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home > training > bi terminology

Terminology

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Ad-Hoc
In Latin ad-hoc means, “for this purpose only” and thus ad-hoc reporting is a way to create dynamic, often temporary queries to handle specific questions that no existing reports answers.

Aggregation
A powerful performance tool, aggregations pre-summarize detail data into smaller tables along a specific line of analysis or dimension (such as time). This allows report queries to process against smaller data sets.

Atomic Level Data
The lowest granularity or level of data available also referred to as “detail” data. (Example: Individual sales transaction line items.)

Attributes
A Logical Data model of your business first breaks down by Dimension (Example: Time) which then break down to Attributes (Examples: Year, Month, Day). Attributes will then typically relates to one or more columns in a database.

Base Tables
Fact Tables with data stored at the lowest level of detail.

Conformed Dimension
Conformed dimensions have consistent definitions regardless of where they are used. This allows a single query to be run across multiple tables, Data Marts and Data Warehouses.

Data Dictionary
A repository that keeps detailed definitions regarding your Data Warehouse Logical and Physical data models.
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Data Mart
Similar in structure and purpose to a full Data Warehouse, data marts are smaller sets of data focused on one particular business subject area. Properly designed with conformed dimensions data marts can efficiently work together to work with or even as as your enterprise Data Warehouse.

Data Warehouse
A data warehouse is a central repository for all or significant parts of the data collected by the various business systems of an enterprise. The term was coined by W. H. Inmon. IBM sometimes uses the term “information warehouse.”

Decision Support System (DSS)
Decision Support Systems and tools leverage stored historical data (typically in a Data Warehouse) to help business users make informed decisions. In recent years this term has been slowly replaced with the more encompassing idea of “Business Intelligence.”

Denormalize
Process of taking normalized data and coverting into an unnormalized form. (Example: Collapsing three tables containing reference information on Years, Months and Days into a single table.) Denormalization brings a Data Warehouse Data Model closer to a Star Schema and is often recommended for performance improvements.

Detail Data
The lowest grainularity or level of data available also refered to as “atomic” data. (Example: Individual sales transaction line items.)

Dimension
Logical grouping of related business Attributes that typically form at least one Hierachy or Drill-Path for analysis. (Examples: Time Dimension = Year > Month > Day, Geography Dimension = Country > State > City).

Drill-Down
Process of finding more detailed data by displaying data at a lower level than was previously shown.

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