund Ledger, and Item Creation Date.